The Bat algorithm,a metaheuristic optimization technique inspired by the foraging behaviour of bats,has been employed to tackle optimization problems.Known for its ease of implementation,parameter tunability,and stron...The Bat algorithm,a metaheuristic optimization technique inspired by the foraging behaviour of bats,has been employed to tackle optimization problems.Known for its ease of implementation,parameter tunability,and strong global search capabilities,this algorithm finds application across diverse optimization problem domains.However,in the face of increasingly complex optimization challenges,the Bat algorithm encounters certain limitations,such as slow convergence and sensitivity to initial solutions.In order to tackle these challenges,the present study incorporates a range of optimization compo-nents into the Bat algorithm,thereby proposing a variant called PKEBA.A projection screening strategy is implemented to mitigate its sensitivity to initial solutions,thereby enhancing the quality of the initial solution set.A kinetic adaptation strategy reforms exploration patterns,while an elite communication strategy enhances group interaction,to avoid algorithm from local optima.Subsequently,the effectiveness of the proposed PKEBA is rigorously evaluated.Testing encompasses 30 benchmark functions from IEEE CEC2014,featuring ablation experiments and comparative assessments against classical algorithms and their variants.Moreover,real-world engineering problems are employed as further validation.The results conclusively demonstrate that PKEBA ex-hibits superior convergence and precision compared to existing algorithms.展开更多
In the deployment of wireless networks in two-dimensional outdoor campus spaces,aiming at the problem of efficient coverage of the monitoring area by limited number of access points(APs),this paper proposes a deployme...In the deployment of wireless networks in two-dimensional outdoor campus spaces,aiming at the problem of efficient coverage of the monitoring area by limited number of access points(APs),this paper proposes a deployment method of multi-objective optimization with virtual force fusion bat algorithm(VFBA)using the classical four-node regular distribution as an entry point.The introduction of Lévy flight strategy for bat position updating helps to maintain the population diversity,reduce the premature maturity problem caused by population convergence,avoid the over aggregation of individuals in the local optimal region,and enhance the superiority in global search;the virtual force algorithm simulates the attraction and repulsion between individuals,which enables individual bats to precisely locate the optimal solution within the search space.At the same time,the fusion effect of virtual force prompts the bat individuals to move faster to the potential optimal solution.To validate the effectiveness of the fusion algorithm,the benchmark test function is selected for simulation testing.Finally,the simulation result verifies that the VFBA achieves superior coverage and effectively reduces node redundancy compared to the other three regular layout methods.The VFBA also shows better coverage results when compared to other optimization algorithms.展开更多
Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a produc...Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model(FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm(GA), particle swarm optimization(PSO) algorithm and differential mutation BA(DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms.展开更多
Swarm intelligence in a bat algorithm(BA)provides social learning.Genetic operations for reproducing individuals in a genetic algorithm(GA)offer global search ability in solving complex optimization problems.Their int...Swarm intelligence in a bat algorithm(BA)provides social learning.Genetic operations for reproducing individuals in a genetic algorithm(GA)offer global search ability in solving complex optimization problems.Their integration provides an opportunity for improved search performance.However,existing studies adopt only one genetic operation of GA,or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions only.Differing from them,this work proposes an improved self-adaptive bat algorithm with genetic operations(SBAGO)where GA and BA are combined in a highly integrated way.Specifically,SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and high-quality.Guided by these exemplars,SBAGO improves both BA’s efficiency and global search capability.We evaluate this approach by using 29 widely-adopted problems from four test suites.SBAGO is also evaluated by a real-life optimization problem in mobile edge computing systems.Experimental results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness,search accuracy,local optima avoidance,and robustness.展开更多
Path planning algorithm is the key point to UAV path planning scenario.Many traditional path planning methods still suffer from low convergence rate and insufficient robustness.In this paper,three main methods are con...Path planning algorithm is the key point to UAV path planning scenario.Many traditional path planning methods still suffer from low convergence rate and insufficient robustness.In this paper,three main methods are contributed to solving these problems.First,the improved artificial potential field(APF)method is adopted to accelerate the convergence process of the bat’s position update.Second,the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat algorithm.Third chaos strategy is proposed to avoid falling into a local optimum.Compared with standard APF and chaos strategy in UAV path planning scenarios,the improved algorithm CPFIBA(The improved artificial potential field method combined with chaotic bat algorithm,CPFIBA)significantly increases the success rate of finding suitable planning path and decrease the convergence time.Simulation results show that the proposed algorithm also has great robustness for processing with path planning problems.Meanwhile,it overcomes the shortcomings of the traditional meta-heuristic algorithms,as their convergence process is the potential to fall into a local optimum.From the simulation,we can see also obverse that the proposed CPFIBA provides better performance than BA and DEBA in problems of UAV path planning.展开更多
