Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
Evolutionary multi-task optimization(EMTO)presents an efficient way to solve multiple tasks simultaneously.However,difficulties they face in curbing the performance degradation caused by unmatched knowledge transfer a...Evolutionary multi-task optimization(EMTO)presents an efficient way to solve multiple tasks simultaneously.However,difficulties they face in curbing the performance degradation caused by unmatched knowledge transfer and inefficient evolutionary strategies become more severe as the number of iterations increases.Motivated by this,a novel self-adjusting dualmode evolutionary framework,which integrates variable classification evolution and knowledge dynamic transfer strategies,is designed to compensate for this deficiency.First,a dual-mode evolutionary framework is designed to meet the needs of evolution in different states.Then,a self-adjusting strategy based on spatial-temporal information is adopted to guide the selection of evolutionary modes.Second,a classification mechanism for decision variables is proposed to achieve the grouping of variables with different attributes.Then,the evolutionary algorithm with a multi-operator mechanism is employed to conduct classified evolution of decision variables.Third,an evolutionary strategy based on multi-source knowledge sharing is presented to realize the cross-domain transfer of knowledge.Then,a dynamic weighting strategy is developed for efficient utilization of knowledge.Finally,by conducting experiments and comparing the designed method with several existing algorithms,the empirical results confirm that it significantly outperforms its peers in tackling benchmark instances.展开更多
Characteristics of knowledge exchanging behavior among individual agents in a knowledge dynamic interaction system are studied by using the game theory. An analytic model of evolutionary game of continuous dynamic kno...Characteristics of knowledge exchanging behavior among individual agents in a knowledge dynamic interaction system are studied by using the game theory. An analytic model of evolutionary game of continuous dynamic knowledge interaction behavior is founded based on the structure of the evolutionary game chain. Possible evolution trends of the model are discussed. Finally, evolutionary stable strategies （ESSs） of knowledge transactions among individual agents in the knowledge network are identified by simulation data. Stable charicteristics of ESS in a continuous knowledge exchanging team help employee to communicate and grasp the dynamic regulation of shared knowledge.展开更多
How ecological and evolutionary factors affect small mammal diversity in arid regions remains largely unknown.Here,we combined the largest phylogeny and occurrence dataset of Gerbillinae desert rodents to explore the ...How ecological and evolutionary factors affect small mammal diversity in arid regions remains largely unknown.Here,we combined the largest phylogeny and occurrence dataset of Gerbillinae desert rodents to explore the underlying factors shaping present-day distribution patterns.In particular,we analyzed the relative contributions of ecological and evolutionary factors on their species diversity using a variety of models.Additionally,we inferred the ancestral range and possible dispersal scenarios and estimated the diversification rate of Gerbillinae.We found that Gerbillinae likely originated in the Horn of Africa in the Middle Miocene and then dispersed and diversified across arid regions in northern and southern Africa and western and central Asia,forming their current distribution pattern.Multiple ecological and evolutionary factors jointly determine the spatial pattern of Gerbillinae diversity,but evolutionary factors(evolutionary time and speciation rate)and habitat filtering were the most important in explaining the spatial variation in species richness.Our study enhances the understanding of the diversity patterns of small mammals in arid regions and highlights the importance of including evolutionary factors when interpreting the mechanisms underlying large-scale species diversity patterns.展开更多
The proof-of-stake(PoS)mechanism is a consensus protocol within blockchain technology that determines the validation of transactions and the minting of new blocks based on the participant’s stake in the cryptocurrenc...The proof-of-stake(PoS)mechanism is a consensus protocol within blockchain technology that determines the validation of transactions and the minting of new blocks based on the participant’s stake in the cryptocurrency network.In contrast to proof-of-work(PoW),which relies on computational power to validate transactions,PoS employs a deterministic and resourceefficient approach to elect validators.Whereas,an inherent risk of PoS is the potential for centralization among a small cohort of network participants possessing substantial stakes,jeopardizing system decentralization and posing security threats.To mitigate centralization issues within PoS,this study introduces an incentive-aligned mechanism named decentralized proof-of-stake(DePoS),wherein the second-largest stakeholder is chosen as the final validator with a higher probability.Integrated with the verifiable random function(VRF),DePoS rewards the largest stakeholder with uncertainty,thus disincentivizing stakeholders from accumulating the largest stake.Additionally,a dynamic evolutionary game model is innovatively developed to simulate the evolution of staking pools,thus facilitating the investigation of staking pool selection dynamics and equilibrium stability across PoS and DePoS systems.The findings demonstrate that DePoS generally fosters wealth decentralization by discouraging the accumulation of significant cryptocurrency holdings.Through theoretical analysis of stakeholder predilection in staking pool selection and the simulation of the evolutionary tendency in pool scale,this research demonstrates the comparative advantage in decentralization offered by DePoS over the conventional PoS.展开更多
