In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update ...In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update based on two extreme values: personal best and global best, which limits the diversity of information. Ideally, particles should learn from multiple advantageous particles to enhance interactivity and optimization efficiency. Accordingly, this paper proposes a PSO that simulates the evolutionary dynamics of species survival in mountain peak ecology (PEPSO) for feature selection. Based on the pyramid topology, the algorithm simulates the features of mountain peak ecology in nature and the competitive-cooperative strategies among species. According to the principles of the algorithm, the population is first adaptively divided into many subgroups based on the fitness level of particles. Then, particles within each subgroup are divided into three different types based on their evolutionary levels, employing different adaptive inertia weight rules and dynamic learning mechanisms to define distinct learning modes. Consequently, all particles play their respective roles in promoting the global optimization performance of the algorithm, similar to different species in the ecological pattern of mountain peaks. Experimental validation of the PEPSO performance was conducted on 18 public datasets. The experimental results demonstrate that the PEPSO outperforms other PSO variant-based feature selection methods and mainstream feature selection methods based on intelligent optimization algorithms in terms of overall performance in global search capability, classification accuracy, and reduction of feature space dimensions. Wilcoxon signed-rank test also confirms the excellent performance of the PEPSO.展开更多
Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the chall...Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.展开更多
The optimal allocation model of regional water resources is built with the purpose of maximizing the comprehensive economic,social and environmental benefits of regional water consumption.In order to solve the problem...The optimal allocation model of regional water resources is built with the purpose of maximizing the comprehensive economic,social and environmental benefits of regional water consumption.In order to solve the problems that easily appear during the model solution of regional water resource optimal allocation with multiple water sources,multiple users and multiple objectives like"curse of dimensionality"or sinking into local optimum,this paper proposes a particle swarm optimization(PSO)algorithm based on immune evolutionary algorithm(IEA).This algorithm introduces immunology principle into particle swarm algorithm.Its immune memorizing and self-adjusting mechanism is utilized to keep the particles in the fitness level at a certain concentration and guarantee the diversity of population.Also,the global search characteristics of IEA and the local search capacity of particle swarm algorithm have been fully utilized to overcome the dependence of PSO on initial swarm and the deficiency of vulnerability to local optimum.After applying this model to the allocation of water resources in Zhoukou,we obtain the scheme for optimization allocation of water resources in the planning level years,i.e.2015and 2025 under the guarantee rate of 50%.The calculation results indicate that the application of this algorithm to solve the issue of optimal allocation of regional water resources is reliable and reasonable.Thus it ofers a new idea for solving the issue of optimal allocation of water resources.展开更多
An efficient method is proposed for the design of finite impulse response(FIR) filter with arbitrary pass band edge,stop band edge frequencies and transition width.The proposed FIR band stop filter is designed using c...An efficient method is proposed for the design of finite impulse response(FIR) filter with arbitrary pass band edge,stop band edge frequencies and transition width.The proposed FIR band stop filter is designed using craziness based particle swarm optimization(CRPSO) approach.Given the filter specifications to be realized,the CRPSO algorithm generates a set of optimal filter coefficients and tries to meet the ideal frequency response characteristics.In this paper,for the given problem,the realizations of the optimal FIR band pass filters of different orders have been performed.The simulation results have been compared with those obtained by the well accepted evolutionary algorithms,such as Parks and McClellan algorithm(PMA),genetic algorithm(GA) and classical particle swarm optimization(PSO).Several numerical design examples justify that the proposed optimal filter design approach using CRPSO outperforms PMA and PSO,not only in the accuracy of the designed filter but also in the convergence speed and solution quality.展开更多
