An adaptive quantum-inspired evolutionary algorithm based on Hamming distance （HD-QEA） was presented to optimize the network coding resources in multicast networks. In the HD-QEA, the diversity among individuals was...An adaptive quantum-inspired evolutionary algorithm based on Hamming distance （HD-QEA） was presented to optimize the network coding resources in multicast networks. In the HD-QEA, the diversity among individuals was taken into consideration, and a suitable rotation angle step （RAS） was assigned to each individual according to the Hamming distance. Performance comparisons were conducted among the HD-QEA, a basic quantum-inspired evolutionary algorithm （QEA） and an individual＇s fitness based adaptive QEA. A solid demonstration was provided that the proposed HD-QEA is better than the other two algorithms in terms of the convergence speed and the global optimization capability when they are employed to optimize the network coding resources in multicast networks.展开更多
In the previous papers,Quantum-inspired multi-objective evolutionary algorithm(QMEA)was proved to be better than conventional genetic algorithms for multi-objective optimization problem.To improve the quality of the n...In the previous papers,Quantum-inspired multi-objective evolutionary algorithm(QMEA)was proved to be better than conventional genetic algorithms for multi-objective optimization problem.To improve the quality of the non-dominated set as well as the diversity of population in multi-objective problems,in this paper,a Novel Cloud-based quantum-inspired multi-objective evolutionary Algorithm(CQMEA)is proposed.CQMEA is proposed by employing the concept and principles of Cloud theory.The algorithm utilizes the random orientation and stability of the cloud model,uses a self-adaptive mechanism with cloud model of Quantum gates updating strategy to implement global search efficient.By using the self-adaptive mechanism and the better solution which is determined by the membership function uncertainly,Compared with several well-known algorithms such as NSGA-Ⅱ,QMEA.Experimental results show that(CQMEA)is more effective than QMEA and NSGA-Ⅱ.展开更多
In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and red...In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.展开更多
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
The knapsack problem is a well-known combinatorial optimization problem which has been proved to be NP-hard.This paper proposes a new algorithm called quantum-inspired ant algorithm(QAA)to solve the knapsack problem.Q...The knapsack problem is a well-known combinatorial optimization problem which has been proved to be NP-hard.This paper proposes a new algorithm called quantum-inspired ant algorithm(QAA)to solve the knapsack problem.QAA takes the advantage of the principles in quantum computing,such as qubit,quantum gate,and quantum superposition of states,to get more probabilistic-based status with small colonies.By updating the pheromone in the ant algorithm and rotating the quantum gate,the algorithm can finally reach the optimal solution.The detailed steps to use QAA are presented,and by solving series of test cases of classical knapsack problems,the effectiveness and generality of the new algorithm are validated.展开更多
Attribute reduction in the rough set theory is an important feature selection method, but finding a minimum attribute reduction has been proven to be a non-deterministic polynomial （NP）-hard problem. Therefore, it i...Attribute reduction in the rough set theory is an important feature selection method, but finding a minimum attribute reduction has been proven to be a non-deterministic polynomial （NP）-hard problem. Therefore, it is necessary to investigate some fast and effective approximate algorithms. A novel and enhanced quantum-inspired shuffled frog leaping based minimum attribute reduction algorithm （QSFLAR） is proposed. Evolutionary frogs are represented by multi-state quantum bits, and both quantum rotation gate and quantum mutation operators are used to exploit the mechanisms of frog population diversity and convergence to the global optimum. The decomposed attribute subsets are co-evolved by the elitist frogs with a quantum-inspired shuffled frog leaping algorithm. The experimental results validate the better feasibility and effectiveness of QSFLAR, comparing with some representa- tive algorithms. Therefore, QSFLAR can be considered as a more competitive algorithm on the efficiency and accuracy for minimum attribute reduction.展开更多
To enhance the approximation and generalization ability of artificial neural network (ANN) by employing the principles of quantum rotation gate and controlled-not gate, a quantum-inspired neuron with sequence input is...To enhance the approximation and generalization ability of artificial neural network (ANN) by employing the principles of quantum rotation gate and controlled-not gate, a quantum-inspired neuron with sequence input is proposed. In the proposed model, the discrete sequence input is represented by the qubits, which, as the control qubits of the controlled-not gate after being rotated by the quantum rotation gates, control the target qubit for reverse. The model output is described by the probability amplitude of state in the target qubit. Then a quantum-inspired neural network with sequence input (QNNSI) is designed by employing the sequence input-based quantum-inspired neurons to the hidden layer and the classical neurons to the output layer, and a learning algorithm is derived by employing the Levenberg-Marquardt algorithm. Simulation results of benchmark problem show that, under a certain condition, the QNNSI is obviously superior to the ANN.展开更多