Life-history traits represent evolutionary adaptations that mediate responses to external environments.Analyzing variation in these traits provides valuable insights into macroecological processes and supports the dev...Life-history traits represent evolutionary adaptations that mediate responses to external environments.Analyzing variation in these traits provides valuable insights into macroecological processes and supports the development of effective conservation and restoration strategies.However,large-scale biogeographic patterns in life-history trait diversity among terrestrial vertebrates remain insufficiently characterized,and the processes shaping these patterns are not well understood.This study integrated life-history and spatial distribution data for 2334 terrestrial vertebrate species in China,including 398 amphibians,211 reptiles,541 mammals,and 1184 birds,to evaluate spatial patterns of trait diversity and identify underlying drivers.Assemblages in South and Southwest China exhibited high species richness,substantial assemblage-level evolutionary distinctiveness,expanded trait volumes,and elevated trait densities compared to null expectations,indicating roles as both evolutionary museums and cradles.In contrast,assemblages on the Tibetan Plateau showed expanded trait volumes but low trait densities,reflecting niche expansion among limited taxa.These findings emphasize the importance of niche packing before assemblages reach environmental carrying limits.Assemblages with high evolutionary distinctiveness tended to display high trait volumes and low trait densities,suggesting a consistent relationship between phylogenetic structure and functional diversification.Among the four groups,amphibians showed the highest sensitivity to environmental variation,highlighting the need for focused conservation efforts.Overall,this study revealed pronounced spatial heterogeneity in trait diversity across China,shaped by species richness,evolutionary distinctiveness,and environmental variation,providing valuable insights for refining conservation priorities for terrestrial vertebrate taxa.展开更多
Expensive multiobjective optimization problems(EMOPs)are complex optimization problems exacted from realworld applications,where each objective function evaluation(FE)involves expensive computations or physical experi...Expensive multiobjective optimization problems(EMOPs)are complex optimization problems exacted from realworld applications,where each objective function evaluation(FE)involves expensive computations or physical experiments.Many surrogate-assisted evolutionary algorithms(SAEAs)have been designed to solve EMOPs.Nevertheless,EMOPs with large-scale decision variables remain challenging for existing SAEAs,leading to difficulties in maintaining convergence and diversity.To address this deficiency,we proposed a variable reconstructionbased SAEA(VREA)to balance convergence enhancement and diversity maintenance.Generally,a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables.Thus,the population can be rapidly pushed towards the Pareto set(PS)by optimizing low-dimensional weight variables with the assistance of surrogate models.Population diversity is improved due to the cluster-based variable reconstruction strategy.An adaptive search step size strategy is proposed to balance exploration and exploitation further.Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task.Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs.展开更多
The yellow boxfish(Ostracion cubicus)exhibits a combination of derived morphological traits specialized for coral reef environments and ancestral characteristics,including a fused dermal plate.Contradictory evolutiona...The yellow boxfish(Ostracion cubicus)exhibits a combination of derived morphological traits specialized for coral reef environments and ancestral characteristics,including a fused dermal plate.Contradictory evolutionary evidence hinders true classification of O.cubicus.To clarify its evolutionary position within Tetraodontiformes,a chromosome-level genome assembly was generated,representing the most contiguous and complete genome to date for this lineage.Notably,O.cubicus possessed the largest genome within the order Tetraodontiformes,primarily due to extensive transposable element expansion.Phylogenetic analysis based on 19 whole genomes and 131 mitochondrial genomes resolved Tetraodontiformes into three major sister groups(Ostraciidae-Molidae,Tetraodontidae,and Balistidae-Monacanthidae).Comparative genomic evidence indicated that O.cubicus diverged early from the common ancestor of modern Tetraodontiformes and retained the highest number of HOX genes among surveyed taxa.Although overall genomic architecture was largely conserved,certain genetic and environmental changes may have contributed to its phenotypic adaptations,including climate cooling during the Miocene-Pliocene Transition,recent DNA and long interspersed nuclear element(LINE)transposon bursts,lineage-specific chromosomal rearrangements,and gene family expansion.Many positively selected genes and rapidly evolving genes were associated with skeletal development,including bmp7,egf7,and bmpr2.Transcriptomic comparisons between carapace and tail skin revealed various candidate genes and pathways related to carapace formation,such as postn,scpp1,and components of the TGF-βsignaling pathway.A derived amino acid substitution in eda,coupled with protein structural modeling,suggested potential molecular convergence in dermal plate formation among teleosts.These findings provide novel insights into the genomic and developmental basis of carapace evolution and coral reef-adaptation in O.cubicus,offering a strong case for evolutionary balance between genomic conservation with regulatory innovation to achieve coral reef specialization.展开更多
The livestock farming is an important pillar of the rural economy in China.To explore the impact of government technical subsidies and pollution penalties on the digital and intelligent transformation of livestock ent...The livestock farming is an important pillar of the rural economy in China.To explore the impact of government technical subsidies and pollution penalties on the digital and intelligent transformation of livestock enterprises,an evolutionary game theoretical model between the government and livestock enterprises is constructed.The interaction mechanism of the game between the government and breeding enterprises is explored,and simulation is conducted.The research results show that the combined strategy of pollution penalties and technical subsidies is the optimal strategy for the government;the system is jointly driven by government subsidies,technical costs of transformation input,public willingness,and enterprise willingness.展开更多