Population-based algorithms have been used in many real-world problems.Bat algorithm(BA)is one of the states of the art of these approaches.Because of the super bat,on the one hand,BA can converge quickly;on the other...Population-based algorithms have been used in many real-world problems.Bat algorithm(BA)is one of the states of the art of these approaches.Because of the super bat,on the one hand,BA can converge quickly;on the other hand,it is easy to fall into local optimum.Therefore,for typical BA algorithms,the ability of exploration and exploitation is not strong enough and it is hard to find a precise result.In this paper,we propose a novel bat algorithm based on cross boundary learning(CBL)and uniform explosion strategy(UES),namely BABLUE in short,to avoid the above contradiction and achieve both fast convergence and high quality.Different from previous opposition-based learning,the proposed CBL can expand the search area of population and then maintain the ability of global exploration in the process of fast convergence.In order to enhance the ability of local exploitation of the proposed algorithm,we propose UES,which can achieve almost the same search precise as that of firework explosion algorithm but consume less computation resource.BABLUE is tested with numerous experiments on unimodal,multimodal,one-dimensional,high-dimensional and discrete problems,and then compared with other typical intelligent optimization algorithms.The results show that the proposed algorithm outperforms other algorithms.展开更多
Bat algorithm(BA)is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization problems.BA leverages the echolocation feature of bats produced by imitating the bats’searching...Bat algorithm(BA)is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization problems.BA leverages the echolocation feature of bats produced by imitating the bats’searching behavior.BA faces premature convergence due to its local search capability.Instead of using the standard uniform walk,the Torus walk is viewed as a promising alternative to improve the local search capability.In this work,we proposed an improved variation of BA by applying torus walk to improve diversity and convergence.The proposed.Modern Computerized Bat Algorithm(MCBA)approach has been examined for fifteen well-known benchmark test problems.The finding of our technique shows promising performance as compared to the standard PSO and standard BA.The proposed MCBA,BPA,Standard PSO,and Standard BA have been examined for well-known benchmark test problems and training of the artificial neural network(ANN).We have performed experiments using eight benchmark datasets applied from the worldwide famous machine-learning(ML)repository of UCI.Simulation results have shown that the training of an ANN with MCBA-NN algorithm tops the list considering exactness,with more superiority compared to the traditional methodologies.The MCBA-NN algorithm may be used effectively for data classification and statistical problems in the future.展开更多
Wireless sensor network (WSN) has been widely used due to its vastrange of applications. The energy problem is one of the important problems influencingthe complete application. Sensor nodes use very small batteries a...Wireless sensor network (WSN) has been widely used due to its vastrange of applications. The energy problem is one of the important problems influencingthe complete application. Sensor nodes use very small batteries as a powersource and replacing them is not an easy task. With this restriction, the sensornodes must conserve their energy and extend the network lifetime as long as possible.Also, these limits motivate much of the research to suggest solutions in alllayers of the protocol stack to save energy. So, energy management efficiencybecomes a key requirement in WSN design. The efficiency of these networks ishighly dependent on routing protocols directly affecting the network lifetime.Clustering is one of the most popular techniques preferred in routing operations.In this work we propose a novel energy-efficient protocol for WSN based on a batalgorithm called ECO-BAT (Energy Consumption Optimization with BAT algorithmfor WSN) to prolong the network lifetime. We use an objective function thatgenerates an optimal number of sensor clusters with cluster heads (CH) to minimizeenergy consumption. The performance of the proposed approach is comparedwith Low-Energy Adaptive Clustering Hierarchy (LEACH) and EnergyEfficient cluster formation in wireless sensor networks based on the Multi-Objective Bat algorithm (EEMOB) protocols. The results obtained are interestingin terms of energy-saving and prolongation of the network lifetime.展开更多
In the new competitive electricity market, the accurate operation management of Micro-Grid (MG) with various types of renewable power sources (RES) can be an effective approach to supply the electrical consumers more ...In the new competitive electricity market, the accurate operation management of Micro-Grid (MG) with various types of renewable power sources (RES) can be an effective approach to supply the electrical consumers more reliably and economically. In this regard, this paper proposes a novel solution methodology based on bat algorithm to solve the op- timal energy management of MG including several RESs with the back-up of Fuel Cell (FC), Wind Turbine (WT), Photovoltaics (PV), Micro Turbine (MT) as well as storage devices to meet the energy mismatch. The problem is formulated as a nonlinear constraint optimization problem to minimize the total cost of the grid and RESs, simultaneously. In addition, the problem considers the interactive effects of MG and utility in a 24 hour time interval which would in- crease the complexity of the problem from the optimization point of view more severely. The proposed optimization technique is consisted of a self adaptive modification method compromised of two modification methods based on bat algorithm to explore the total search space globally. The superiority of the proposed method over the other well-known algorithms is demonstrated through a typical renewable MG as the test system.展开更多