A surrogate based particle swarm optimization （SBPSO） algorithm which combines the surrogate modeling technique and particle swarm optimization is applied to the reliability- based robust design （RBRD） of composit...A surrogate based particle swarm optimization （SBPSO） algorithm which combines the surrogate modeling technique and particle swarm optimization is applied to the reliability- based robust design （RBRD） of composite pressure vessels. The algorithm and efficiency of SBPSO are displayed through numerical examples. A model for filament-wound composite pressure vessels with metallic liner is then studied by netting analysis and its responses are analyzed by using Finite element method （performed by software ANSYS）. An optimization problem for maximizing the performance factor is formulated by choosing the winding orientation of the helical plies in the cylindrical portion, the thickness of metal liner and the drop off region size as the design variables. Strength constraints for composite layers and the metal liner are constructed by using Tsai-Wu failure criterion and Mises failure criterion respectively. Numerical examples show that the method proposed can effectively solve the RBRD problem, and the optimal results of the proposed model can satisfy certain reliability requirement and have the robustness to the fluctuation of design variables.展开更多
The collective behavior of certain animals and insects has the characteristic of self-organization. The simple interactions among individuals can produce complex adaptive patterns at the level of the group. Recently,n...The collective behavior of certain animals and insects has the characteristic of self-organization. The simple interactions among individuals can produce complex adaptive patterns at the level of the group. Recently,new scientific investigation pointed out that desert locusts show extreme phenotypic plasticity in transforming between the lonely phase and the swarming gregarious phase depending on the population density,which is controlled by a serotonin called 5-hydroxytryptamine( 5HT). In this paper,based on the mechanism of the locusts' collective behavior,a new particle swarm optimization technique called LBPSO is studied. The number of swarms is selfadaptively adjusted by the acquired outstanding particles coming from behind the previous global best solution. The swarm sizes are related to the corresponding serotonin 5HT,which is determined by the optimization parameters such as global best and iteration number. And each swarm adopts one of three rules below according to its density, generalized social evolution strategy, generalized cognition evolution strategy and the independent moving strategy. A comparative study of LBPSO,social particle swarm optimization( SPSO), improved SPSO and the standard particle swarm optimization( StdPSO) on their abilities of tracking optima is carried out. And the results under four static benchmark functions and a dynamic function generator moving peaks benchmark( MPB)show that LBPSO outperforms the other three functions in both static and dynamic landscapes due to the introduced locusts' collective behavior.展开更多
Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and quality.Classification and accurate identification of all types of weeds is a cha...Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and quality.Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity.To address this issue,an efficient weed classification model is proposed with the Deep Convolutional Neural Network(CNN)that implements automatic feature extraction and performs complex feature learning for image classification.Throughout this work,weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds based on the two publicly available weed datasets.The Tamil Nadu Agricultural University(TNAU)dataset used as afirst dataset that consists of 40 classes of weed images and the other dataset is from Indian Council of Agriculture Research–Directorate of Weed Research(ICAR-DWR)which contains 50 classes of weed images.An effective Particle Swarm Optimization(PSO)technique is applied in the proposed CNN to automa-tically evolve and improve its classification accuracy.The proposed model was evaluated and compared with pre-trained transfer learning models such as GoogLeNet,AlexNet,Residual neural Network(ResNet)and Visual Geometry Group Network(VGGNet)for weed classification.This work shows that the performance of the PSO assisted proposed CNN model is significantly improved the success rate by 98.58%for TNAU and 97.79%for ICAR-DWR weed datasets.展开更多
A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and o...A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and optimization problems are typicallyvery complex with a huge solution space, large number of variables, and multiple objectives. Heuristic algorithms can solve theseproblems in an acceptable time but are usually limited to some particular problem circumstances. On the other hand, evolutionaryalgorithms(EAs), which are general stochastic algorithms inspired by the natural biological evolution and/or social behavior of species, can theoretically be used to solve any complex optimization problems including those found in SDNs. This paper reviewsfour types of EAs that are widely applied in current SDNs: Genetic Algorithms(GAs), Particle Swarm Optimization(PSO), Ant Colony Optimization(ACO), and Simulated Annealing(SA) by discussing their techniques, summarizing their representative applications, and highlighting their issues and future works. To the best of our knowledge, our work is the first that compares the tech-niques and categorizes the applications of these four EAs in SDNs.展开更多