Quantitatively mapping enzyme sequence-catalysis landscapes remains a critical challenge in understanding enzyme function,evolution,and design.In this study,we leveraged emerging microfluidic technology to measure cat...Quantitatively mapping enzyme sequence-catalysis landscapes remains a critical challenge in understanding enzyme function,evolution,and design.In this study,we leveraged emerging microfluidic technology to measure catalytic constants-kcat and KM-for hundreds of diverse orthologs and mutants of adenylate kinase(ADK).We dissected this sequence-catalysis landscape's topology,navigability,and mechanistic underpinnings,revealing catalytically heterogeneous neighborhoods organized by domain architecture.展开更多
The present investigation aims at unveiling the main causes of the recorded disparate phylogeographic patterning among the two highly dispersive coastal crab species Carcinus aestuarii and Pachygrapsus marmoratus in t...The present investigation aims at unveiling the main causes of the recorded disparate phylogeographic patterning among the two highly dispersive coastal crab species Carcinus aestuarii and Pachygrapsus marmoratus in the Mediterranean Sea.For this purpose,available mitochondrial and nuclear data for both species were re-analyzed and investigated for genetic polymorphism and differentiation patterns across three defined geographic scales in their distribution ranges,but also across the same locations in the Mediterranean Sea.The temporal frame of genetic diversification was also determined for both species in order to check whether observed differences in phylogeographic patterns among these coastal decapods could be attributed to different evolutionary histories.The obtained results revealed a more variable and diversified gene pool in the green crab C.aestuarii than the one recorded in the marbled crab P.marmoratus.Lack of significant correlation between pairwise genetic dissimilarities observed among C.aestuarii populations and those detected for P.marmoratus was notably discerned across the same defined Mediterranean locations.This finding indicates that the pattern of pairwise genetic differentiation does not vary in the same way in both examined crab species.Significant outputs of population genetic differentiation,retrieved within both species,were shown to be differently associated with the potential effects of various kinds of isolation processes(related to geography,environment and biogeographic boundary).Evolutionary history reconstruction showed older genetic diversification event in C.aestuarii than the one recorded in P.marmoratus.These recorded temporal frames suggest different modes of genetic diversification in both crab species(glacial vicariance for C.aestuarii and interglacial dispersal for P.marmoratus).They may also provide an explanation for the recorded differences in variation of patterns of population genetic diversity and structure,when integrated with species ecological requirements and life-history traits.展开更多
In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential r...In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential reasoning(ER)methodology.The proposed approach uses q-RLDFS in order to represent the evaluating values of the alternatives corresponding to the attributes.DE optimization is used to obtain the optimal weights of the attributes,and ER methodology is used to compute the aggregated q-rung linear diophantine fuzzy values(q-RLDFVs)of each alternative.Then the score values of alternatives are computed based on the aggregated q-RLDFVs.An alternative with the maximum score value is selected as a better one.The applicability of the proposed approach has been illustrated in COVID-19 emergency decision-making system and sustainable energy planning management.Moreover,we have validated the proposed approach with a numerical example.Finally,a comparative study is provided with the existing models,where the proposed approach is found to be robust to perform better and consistent in uncertain environments.展开更多
As a key component of the plant antioxidant enzymatic system,superoxide dismutase(SOD)can efficiently protect cells from oxidative stress and maintain redox homeostasis.Currently,there are few studies related to SOD g...As a key component of the plant antioxidant enzymatic system,superoxide dismutase(SOD)can efficiently protect cells from oxidative stress and maintain redox homeostasis.Currently,there are few studies related to SOD genes in various taxa of algae,and the specific functions and evolutionary patterns of these family members remain unclear.In this study,comprehensively evolutionary analysis of SOD gene family in the bladed Bangiales was carried out.A total of 9,10,and 12 SOD genes were identified from three species of Pophyra umbilicalis,Pyropia haitanensis,and Pyropia yezoensis,respectively.Based on phylogenetic analysis,SOD gene members within the same subfamily exhibited similar motif patterns as well as conserved domains,which could be attribute to Cu/Zn-SOD and Fe/Mn-SOD.The promoter regions of SOD genes were rich in hormone-responsive,stress-responsive,and growth cis-acting elements,with variations and similarities observed among different species of other red algae and subfamilies.According to subcellular location prediction,it is suggested that Cu/Zn-SOD was predominantly located in chloroplasts,while Fe/Mn-SOD was primarily located in mitochondria.Also,the two subfamilies differed significantly in the two-/three-dimensional protein structures.In terms of gene evolution,the strongest collinearity relationship was shown between Pyropia haitanensis and Pyropia yezoensis,with all the 1꞉1 orthologous gene pair being subjected to a purifying selection(Ka/Ks<1,Ka:non-synonymy rate;Ks:synonymy rate).Moreover,12 SOD genes underwent positive selection during the evolutionary process.Furthermore,gene expression analysis based on transcriptomic data from Pyropia haitanensis showed that the expression patterns of SOD genes varied under different stress conditions.Together,this study revealed the evolutionary pattern of SOD genes in three bladed Bangiales species,which will lay the foundation for subsequent studies on the function of SOD genes.展开更多