All task scheduling applications need to ensure that resources are optimally used,performance is enhanced,and costs are minimized.The purpose of this paper is to discuss how to Fitness Calculate Values(FCVs)to provide...All task scheduling applications need to ensure that resources are optimally used,performance is enhanced,and costs are minimized.The purpose of this paper is to discuss how to Fitness Calculate Values(FCVs)to provide application software with a reliable solution during the initial stages of load balancing.The cloud computing environment is the subject of this study.It consists of both physical and logical components(most notably cloud infrastructure and cloud storage)(in particular cloud services and cloud platforms).This intricate structure is interconnected to provide services to users and improve the overall system’s performance.This case study is one of the most important segments of cloud computing,i.e.,Load Balancing.This paper aims to introduce a new approach to balance the load among Virtual Machines(VM’s)of the cloud computing environment.The proposed method led to the proposal and implementation of an algorithm inspired by the Bat Algorithm(BA).This proposed Modified Bat Algorithm(MBA)allows balancing the load among virtual machines.The proposed algorithm works in two variants:MBA with Overloaded Optimal Virtual Machine(MBAOOVM)and Modified Bat Algorithm with Balanced Virtual Machine(MBABVM).MBA generates cost-effective solutions and the strengths of MBA are finally validated by comparing it with Bat Algorithm.展开更多
The projection pursuit model is used to study the assessment of air pollution caused by vehicle emissions at intersections. Based on the analysis of the characteristics and regularities of vehicle emissions at interse...The projection pursuit model is used to study the assessment of air pollution caused by vehicle emissions at intersections. Based on the analysis of the characteristics and regularities of vehicle emissions at intersections, a vehicle emission model based on projection pursuit is established, and the bat algorithm is used to solve the optimization function. The research results show that the projection pursuit model can not only measure the air pollution of vehicle emissions at intersections, but also effectively evaluate the level of vehicle exhaust emissions at intersections. Taking the air pollution caused by vehicle emissions at intersections as the research object and considering the influence factors of vehicle emissions on air pollution comprehensively, the evaluation index system of vehicle emissions at intersections on air pollution is constructed. Based on large data analysis, a prediction model of air pollution caused by vehicle emissions at intersections is constructed, and an improved bat algorithm is used to realize the assessment process. The application results show that the prediction model of vehicle emissions at intersections can define the degree of air pollution caused by vehicle emissions, and it has good guiding significance and practical value for solving the problem of air pollution caused by vehicle emissions.展开更多
Unmanned aerial vehicle(UAV)paths in the field directly affect the efficiency and accuracy of payload data collection.Path planning of UAV advancing along river valleys in wild environments is one of the first and mos...Unmanned aerial vehicle(UAV)paths in the field directly affect the efficiency and accuracy of payload data collection.Path planning of UAV advancing along river valleys in wild environments is one of the first and most difficult problems faced by unmanned surveys of debris flow valleys.This study proposes a new hybrid bat optimization algorithm,GRE-Bat(Good point set,Reverse learning,Elite Pool-Bat algorithm),for unmanned exploration path planning of debris flow sources in outdoor environments.In the GRE-Bat algorithm,the good point set strategy is adopted to evenly distribute the population,ensure sufficient coverage of the search space,and improve the stability of the convergence accuracy of the algorithm.Subsequently,a reverse learning strategy is introduced to increase the diversity of the population and improve the local stagnation problem of the algorithm.In addition,an Elite pool strategy is added to balance the replacement and learning behaviors of particles within the population based on elimination and local perturbation factors.To demonstrate the effectiveness of the GRE-Bat algorithm,we conducted multiple simulation experiments using benchmark test functions and digital terrain models.Compared to commonly used path planning algorithms such as the Bat Algorithm(BA)and the Improved Sparrow Search Algorithm(ISSA),the GRE-Bat algorithm can converge to the optimal value in different types of test functions and obtains a near-optimal solution after an average of 60 iterations.The GRE-Bat algorithm can obtain higher quality flight routes in the designated environment of unmanned investigation in the debris flow gully basin,demonstrating its potential for practical application.展开更多
Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomef...Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on WL5.While on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.展开更多
In 2015,the discovery of mammalian fossils,paleolithic artifacts,and burned bones in the Maoershan Cave of the Guilin Basin,northeastern Guangxi,indicated that it is a late Middle Pleistocene Paleolithic site.In 2021,...In 2015,the discovery of mammalian fossils,paleolithic artifacts,and burned bones in the Maoershan Cave of the Guilin Basin,northeastern Guangxi,indicated that it is a late Middle Pleistocene Paleolithic site.In 2021,stratigraphic sectioning and the systematic screening of small mammal sand samples were conducted.This paper presents a comprehensive account of the new material of the fruit bat Rousettus leschenaultii,accompanied by a concise overview of the non-volant micromammals that coexisted with it in the Quaternary sediments of the Maoershan Cave.This finding marks the second occurrence of fruit bat fossils in China,contributing to our understanding of the dental morphology and past geographical distribution of Rousettus.The micromammalian assemblage of the Maoershan Cave is composed of 3 orders,9 families,26 genera,and 30 species,and exhibits notable similarities with those of the late Middle Pleistocene Yumi Cave,Xinglong Cave,Yanhui Cave,Mawokou Cave,Zhongliangshan,and Chuan Cave faunas.The biochronology of the micromammalian assemblage from the Maoershan Cave has been determined to be consistent with the late Middle Pleistocene,providing a new assemblage in the Pleistocene mammalian faunal sequence in southern China.The presence of abundant oriental forested elements indicates that the Guilin Basin used to be a humid and warm subtropical forest paleoenvironment similar to the contemporary environment during the late Middle Pleistocene.展开更多