To enhance the approximation ability of neural networks, by introducing quantum rotation gates to the traditional BP networks, a novel quantum-inspired neural network model is proposed in this paper. In our model, the...To enhance the approximation ability of neural networks, by introducing quantum rotation gates to the traditional BP networks, a novel quantum-inspired neural network model is proposed in this paper. In our model, the hidden layer consists of quantum neurons. Each quantum neuron carries a group of quantum rotation gates which are used to update the quantum weights. Both input and output layer are composed of the traditional neurons. By employing the back propagation algorithm, the training algorithms are designed. Simulation-based experiments using two application examples of pattern recognition and function approximation, respectively, illustrate the availability of the proposed model.展开更多
A novel algorithm, the Immune Quantum-inspired Genetic Algorithm (IQGA), is proposed by introducing immune concepts and methods into Quantum-inspired Genetic Algorithm (QGA). With the condition of preserving QGA's...A novel algorithm, the Immune Quantum-inspired Genetic Algorithm (IQGA), is proposed by introducing immune concepts and methods into Quantum-inspired Genetic Algorithm (QGA). With the condition of preserving QGA's advantages, IQGA utilizes the characteristics and knowledge in the pending problems for restraining the repeated and ineffective operations during evolution, so as to improve the algorithm efficiency. The experimental results of the knapsack problem show that the performance of IQGA is superior to the Conventional Genetic Algorithm (CGA), the Immune Genetic Algorithm (IGA) and QGA.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
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
The theory of spatial narrative enhances the connotation of architectural design and broadens the interaction between architecture and its users.Drawing upon a literature review of relevant keywords related to“archit...The theory of spatial narrative enhances the connotation of architectural design and broadens the interaction between architecture and its users.Drawing upon a literature review of relevant keywords related to“architectural space narrative”from the China National Knowledge Infrastructure(CNKI)over the past two decades,alongside an analysis and content identification of high-frequency keywords,clustering,timelines,and other knowledge graphs generated by CiteSpace,this study summarizes the evolution path of spatial narrative theory.This approach offers diverse perspectives for examining emerging trends and key issues within spatial narrative research.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
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
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
Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review exp...Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential.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
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
基金supported by the National Natural Science Foundation of China (61473179)the Doctor Foundation of Shandong Province (BS2013DX032)the Youth Scholars Development Program of Shandong University of Technology (2014-09)
文摘An adaptive quantum-inspired evolutionary algorithm based on Hamming distance （HD-QEA） was presented to optimize the network coding resources in multicast networks. In the HD-QEA, the diversity among individuals was taken into consideration, and a suitable rotation angle step （RAS） was assigned to each individual according to the Hamming distance. Performance comparisons were conducted among the HD-QEA, a basic quantum-inspired evolutionary algorithm （QEA） and an individual＇s fitness based adaptive QEA. A solid demonstration was provided that the proposed HD-QEA is better than the other two algorithms in terms of the convergence speed and the global optimization capability when they are employed to optimize the network coding resources in multicast networks.
基金Supported by the National Natural Science Foundation of China under Grant No.60903168the Scientific Research Fund of Hunan Provincial Education Department of China under Grant No.10B062Guangdong University of Petrochemical Technology Youth innovative personnel training project(NO 2010YC09)
文摘In the previous papers,Quantum-inspired multi-objective evolutionary algorithm(QMEA)was proved to be better than conventional genetic algorithms for multi-objective optimization problem.To improve the quality of the non-dominated set as well as the diversity of population in multi-objective problems,in this paper,a Novel Cloud-based quantum-inspired multi-objective evolutionary Algorithm(CQMEA)is proposed.CQMEA is proposed by employing the concept and principles of Cloud theory.The algorithm utilizes the random orientation and stability of the cloud model,uses a self-adaptive mechanism with cloud model of Quantum gates updating strategy to implement global search efficient.By using the self-adaptive mechanism and the better solution which is determined by the membership function uncertainly,Compared with several well-known algorithms such as NSGA-Ⅱ,QMEA.Experimental results show that(CQMEA)is more effective than QMEA and NSGA-Ⅱ.
基金supported by the National Natural Science Foundation of China under Grant No.61972040the Science and Technology Research and Development Project funded by China Railway Material Trade Group Luban Company.
文摘In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.
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 by the National Natural Science Foundation of China(70871081)the Shanghai Leading Academic Discipline Project(S30504).