Current topology optimization methods for nonlinear continuum structures often suffer from low computational efficiency and limited applicability to complex nonlinear problems.To address these issues,this paper propos...Current topology optimization methods for nonlinear continuum structures often suffer from low computational efficiency and limited applicability to complex nonlinear problems.To address these issues,this paper proposes an improved bi-directional evolutionary structural optimization(BESO)method tailored for maximizing stiffness in nonlinear structures.The optimization program is developed in Python and can be combined with Abaqus software to facilitate finite element analysis(FEA).To accelerate the speed of optimization,a novel adaptive evolutionary ratio(ER)strategy based on the BESO method is introduced,with four distinct adaptive ER functions proposed.The Newton-Raphson method is utilized for iteratively solving nonlinear equilibrium equations,and the sensitivity information for updating design variables is derived using the adjoint method.Additionally,this study extends topology optimization to account for both material nonlinearity and geometric nonlinearity,analyzing the effects of various nonlinearities.A series of comparative studies are conducted using benchmark cases to validate the effectiveness of the proposed method.The results show that the BESO method with adaptive ER significantly improves the optimization efficiency.Compared to the BESO method with a fixed ER,the convergence speed of the four adaptive ER BESO methods is increased by 37.3%,26.7%,12%and 18.7%,respectively.Given that Abaqus is a powerful FEA platform,this method has the potential to be extended to large-scale engineering structures and to address more complex optimization problems.This research proposes an improved BESO method with novel adaptive ER,which significantly accelerates the optimization process and enables its application to topology optimization of nonlinear structures.展开更多
In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding struct...In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding structures typically need to be lightweight,miniaturized,and radiation-protected,which is a multi-parameter and multi-objective optimization problem.The conventional multi-objective(two or three objectives)optimization method for radiation-shielding design exhibits limitations for a number of optimization objectives and variable parameters,as well as a deficiency in achieving a global optimal solution,thereby failing to meet the requirements of shielding optimization for newly developed reactors.In this study,genetic and artificial bee-colony algorithms are combined with a reference-point-selection strategy and applied to the many-objective(having four or more objectives)optimal design of reactor radiation shielding.To validate the reliability of the methods,an optimization simulation is conducted on three-dimensional shielding structures and another complicated shielding-optimization problem.The numerical results demonstrate that the proposed algorithms outperform conventional shielding-design methods in terms of optimization performance,and they exhibit their reliability in practical engineering problems.The many-objective optimization algorithms developed in this study are proven to efficiently and consistently search for Pareto-front shielding schemes.Therefore,the algorithms proposed in this study offer novel insights into improving the shielding-design performance and shielding quality of new reactor types.展开更多
Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk.How-ever,the problem of matching functions between multiple firmwares has not been studied before.This paper proposes a multi...Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk.How-ever,the problem of matching functions between multiple firmwares has not been studied before.This paper proposes a multi-firmware comparison method based on evolutionary algorithms and trusted base points.We first model the multi-firmware comparison as a multi-sequence matching problem.Then,we propose an adaptation function and a population generation method based on trusted base points.Finally,we apply an evolutionary algorithm to find the optimal result.At the same time,we design the similarity of matching results as an evaluation metric to measure the effect of multi-firmware comparison.The experiments show that the proposed method outperforms Bindiff and the string-based method.Precisely,the similarity between the matching results of the proposed method and Bindiff matching results is 61%,and the similarity between the matching results of the proposed method and the string-based method is 62.8%.By sampling and manual verification,the accuracy of the matching results of the proposed method can be about 66.4%.展开更多
Convolutional neural networks(CNNs)exhibit superior performance in image feature extraction,making them extensively used in the area of traffic sign recognition.However,the design of existing traffic sign recognition ...Convolutional neural networks(CNNs)exhibit superior performance in image feature extraction,making them extensively used in the area of traffic sign recognition.However,the design of existing traffic sign recognition algorithms often relies on expert knowledge to enhance the image feature extraction networks,necessitating image preprocessing and model parameter tuning.This increases the complexity of the model design process.This study introduces an evolutionary neural architecture search(ENAS)algorithm for the automatic design of neural network models tailored for traffic sign recognition.By integrating the construction parameters of residual network(ResNet)into evolutionary algorithms(EAs),we automatically generate lightweight networks for traffic sign recognition,utilizing blocks as the fundamental building units.Experimental evaluations on the German traffic sign recognition benchmark(GTSRB)dataset reveal that the algorithm attains a recognition accuracy of 99.32%,with a mere 2.8×10^(6)parameters.Experimental results comparing the proposed method with other traffic sign recognition algorithms demonstrate that the method can more efficiently discover neural network architectures,significantly reducing the number of network parameters while maintaining recognition accuracy.展开更多
In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classificati...In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency.展开更多
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金supported in part by the Plan of Key Scientific Research Projects of Colleges and Universities in Henan Province(25A413005,24A120005)the National Science and Technology Major Project(2021ZD0112302)the National Natural Science Foundation of China(62222301,61890930-5,62021003).