Temperate forest ecosystems are important habitats for many bat species. However, these habitats are increasingly affected by anthropogenic disturbances, particularly urban development, leading to landscapes with vary...Temperate forest ecosystems are important habitats for many bat species. However, these habitats are increasingly affected by anthropogenic disturbances, particularly urban development, leading to landscapes with varying land cover composition and configuration. Limited research has examined how forest and urban landscape composition and configuration influence bat activity and diversity. Using a multi-year statewide bat acoustic monitoring dataset from North Carolina, USA, we investigated the effects of forest and urban composition and configuration at multiple spatial scales on bat activity and diversity. First, we constructed single-variable landscape index regression models and found that both the composition and configuration of forests and urban developments influenced bat activity and diversity in a species-specific manner. Next, we applied a hierarchical partitioning approach to compare the relative contributions of composition and configuration indices in explaining variance in bat activity. For big brown bats and hoary bats, evergreen forest and urban development composition indices contributed the most to explaining activity variance. In contrast, for eastern red bats, evening bats, and tricolored bats, deciduous forest fragmentation indices describing landscape configuration were the most influential factors. Silver-haired bat activity variance was primarily explained by an evergreen forest fragmentation index. Lastly, urban development configuration indices were the strongest predictors of Mexican free-tailed bat activity and total bat activity. These results suggest that forest and urban landscape configuration should be considered in conservation and management planning for North American temperate forest ecosystems, particularly in regions that have not experienced drastic deforestation in recent decades.展开更多
Background:Bat ear is a congenital condition that can have psychological effects on patients and is readily correctable,often yielding highly satisfactory outcomes for both patients and surgeons.It is crucial to selec...Background:Bat ear is a congenital condition that can have psychological effects on patients and is readily correctable,often yielding highly satisfactory outcomes for both patients and surgeons.It is crucial to select appropriate techniques in the surgical plan to ensure optimal outcomes.This study aimed to compare the outcomes of otoplasty using the cartilage scoring technique versus the cartilage excision technique for correcting the antihelix.Methods:Twenty patients were divided into two groups:group A underwent the cartilage scoring technique,and group B received the cartilage excision technique.Symmetry,satisfaction,and complications were evaluated and compared between the groups.A visual analog score(VAS)was used to assess the perceived symmetry and satisfaction in both groups.Results:Group A(cartilage scoring technique)demonstrated excellent postoperative symmetry,with a total symmetry score of 96/100 on cartilage grading.In group B,6 of the 10 individuals reported high satisfaction(score:10/10 points on VAS),whereas the remaining 4 reported moderate satisfaction(score:5–9/10 points).The total symmetry score in group B(cartilage excision technique)was 92/100,which was lower than that in group A.No relapses in angle measurements were observed in group A.In contrast,group B showed six average and four slightly overcorrected angle measurements.In group B,the postoperative measures were less satisfactory.Angle measurements were average angles in four cases,and somewhat overcorrected in six.Regarding the helix-to-mastoid distance,three cases were within the average range,whereas seven were slightly overcorrected.No relapses were observed in either group.Concerning complications,we employed minus scoring to measure all complications.Group A had a negative score of19,whereas group B had24.Conclusion:The outcomes were highly satisfactory for patients and surgeons treated using the cartilage scoring technique,and relatively less satisfactory for patients and surgeons treated using the cartilage excision technique.展开更多
Energy conservation and congestion control are widely researched topics in Wireless Sensor Networks in recent years. The main objective is to develop a model to find the optimized path on the basis of distance between...Energy conservation and congestion control are widely researched topics in Wireless Sensor Networks in recent years. The main objective is to develop a model to find the optimized path on the basis of distance between source and destination and the residual energy of the node. This paper shows an implementation of nature inspired improved Bat Algorithm to control congestion in Wireless Sensor Networks at transport layer. The Algorithm has been applied on the fitness function to obtain an optimum solution. Simulation results have shown improvement in parameters like network lifetime and throughput as compared with CODA (Congestion Detection and Avoidance), PSO (Particle Swarm Optimization) algorithm and ACO (Ant Colony Optimization).展开更多
基金partially supported by MRC(MC_PC_17171)Royal Society(RP202G0230)+8 种基金BHF(AA/18/3/34220)Hope Foundation for Cancer Research(RM60G0680)GCRF(20P2PF11)Sino-UK Industrial Fund(RP202G0289)LIAS(20P2ED10,20P2RE969)Data Science Enhancement Fund(20P2RE237)Fight for Sight(24NN201)Sino-UK Education Fund(OP202006)BBSRC(RM32G0178B8).