文摘The knapsack problem is a well-known combinatorial optimization problem which has been proved to be NP-hard.This paper proposes a new algorithm called quantum-inspired ant algorithm(QAA)to solve the knapsack problem.QAA takes the advantage of the principles in quantum computing,such as qubit,quantum gate,and quantum superposition of states,to get more probabilistic-based status with small colonies.By updating the pheromone in the ant algorithm and rotating the quantum gate,the algorithm can finally reach the optimal solution.The detailed steps to use QAA are presented,and by solving series of test cases of classical knapsack problems,the effectiveness and generality of the new algorithm are validated.
基金supported by the National Natural Science Foundation of China(6113900261171132)+4 种基金the Funding of Jiangsu Innovation Program for Graduate Education(CXZZ11 0219)the Natural Science Foundation of Jiangsu Education Department(12KJB520013)the Applying Study Foundation of Nantong(BK2011062)the Open Project Program of State Key Laboratory for Novel Software Technology,Nanjing University(KFKT2012B28)the Natural Science Pre-Research Foundation of Nantong University(12ZY016)
文摘Attribute reduction in the rough set theory is an important feature selection method, but finding a minimum attribute reduction has been proven to be a non-deterministic polynomial （NP）-hard problem. Therefore, it is necessary to investigate some fast and effective approximate algorithms. A novel and enhanced quantum-inspired shuffled frog leaping based minimum attribute reduction algorithm （QSFLAR） is proposed. Evolutionary frogs are represented by multi-state quantum bits, and both quantum rotation gate and quantum mutation operators are used to exploit the mechanisms of frog population diversity and convergence to the global optimum. The decomposed attribute subsets are co-evolved by the elitist frogs with a quantum-inspired shuffled frog leaping algorithm. The experimental results validate the better feasibility and effectiveness of QSFLAR, comparing with some representa- tive algorithms. Therefore, QSFLAR can be considered as a more competitive algorithm on the efficiency and accuracy for minimum attribute reduction.
文摘To enhance the approximation and generalization ability of artificial neural network (ANN) by employing the principles of quantum rotation gate and controlled-not gate, a quantum-inspired neuron with sequence input is proposed. In the proposed model, the discrete sequence input is represented by the qubits, which, as the control qubits of the controlled-not gate after being rotated by the quantum rotation gates, control the target qubit for reverse. The model output is described by the probability amplitude of state in the target qubit. Then a quantum-inspired neural network with sequence input (QNNSI) is designed by employing the sequence input-based quantum-inspired neurons to the hidden layer and the classical neurons to the output layer, and a learning algorithm is derived by employing the Levenberg-Marquardt algorithm. Simulation results of benchmark problem show that, under a certain condition, the QNNSI is obviously superior to the ANN.
文摘To enhance the approximation ability of neural networks, by introducing quantum rotation gates to the traditional BP networks, a novel quantum-inspired neural network model is proposed in this paper. In our model, the hidden layer consists of quantum neurons. Each quantum neuron carries a group of quantum rotation gates which are used to update the quantum weights. Both input and output layer are composed of the traditional neurons. By employing the back propagation algorithm, the training algorithms are designed. Simulation-based experiments using two application examples of pattern recognition and function approximation, respectively, illustrate the availability of the proposed model.
基金Supported by the National Natural Science Foundation of China (No.60133010 and No.60141002).
文摘A novel algorithm, the Immune Quantum-inspired Genetic Algorithm (IQGA), is proposed by introducing immune concepts and methods into Quantum-inspired Genetic Algorithm (QGA). With the condition of preserving QGA's advantages, IQGA utilizes the characteristics and knowledge in the pending problems for restraining the repeated and ineffective operations during evolution, so as to improve the algorithm efficiency. The experimental results of the knapsack problem show that the performance of IQGA is superior to the Conventional Genetic Algorithm (CGA), the Immune Genetic Algorithm (IGA) and QGA.
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.
基金Sponsored by General Project of the Modern Design and Culture Research Center,Sichuan Provincial Key Research Base for Social Sciences(MD24E019)General Project of the Digital Culture and Media Research Base,Sichuan Provincial Key Research Base for Philosophy and Social Sciences(SC23DCMB006).
文摘The theory of spatial narrative enhances the connotation of architectural design and broadens the interaction between architecture and its users.Drawing upon a literature review of relevant keywords related to“architectural space narrative”from the China National Knowledge Infrastructure(CNKI)over the past two decades,alongside an analysis and content identification of high-frequency keywords,clustering,timelines,and other knowledge graphs generated by CiteSpace,this study summarizes the evolution path of spatial narrative theory.This approach offers diverse perspectives for examining emerging trends and key issues within spatial narrative research.
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 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 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Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential.
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(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.