文摘Evolutionary multi-task optimization(EMTO)presents an efficient way to solve multiple tasks simultaneously.However,difficulties they face in curbing the performance degradation caused by unmatched knowledge transfer and inefficient evolutionary strategies become more severe as the number of iterations increases.Motivated by this,a novel self-adjusting dualmode evolutionary framework,which integrates variable classification evolution and knowledge dynamic transfer strategies,is designed to compensate for this deficiency.First,a dual-mode evolutionary framework is designed to meet the needs of evolution in different states.Then,a self-adjusting strategy based on spatial-temporal information is adopted to guide the selection of evolutionary modes.Second,a classification mechanism for decision variables is proposed to achieve the grouping of variables with different attributes.Then,the evolutionary algorithm with a multi-operator mechanism is employed to conduct classified evolution of decision variables.Third,an evolutionary strategy based on multi-source knowledge sharing is presented to realize the cross-domain transfer of knowledge.Then,a dynamic weighting strategy is developed for efficient utilization of knowledge.Finally,by conducting experiments and comparing the designed method with several existing algorithms,the empirical results confirm that it significantly outperforms its peers in tackling benchmark instances.
文摘Characteristics of knowledge exchanging behavior among individual agents in a knowledge dynamic interaction system are studied by using the game theory. An analytic model of evolutionary game of continuous dynamic knowledge interaction behavior is founded based on the structure of the evolutionary game chain. Possible evolution trends of the model are discussed. Finally, evolutionary stable strategies （ESSs） of knowledge transactions among individual agents in the knowledge network are identified by simulation data. Stable charicteristics of ESS in a continuous knowledge exchanging team help employee to communicate and grasp the dynamic regulation of shared knowledge.
基金supported by grants from the Third Xinjiang Scientific Expedition Program(Grant No.2022xjkk0205 to Lin Xia,No.2021xjkk0604 to Jilong Cheng)the National Natural Science Foundation of China(32170416 to Qisen Yang,31900325 to Jilong Cheng)+1 种基金the Joint Fund of National Natural Science Foundation of China(U2003203 to Lin Xia)the Key Laboratory of Zoological Systematics and Evolution of the Chinese Academy of Sciences(Y229YX5105 to Qisen Yang).
文摘How ecological and evolutionary factors affect small mammal diversity in arid regions remains largely unknown.Here,we combined the largest phylogeny and occurrence dataset of Gerbillinae desert rodents to explore the underlying factors shaping present-day distribution patterns.In particular,we analyzed the relative contributions of ecological and evolutionary factors on their species diversity using a variety of models.Additionally,we inferred the ancestral range and possible dispersal scenarios and estimated the diversification rate of Gerbillinae.We found that Gerbillinae likely originated in the Horn of Africa in the Middle Miocene and then dispersed and diversified across arid regions in northern and southern Africa and western and central Asia,forming their current distribution pattern.Multiple ecological and evolutionary factors jointly determine the spatial pattern of Gerbillinae diversity,but evolutionary factors(evolutionary time and speciation rate)and habitat filtering were the most important in explaining the spatial variation in species richness.Our study enhances the understanding of the diversity patterns of small mammals in arid regions and highlights the importance of including evolutionary factors when interpreting the mechanisms underlying large-scale species diversity patterns.
文摘The proof-of-stake(PoS)mechanism is a consensus protocol within blockchain technology that determines the validation of transactions and the minting of new blocks based on the participant’s stake in the cryptocurrency network.In contrast to proof-of-work(PoW),which relies on computational power to validate transactions,PoS employs a deterministic and resourceefficient approach to elect validators.Whereas,an inherent risk of PoS is the potential for centralization among a small cohort of network participants possessing substantial stakes,jeopardizing system decentralization and posing security threats.To mitigate centralization issues within PoS,this study introduces an incentive-aligned mechanism named decentralized proof-of-stake(DePoS),wherein the second-largest stakeholder is chosen as the final validator with a higher probability.Integrated with the verifiable random function(VRF),DePoS rewards the largest stakeholder with uncertainty,thus disincentivizing stakeholders from accumulating the largest stake.Additionally,a dynamic evolutionary game model is innovatively developed to simulate the evolution of staking pools,thus facilitating the investigation of staking pool selection dynamics and equilibrium stability across PoS and DePoS systems.The findings demonstrate that DePoS generally fosters wealth decentralization by discouraging the accumulation of significant cryptocurrency holdings.Through theoretical analysis of stakeholder predilection in staking pool selection and the simulation of the evolutionary tendency in pool scale,this research demonstrates the comparative advantage in decentralization offered by DePoS over the conventional PoS.