文摘The Bat algorithm,a metaheuristic optimization technique inspired by the foraging behaviour of bats,has been employed to tackle optimization problems.Known for its ease of implementation,parameter tunability,and strong global search capabilities,this algorithm finds application across diverse optimization problem domains.However,in the face of increasingly complex optimization challenges,the Bat algorithm encounters certain limitations,such as slow convergence and sensitivity to initial solutions.In order to tackle these challenges,the present study incorporates a range of optimization compo-nents into the Bat algorithm,thereby proposing a variant called PKEBA.A projection screening strategy is implemented to mitigate its sensitivity to initial solutions,thereby enhancing the quality of the initial solution set.A kinetic adaptation strategy reforms exploration patterns,while an elite communication strategy enhances group interaction,to avoid algorithm from local optima.Subsequently,the effectiveness of the proposed PKEBA is rigorously evaluated.Testing encompasses 30 benchmark functions from IEEE CEC2014,featuring ablation experiments and comparative assessments against classical algorithms and their variants.Moreover,real-world engineering problems are employed as further validation.The results conclusively demonstrate that PKEBA ex-hibits superior convergence and precision compared to existing algorithms.
基金supported in part by the National Natural Science Foundation of China under Grant No.62271453in part by the National Natural Science Foundation of China No.62101512+2 种基金in part by the Central Support for Local Projects under Grant No.YDZJSX2024D031in part by Project supported by the Shanxi Provincial Foundation for Leaders of Disciplines in Science,China under Grant No.2024Q022in part by Shanxi Province Patent Conversion Special Plan Funding Projects under Grant No.202405004。
文摘In the deployment of wireless networks in two-dimensional outdoor campus spaces,aiming at the problem of efficient coverage of the monitoring area by limited number of access points(APs),this paper proposes a deployment method of multi-objective optimization with virtual force fusion bat algorithm(VFBA)using the classical four-node regular distribution as an entry point.The introduction of Lévy flight strategy for bat position updating helps to maintain the population diversity,reduce the premature maturity problem caused by population convergence,avoid the over aggregation of individuals in the local optimal region,and enhance the superiority in global search;the virtual force algorithm simulates the attraction and repulsion between individuals,which enables individual bats to precisely locate the optimal solution within the search space.At the same time,the fusion effect of virtual force prompts the bat individuals to move faster to the potential optimal solution.To validate the effectiveness of the fusion algorithm,the benchmark test function is selected for simulation testing.Finally,the simulation result verifies that the VFBA achieves superior coverage and effectively reduces node redundancy compared to the other three regular layout methods.The VFBA also shows better coverage results when compared to other optimization algorithms.
文摘Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model(FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm(GA), particle swarm optimization(PSO) algorithm and differential mutation BA(DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms.
基金This work was supported in part by the Fundamental Research Funds for the Central Universities(YWF-22-L-1203)the National Natural Science Foundation of China(62173013,62073005)+1 种基金the National Key Research and Development Program of China(2020YFB1712203)U.S.National Science Foundation(CCF-0939370,CCF-1908308).
文摘Swarm intelligence in a bat algorithm(BA)provides social learning.Genetic operations for reproducing individuals in a genetic algorithm(GA)offer global search ability in solving complex optimization problems.Their integration provides an opportunity for improved search performance.However,existing studies adopt only one genetic operation of GA,or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions only.Differing from them,this work proposes an improved self-adaptive bat algorithm with genetic operations(SBAGO)where GA and BA are combined in a highly integrated way.Specifically,SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and high-quality.Guided by these exemplars,SBAGO improves both BA’s efficiency and global search capability.We evaluate this approach by using 29 widely-adopted problems from four test suites.SBAGO is also evaluated by a real-life optimization problem in mobile edge computing systems.Experimental results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness,search accuracy,local optima avoidance,and robustness.
基金This project is supported by National Science Foundation for Young Scientists of China(61701322)the Key Projects of Liaoning Natural Science Foundation(20170540700)+3 种基金the Key Projects of Liaoning Provincial Department of Education Science Foundation(L201702)Liaoning Natural Science Foundation(201502008,20102175)the Program for Liaoning Excellent Talents in University(LJQ2012011)the Liaoning Provincial Department of Education Science Foundation(L201630).