基金supported by the National Key Program of Research and Development,Ministry of Science and Technology(2022YFF1301401,2022YFF0802300)National Natural Science Foundation of China(32370553)+3 种基金Second Tibetan Plateau Scientific Expedition and Research Program(STEP,2019QZKK0501)China Biodiversity Observation Networks(Sino BON)Fundamental Research Funds for the Central Universities(SWU-KR24004)Scientific Research Innovation Project of Graduate Student of Southwest University(SWUB23080)。
文摘Life-history traits represent evolutionary adaptations that mediate responses to external environments.Analyzing variation in these traits provides valuable insights into macroecological processes and supports the development of effective conservation and restoration strategies.However,large-scale biogeographic patterns in life-history trait diversity among terrestrial vertebrates remain insufficiently characterized,and the processes shaping these patterns are not well understood.This study integrated life-history and spatial distribution data for 2334 terrestrial vertebrate species in China,including 398 amphibians,211 reptiles,541 mammals,and 1184 birds,to evaluate spatial patterns of trait diversity and identify underlying drivers.Assemblages in South and Southwest China exhibited high species richness,substantial assemblage-level evolutionary distinctiveness,expanded trait volumes,and elevated trait densities compared to null expectations,indicating roles as both evolutionary museums and cradles.In contrast,assemblages on the Tibetan Plateau showed expanded trait volumes but low trait densities,reflecting niche expansion among limited taxa.These findings emphasize the importance of niche packing before assemblages reach environmental carrying limits.Assemblages with high evolutionary distinctiveness tended to display high trait volumes and low trait densities,suggesting a consistent relationship between phylogenetic structure and functional diversification.Among the four groups,amphibians showed the highest sensitivity to environmental variation,highlighting the need for focused conservation efforts.Overall,this study revealed pronounced spatial heterogeneity in trait diversity across China,shaped by species richness,evolutionary distinctiveness,and environmental variation,providing valuable insights for refining conservation priorities for terrestrial vertebrate taxa.
基金supported by the National Natural Science Foundation of China(U20A20306,62276191)the Fundamental Research Funds for the Central Universities(HUST2023JYCXJJ011).
文摘Expensive multiobjective optimization problems(EMOPs)are complex optimization problems exacted from realworld applications,where each objective function evaluation(FE)involves expensive computations or physical experiments.Many surrogate-assisted evolutionary algorithms(SAEAs)have been designed to solve EMOPs.Nevertheless,EMOPs with large-scale decision variables remain challenging for existing SAEAs,leading to difficulties in maintaining convergence and diversity.To address this deficiency,we proposed a variable reconstructionbased SAEA(VREA)to balance convergence enhancement and diversity maintenance.Generally,a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables.Thus,the population can be rapidly pushed towards the Pareto set(PS)by optimizing low-dimensional weight variables with the assistance of surrogate models.Population diversity is improved due to the cluster-based variable reconstruction strategy.An adaptive search step size strategy is proposed to balance exploration and exploitation further.Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task.Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs.
基金supported by the Basic and Applied Basic Research Foundation of Guangdong Province(2020A1515110882)National Science Fund for Distinguished Young Scholars(32225049)。
文摘The yellow boxfish(Ostracion cubicus)exhibits a combination of derived morphological traits specialized for coral reef environments and ancestral characteristics,including a fused dermal plate.Contradictory evolutionary evidence hinders true classification of O.cubicus.To clarify its evolutionary position within Tetraodontiformes,a chromosome-level genome assembly was generated,representing the most contiguous and complete genome to date for this lineage.Notably,O.cubicus possessed the largest genome within the order Tetraodontiformes,primarily due to extensive transposable element expansion.Phylogenetic analysis based on 19 whole genomes and 131 mitochondrial genomes resolved Tetraodontiformes into three major sister groups(Ostraciidae-Molidae,Tetraodontidae,and Balistidae-Monacanthidae).Comparative genomic evidence indicated that O.cubicus diverged early from the common ancestor of modern Tetraodontiformes and retained the highest number of HOX genes among surveyed taxa.Although overall genomic architecture was largely conserved,certain genetic and environmental changes may have contributed to its phenotypic adaptations,including climate cooling during the Miocene-Pliocene Transition,recent DNA and long interspersed nuclear element(LINE)transposon bursts,lineage-specific chromosomal rearrangements,and gene family expansion.Many positively selected genes and rapidly evolving genes were associated with skeletal development,including bmp7,egf7,and bmpr2.Transcriptomic comparisons between carapace and tail skin revealed various candidate genes and pathways related to carapace formation,such as postn,scpp1,and components of the TGF-βsignaling pathway.A derived amino acid substitution in eda,coupled with protein structural modeling,suggested potential molecular convergence in dermal plate formation among teleosts.These findings provide novel insights into the genomic and developmental basis of carapace evolution and coral reef-adaptation in O.cubicus,offering a strong case for evolutionary balance between genomic conservation with regulatory innovation to achieve coral reef specialization.
文摘The livestock farming is an important pillar of the rural economy in China.To explore the impact of government technical subsidies and pollution penalties on the digital and intelligent transformation of livestock enterprises,an evolutionary game theoretical model between the government and livestock enterprises is constructed.The interaction mechanism of the game between the government and breeding enterprises is explored,and simulation is conducted.The research results show that the combined strategy of pollution penalties and technical subsidies is the optimal strategy for the government;the system is jointly driven by government subsidies,technical costs of transformation input,public willingness,and enterprise willingness.
文摘Quantitatively mapping enzyme sequence-catalysis landscapes remains a critical challenge in understanding enzyme function,evolution,and design.In this study,we leveraged emerging microfluidic technology to measure catalytic constants-kcat and KM-for hundreds of diverse orthologs and mutants of adenylate kinase(ADK).We dissected this sequence-catalysis landscape's topology,navigability,and mechanistic underpinnings,revealing catalytically heterogeneous neighborhoods organized by domain architecture.
文摘The present investigation aims at unveiling the main causes of the recorded disparate phylogeographic patterning among the two highly dispersive coastal crab species Carcinus aestuarii and Pachygrapsus marmoratus in the Mediterranean Sea.For this purpose,available mitochondrial and nuclear data for both species were re-analyzed and investigated for genetic polymorphism and differentiation patterns across three defined geographic scales in their distribution ranges,but also across the same locations in the Mediterranean Sea.The temporal frame of genetic diversification was also determined for both species in order to check whether observed differences in phylogeographic patterns among these coastal decapods could be attributed to different evolutionary histories.The obtained results revealed a more variable and diversified gene pool in the green crab C.aestuarii than the one recorded in the marbled crab P.marmoratus.Lack of significant correlation between pairwise genetic dissimilarities observed among C.aestuarii populations and those detected for P.marmoratus was notably discerned across the same defined Mediterranean locations.This finding indicates that the pattern of pairwise genetic differentiation does not vary in the same way in both examined crab species.Significant outputs of population genetic differentiation,retrieved within both species,were shown to be differently associated with the potential effects of various kinds of isolation processes(related to geography,environment and biogeographic boundary).Evolutionary history reconstruction showed older genetic diversification event in C.aestuarii than the one recorded in P.marmoratus.These recorded temporal frames suggest different modes of genetic diversification in both crab species(glacial vicariance for C.aestuarii and interglacial dispersal for P.marmoratus).They may also provide an explanation for the recorded differences in variation of patterns of population genetic diversity and structure,when integrated with species ecological requirements and life-history traits.
文摘In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential reasoning(ER)methodology.The proposed approach uses q-RLDFS in order to represent the evaluating values of the alternatives corresponding to the attributes.DE optimization is used to obtain the optimal weights of the attributes,and ER methodology is used to compute the aggregated q-rung linear diophantine fuzzy values(q-RLDFVs)of each alternative.Then the score values of alternatives are computed based on the aggregated q-RLDFVs.An alternative with the maximum score value is selected as a better one.The applicability of the proposed approach has been illustrated in COVID-19 emergency decision-making system and sustainable energy planning management.Moreover,we have validated the proposed approach with a numerical example.Finally,a comparative study is provided with the existing models,where the proposed approach is found to be robust to perform better and consistent in uncertain environments.
基金Supported by the National Key R&D Program of China(No.2023 YFD 2400102)the Guangxi Key Laboratory of Beibu Gulf Marine Biodiversity Conservation,Beibu Gulf University(No.2024 KA 04)。
文摘As a key component of the plant antioxidant enzymatic system,superoxide dismutase(SOD)can efficiently protect cells from oxidative stress and maintain redox homeostasis.Currently,there are few studies related to SOD genes in various taxa of algae,and the specific functions and evolutionary patterns of these family members remain unclear.In this study,comprehensively evolutionary analysis of SOD gene family in the bladed Bangiales was carried out.A total of 9,10,and 12 SOD genes were identified from three species of Pophyra umbilicalis,Pyropia haitanensis,and Pyropia yezoensis,respectively.Based on phylogenetic analysis,SOD gene members within the same subfamily exhibited similar motif patterns as well as conserved domains,which could be attribute to Cu/Zn-SOD and Fe/Mn-SOD.The promoter regions of SOD genes were rich in hormone-responsive,stress-responsive,and growth cis-acting elements,with variations and similarities observed among different species of other red algae and subfamilies.According to subcellular location prediction,it is suggested that Cu/Zn-SOD was predominantly located in chloroplasts,while Fe/Mn-SOD was primarily located in mitochondria.Also,the two subfamilies differed significantly in the two-/three-dimensional protein structures.In terms of gene evolution,the strongest collinearity relationship was shown between Pyropia haitanensis and Pyropia yezoensis,with all the 1꞉1 orthologous gene pair being subjected to a purifying selection(Ka/Ks<1,Ka:non-synonymy rate;Ks:synonymy rate).Moreover,12 SOD genes underwent positive selection during the evolutionary process.Furthermore,gene expression analysis based on transcriptomic data from Pyropia haitanensis showed that the expression patterns of SOD genes varied under different stress conditions.Together,this study revealed the evolutionary pattern of SOD genes in three bladed Bangiales species,which will lay the foundation for subsequent studies on the function of SOD genes.