文摘Path planning algorithm is the key point to UAV path planning scenario.Many traditional path planning methods still suffer from low convergence rate and insufficient robustness.In this paper,three main methods are contributed to solving these problems.First,the improved artificial potential field(APF)method is adopted to accelerate the convergence process of the bat’s position update.Second,the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat algorithm.Third chaos strategy is proposed to avoid falling into a local optimum.Compared with standard APF and chaos strategy in UAV path planning scenarios,the improved algorithm CPFIBA(The improved artificial potential field method combined with chaotic bat algorithm,CPFIBA)significantly increases the success rate of finding suitable planning path and decrease the convergence time.Simulation results show that the proposed algorithm also has great robustness for processing with path planning problems.Meanwhile,it overcomes the shortcomings of the traditional meta-heuristic algorithms,as their convergence process is the potential to fall into a local optimum.From the simulation,we can see also obverse that the proposed CPFIBA provides better performance than BA and DEBA in problems of UAV path planning.
基金Supported by the National Natural Science Foundation of China(61472289)the Open Project Program of the State Key Laboratory of Digital Manufacturing Equipment and Technology(DMETKF2017016)
文摘Population-based algorithms have been used in many real-world problems.Bat algorithm(BA)is one of the states of the art of these approaches.Because of the super bat,on the one hand,BA can converge quickly;on the other hand,it is easy to fall into local optimum.Therefore,for typical BA algorithms,the ability of exploration and exploitation is not strong enough and it is hard to find a precise result.In this paper,we propose a novel bat algorithm based on cross boundary learning(CBL)and uniform explosion strategy(UES),namely BABLUE in short,to avoid the above contradiction and achieve both fast convergence and high quality.Different from previous opposition-based learning,the proposed CBL can expand the search area of population and then maintain the ability of global exploration in the process of fast convergence.In order to enhance the ability of local exploitation of the proposed algorithm,we propose UES,which can achieve almost the same search precise as that of firework explosion algorithm but consume less computation resource.BABLUE is tested with numerous experiments on unimodal,multimodal,one-dimensional,high-dimensional and discrete problems,and then compared with other typical intelligent optimization algorithms.The results show that the proposed algorithm outperforms other algorithms.
基金The APC was funded by PPPI,University Malaysia Sabah,KK,Sabah,Malaysia,https://www.ums.edu.my.
文摘Bat algorithm(BA)is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization problems.BA leverages the echolocation feature of bats produced by imitating the bats’searching behavior.BA faces premature convergence due to its local search capability.Instead of using the standard uniform walk,the Torus walk is viewed as a promising alternative to improve the local search capability.In this work,we proposed an improved variation of BA by applying torus walk to improve diversity and convergence.The proposed.Modern Computerized Bat Algorithm(MCBA)approach has been examined for fifteen well-known benchmark test problems.The finding of our technique shows promising performance as compared to the standard PSO and standard BA.The proposed MCBA,BPA,Standard PSO,and Standard BA have been examined for well-known benchmark test problems and training of the artificial neural network(ANN).We have performed experiments using eight benchmark datasets applied from the worldwide famous machine-learning(ML)repository of UCI.Simulation results have shown that the training of an ANN with MCBA-NN algorithm tops the list considering exactness,with more superiority compared to the traditional methodologies.The MCBA-NN algorithm may be used effectively for data classification and statistical problems in the future.
文摘Wireless sensor network (WSN) has been widely used due to its vastrange of applications. The energy problem is one of the important problems influencingthe complete application. Sensor nodes use very small batteries as a powersource and replacing them is not an easy task. With this restriction, the sensornodes must conserve their energy and extend the network lifetime as long as possible.Also, these limits motivate much of the research to suggest solutions in alllayers of the protocol stack to save energy. So, energy management efficiencybecomes a key requirement in WSN design. The efficiency of these networks ishighly dependent on routing protocols directly affecting the network lifetime.Clustering is one of the most popular techniques preferred in routing operations.In this work we propose a novel energy-efficient protocol for WSN based on a batalgorithm called ECO-BAT (Energy Consumption Optimization with BAT algorithmfor WSN) to prolong the network lifetime. We use an objective function thatgenerates an optimal number of sensor clusters with cluster heads (CH) to minimizeenergy consumption. The performance of the proposed approach is comparedwith Low-Energy Adaptive Clustering Hierarchy (LEACH) and EnergyEfficient cluster formation in wireless sensor networks based on the Multi-Objective Bat algorithm (EEMOB) protocols. The results obtained are interestingin terms of energy-saving and prolongation of the network lifetime.
文摘In the new competitive electricity market, the accurate operation management of Micro-Grid (MG) with various types of renewable power sources (RES) can be an effective approach to supply the electrical consumers more reliably and economically. In this regard, this paper proposes a novel solution methodology based on bat algorithm to solve the op- timal energy management of MG including several RESs with the back-up of Fuel Cell (FC), Wind Turbine (WT), Photovoltaics (PV), Micro Turbine (MT) as well as storage devices to meet the energy mismatch. The problem is formulated as a nonlinear constraint optimization problem to minimize the total cost of the grid and RESs, simultaneously. In addition, the problem considers the interactive effects of MG and utility in a 24 hour time interval which would in- crease the complexity of the problem from the optimization point of view more severely. The proposed optimization technique is consisted of a self adaptive modification method compromised of two modification methods based on bat algorithm to explore the total search space globally. The superiority of the proposed method over the other well-known algorithms is demonstrated through a typical renewable MG as the test system.