基金Supported by National Natural Science Foundation of China(Grant No.52105271).
文摘Current topology optimization methods for nonlinear continuum structures often suffer from low computational efficiency and limited applicability to complex nonlinear problems.To address these issues,this paper proposes an improved bi-directional evolutionary structural optimization(BESO)method tailored for maximizing stiffness in nonlinear structures.The optimization program is developed in Python and can be combined with Abaqus software to facilitate finite element analysis(FEA).To accelerate the speed of optimization,a novel adaptive evolutionary ratio(ER)strategy based on the BESO method is introduced,with four distinct adaptive ER functions proposed.The Newton-Raphson method is utilized for iteratively solving nonlinear equilibrium equations,and the sensitivity information for updating design variables is derived using the adjoint method.Additionally,this study extends topology optimization to account for both material nonlinearity and geometric nonlinearity,analyzing the effects of various nonlinearities.A series of comparative studies are conducted using benchmark cases to validate the effectiveness of the proposed method.The results show that the BESO method with adaptive ER significantly improves the optimization efficiency.Compared to the BESO method with a fixed ER,the convergence speed of the four adaptive ER BESO methods is increased by 37.3%,26.7%,12%and 18.7%,respectively.Given that Abaqus is a powerful FEA platform,this method has the potential to be extended to large-scale engineering structures and to address more complex optimization problems.This research proposes an improved BESO method with novel adaptive ER,which significantly accelerates the optimization process and enables its application to topology optimization of nonlinear structures.
基金supported by the National Natural Science Foundation of China(Nos.12475174 and 12175101)Yue Lu Shan Center Industrial Innovation(No.2024YCII0108)。
文摘In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding structures typically need to be lightweight,miniaturized,and radiation-protected,which is a multi-parameter and multi-objective optimization problem.The conventional multi-objective(two or three objectives)optimization method for radiation-shielding design exhibits limitations for a number of optimization objectives and variable parameters,as well as a deficiency in achieving a global optimal solution,thereby failing to meet the requirements of shielding optimization for newly developed reactors.In this study,genetic and artificial bee-colony algorithms are combined with a reference-point-selection strategy and applied to the many-objective(having four or more objectives)optimal design of reactor radiation shielding.To validate the reliability of the methods,an optimization simulation is conducted on three-dimensional shielding structures and another complicated shielding-optimization problem.The numerical results demonstrate that the proposed algorithms outperform conventional shielding-design methods in terms of optimization performance,and they exhibit their reliability in practical engineering problems.The many-objective optimization algorithms developed in this study are proven to efficiently and consistently search for Pareto-front shielding schemes.Therefore,the algorithms proposed in this study offer novel insights into improving the shielding-design performance and shielding quality of new reactor types.
文摘Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk.How-ever,the problem of matching functions between multiple firmwares has not been studied before.This paper proposes a multi-firmware comparison method based on evolutionary algorithms and trusted base points.We first model the multi-firmware comparison as a multi-sequence matching problem.Then,we propose an adaptation function and a population generation method based on trusted base points.Finally,we apply an evolutionary algorithm to find the optimal result.At the same time,we design the similarity of matching results as an evaluation metric to measure the effect of multi-firmware comparison.The experiments show that the proposed method outperforms Bindiff and the string-based method.Precisely,the similarity between the matching results of the proposed method and Bindiff matching results is 61%,and the similarity between the matching results of the proposed method and the string-based method is 62.8%.By sampling and manual verification,the accuracy of the matching results of the proposed method can be about 66.4%.
基金supported by the National Natural Science Foundation of China(No.62066041).
文摘Convolutional neural networks(CNNs)exhibit superior performance in image feature extraction,making them extensively used in the area of traffic sign recognition.However,the design of existing traffic sign recognition algorithms often relies on expert knowledge to enhance the image feature extraction networks,necessitating image preprocessing and model parameter tuning.This increases the complexity of the model design process.This study introduces an evolutionary neural architecture search(ENAS)algorithm for the automatic design of neural network models tailored for traffic sign recognition.By integrating the construction parameters of residual network(ResNet)into evolutionary algorithms(EAs),we automatically generate lightweight networks for traffic sign recognition,utilizing blocks as the fundamental building units.Experimental evaluations on the German traffic sign recognition benchmark(GTSRB)dataset reveal that the algorithm attains a recognition accuracy of 99.32%,with a mere 2.8×10^(6)parameters.Experimental results comparing the proposed method with other traffic sign recognition algorithms demonstrate that the method can more efficiently discover neural network architectures,significantly reducing the number of network parameters while maintaining recognition accuracy.
基金funded by the Ministry of Higher Education of Malaysia,grant number FRGS/1/2022/ICT02/UPSI/02/1.
文摘In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency.