基金We deeply acknowledge Taif University for supporting this study through Taif University Researchers Supporting Project Number(TURSP-2020/313),Taif University,Taif,Saudi Arabia.
文摘All task scheduling applications need to ensure that resources are optimally used,performance is enhanced,and costs are minimized.The purpose of this paper is to discuss how to Fitness Calculate Values(FCVs)to provide application software with a reliable solution during the initial stages of load balancing.The cloud computing environment is the subject of this study.It consists of both physical and logical components(most notably cloud infrastructure and cloud storage)(in particular cloud services and cloud platforms).This intricate structure is interconnected to provide services to users and improve the overall system’s performance.This case study is one of the most important segments of cloud computing,i.e.,Load Balancing.This paper aims to introduce a new approach to balance the load among Virtual Machines(VM’s)of the cloud computing environment.The proposed method led to the proposal and implementation of an algorithm inspired by the Bat Algorithm(BA).This proposed Modified Bat Algorithm(MBA)allows balancing the load among virtual machines.The proposed algorithm works in two variants:MBA with Overloaded Optimal Virtual Machine(MBAOOVM)and Modified Bat Algorithm with Balanced Virtual Machine(MBABVM).MBA generates cost-effective solutions and the strengths of MBA are finally validated by comparing it with Bat Algorithm.
基金The National Natural Science Foundation of China(No.51178157)High-Level Project of the Top Six Talents in Jiangsu Province(No.JXQC-021)+1 种基金Key Science and Technology Program in Henan Province(No.182102310004)the Humanities and Social Science Research Programs Foundation of the M inistry of Education of China(No.18YJAZH028)
文摘The projection pursuit model is used to study the assessment of air pollution caused by vehicle emissions at intersections. Based on the analysis of the characteristics and regularities of vehicle emissions at intersections, a vehicle emission model based on projection pursuit is established, and the bat algorithm is used to solve the optimization function. The research results show that the projection pursuit model can not only measure the air pollution of vehicle emissions at intersections, but also effectively evaluate the level of vehicle exhaust emissions at intersections. Taking the air pollution caused by vehicle emissions at intersections as the research object and considering the influence factors of vehicle emissions on air pollution comprehensively, the evaluation index system of vehicle emissions at intersections on air pollution is constructed. Based on large data analysis, a prediction model of air pollution caused by vehicle emissions at intersections is constructed, and an improved bat algorithm is used to realize the assessment process. The application results show that the prediction model of vehicle emissions at intersections can define the degree of air pollution caused by vehicle emissions, and it has good guiding significance and practical value for solving the problem of air pollution caused by vehicle emissions.
基金supported by National Natural Science Foundation of China(No.42302336)Project of the Department of Science and Technology of Sichuan Province(No.2024YFHZ0098,No.2023NSFSC0751)Open Project of Chengdu University of Information Technology(KYQN202317,760115027,KYTZ202278,KYTZ202280).
文摘Unmanned aerial vehicle(UAV)paths in the field directly affect the efficiency and accuracy of payload data collection.Path planning of UAV advancing along river valleys in wild environments is one of the first and most difficult problems faced by unmanned surveys of debris flow valleys.This study proposes a new hybrid bat optimization algorithm,GRE-Bat(Good point set,Reverse learning,Elite Pool-Bat algorithm),for unmanned exploration path planning of debris flow sources in outdoor environments.In the GRE-Bat algorithm,the good point set strategy is adopted to evenly distribute the population,ensure sufficient coverage of the search space,and improve the stability of the convergence accuracy of the algorithm.Subsequently,a reverse learning strategy is introduced to increase the diversity of the population and improve the local stagnation problem of the algorithm.In addition,an Elite pool strategy is added to balance the replacement and learning behaviors of particles within the population based on elimination and local perturbation factors.To demonstrate the effectiveness of the GRE-Bat algorithm,we conducted multiple simulation experiments using benchmark test functions and digital terrain models.Compared to commonly used path planning algorithms such as the Bat Algorithm(BA)and the Improved Sparrow Search Algorithm(ISSA),the GRE-Bat algorithm can converge to the optimal value in different types of test functions and obtains a near-optimal solution after an average of 60 iterations.The GRE-Bat algorithm can obtain higher quality flight routes in the designated environment of unmanned investigation in the debris flow gully basin,demonstrating its potential for practical application.
基金This research is supported by Tier-1 Research Grant, vote no. H938 by ResearchManagement Office (RMC), Universiti Tun Hussein Onn Malaysia and Ministry of Higher Education,Malaysia.
文摘Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on WL5.While on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.
文摘In 2015,the discovery of mammalian fossils,paleolithic artifacts,and burned bones in the Maoershan Cave of the Guilin Basin,northeastern Guangxi,indicated that it is a late Middle Pleistocene Paleolithic site.In 2021,stratigraphic sectioning and the systematic screening of small mammal sand samples were conducted.This paper presents a comprehensive account of the new material of the fruit bat Rousettus leschenaultii,accompanied by a concise overview of the non-volant micromammals that coexisted with it in the Quaternary sediments of the Maoershan Cave.This finding marks the second occurrence of fruit bat fossils in China,contributing to our understanding of the dental morphology and past geographical distribution of Rousettus.The micromammalian assemblage of the Maoershan Cave is composed of 3 orders,9 families,26 genera,and 30 species,and exhibits notable similarities with those of the late Middle Pleistocene Yumi Cave,Xinglong Cave,Yanhui Cave,Mawokou Cave,Zhongliangshan,and Chuan Cave faunas.The biochronology of the micromammalian assemblage from the Maoershan Cave has been determined to be consistent with the late Middle Pleistocene,providing a new assemblage in the Pleistocene mammalian faunal sequence in southern China.The presence of abundant oriental forested elements indicates that the Guilin Basin used to be a humid and warm subtropical forest paleoenvironment similar to the contemporary environment during the late Middle Pleistocene.
基金funding support from the United States Fish and Wildlife Service,the North Carolina Wildlife Resources Commission,and the University of North Carolina at Greensboro,as part of a collective effort for the North American Bat Monitoring Program(NABat).
文摘Temperate forest ecosystems are important habitats for many bat species. However, these habitats are increasingly affected by anthropogenic disturbances, particularly urban development, leading to landscapes with varying land cover composition and configuration. Limited research has examined how forest and urban landscape composition and configuration influence bat activity and diversity. Using a multi-year statewide bat acoustic monitoring dataset from North Carolina, USA, we investigated the effects of forest and urban composition and configuration at multiple spatial scales on bat activity and diversity. First, we constructed single-variable landscape index regression models and found that both the composition and configuration of forests and urban developments influenced bat activity and diversity in a species-specific manner. Next, we applied a hierarchical partitioning approach to compare the relative contributions of composition and configuration indices in explaining variance in bat activity. For big brown bats and hoary bats, evergreen forest and urban development composition indices contributed the most to explaining activity variance. In contrast, for eastern red bats, evening bats, and tricolored bats, deciduous forest fragmentation indices describing landscape configuration were the most influential factors. Silver-haired bat activity variance was primarily explained by an evergreen forest fragmentation index. Lastly, urban development configuration indices were the strongest predictors of Mexican free-tailed bat activity and total bat activity. These results suggest that forest and urban landscape configuration should be considered in conservation and management planning for North American temperate forest ecosystems, particularly in regions that have not experienced drastic deforestation in recent decades.
文摘Background:Bat ear is a congenital condition that can have psychological effects on patients and is readily correctable,often yielding highly satisfactory outcomes for both patients and surgeons.It is crucial to select appropriate techniques in the surgical plan to ensure optimal outcomes.This study aimed to compare the outcomes of otoplasty using the cartilage scoring technique versus the cartilage excision technique for correcting the antihelix.Methods:Twenty patients were divided into two groups:group A underwent the cartilage scoring technique,and group B received the cartilage excision technique.Symmetry,satisfaction,and complications were evaluated and compared between the groups.A visual analog score(VAS)was used to assess the perceived symmetry and satisfaction in both groups.Results:Group A(cartilage scoring technique)demonstrated excellent postoperative symmetry,with a total symmetry score of 96/100 on cartilage grading.In group B,6 of the 10 individuals reported high satisfaction(score:10/10 points on VAS),whereas the remaining 4 reported moderate satisfaction(score:5–9/10 points).The total symmetry score in group B(cartilage excision technique)was 92/100,which was lower than that in group A.No relapses in angle measurements were observed in group A.In contrast,group B showed six average and four slightly overcorrected angle measurements.In group B,the postoperative measures were less satisfactory.Angle measurements were average angles in four cases,and somewhat overcorrected in six.Regarding the helix-to-mastoid distance,three cases were within the average range,whereas seven were slightly overcorrected.No relapses were observed in either group.Concerning complications,we employed minus scoring to measure all complications.Group A had a negative score of19,whereas group B had24.Conclusion:The outcomes were highly satisfactory for patients and surgeons treated using the cartilage scoring technique,and relatively less satisfactory for patients and surgeons treated using the cartilage excision technique.
文摘Energy conservation and congestion control are widely researched topics in Wireless Sensor Networks in recent years. The main objective is to develop a model to find the optimized path on the basis of distance between source and destination and the residual energy of the node. This paper shows an implementation of nature inspired improved Bat Algorithm to control congestion in Wireless Sensor Networks at transport layer. The Algorithm has been applied on the fitness function to obtain an optimum solution. Simulation results have shown improvement in parameters like network lifetime and throughput as compared with CODA (Congestion Detection and Avoidance), PSO (Particle Swarm Optimization) algorithm and ACO (Ant Colony Optimization).