One of the main problems of machine learning and data mining is to develop a basic model with a few features,to reduce the algorithms involved in classification’s computational complexity.In this paper,the collection...One of the main problems of machine learning and data mining is to develop a basic model with a few features,to reduce the algorithms involved in classification’s computational complexity.In this paper,the collection of features has an essential importance in the classification process to be able minimize computational time,which decreases data size and increases the precision and effectiveness of specific machine learning activities.Due to its superiority to conventional optimization methods,several metaheuristics have been used to resolve FS issues.This is why hybrid metaheuristics help increase the search and convergence rate of the critical algorithms.A modern hybrid selection algorithm combining the two algorithms;the genetic algorithm(GA)and the Particle Swarm Optimization(PSO)to enhance search capabilities is developed in this paper.The efficacy of our proposed method is illustrated in a series of simulation phases,using the UCI learning array as a benchmark dataset.展开更多
The previous particle swarm optimizers lack direct mechanism to prevent particles beyond predefined search space, which results in invalid solutions in some special cases. A momentum factor is introduced into the orig...The previous particle swarm optimizers lack direct mechanism to prevent particles beyond predefined search space, which results in invalid solutions in some special cases. A momentum factor is introduced into the original particle swarm optimizer to resolve this problem. Furthermore, in order to accelerate convergence, a new strategy about updating velocities is given. The resulting approach is mromentum-PSO which guarantees that particles are never beyond predefined search space without checking boundary in every iteration. In addition, linearly decreasing wight PSO （LDW-PSO） equipped with a boundary checking strategy is also discussed, which is denoted as LDWBC-PSO. LDW-PSO, LDWBC-PSO and momentum-PSO are compared in optimization on five test functions. The experimental results show that in some special cases LDW-PSO finds invalid solutions and LDWBC-PSO has poor performance, while momentum-PSO not only exhibits good performance but also reduces computational cost for updating velocities.展开更多
Purpose–The purpose of this paper is to propose an algorithm that combines the particle swarm optimization(PSO)with the biogeography-based optimization(BBO)algorithm.Design/methodology/approach–The BBO and the PSO a...Purpose–The purpose of this paper is to propose an algorithm that combines the particle swarm optimization(PSO)with the biogeography-based optimization(BBO)algorithm.Design/methodology/approach–The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both algorithms.The efficiency of the proposed algorithm is tested using some selected standard benchmark functions.The performance of the proposed algorithm is compared with that of the differential evolutionary(DE),genetic algorithm(GA),PSO,BBO,blended BBO and hybrid BBO-DE algorithms.Findings–Experimental results indicate that the proposed algorithm outperforms the BBO,PSO,DE,GA,and the blended BBO algorithms and has comparable performance to that of the hybrid BBO-DE algorithm.However,the proposed algorithm is simpler than the BBO-DE algorithm since the PSO does not have complex operations such as mutation and crossover used in the DE algorithm.Originality/value–The proposed algorithm is a generic algorithm that can be used to efficiently solve optimization problems similar to that solved using other popular evolutionary algorithms but with better performance.展开更多
为解决在IIoT(industrial internet of things)环境下,现有的调度算法调度工作流中通信频繁、数据传输量大的任务所带来的完工时间上升、成本增加等影响的问题,提出一种基于聚类的工作流多雾协同调度算法。通过二分K均值算法对工作流中...为解决在IIoT(industrial internet of things)环境下,现有的调度算法调度工作流中通信频繁、数据传输量大的任务所带来的完工时间上升、成本增加等影响的问题,提出一种基于聚类的工作流多雾协同调度算法。通过二分K均值算法对工作流中的任务进行聚类,基于聚类结果,在多个雾服务器之间使用改进的免疫粒子群优化算法进行任务调度。实验结果表明,该算法相比其它一些传统的调度算法在完工时间、成本、负载均衡方面都有一定提升。展开更多
文摘In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update based on two extreme values: personal best and global best, which limits the diversity of information. Ideally, particles should learn from multiple advantageous particles to enhance interactivity and optimization efficiency. Accordingly, this paper proposes a PSO that simulates the evolutionary dynamics of species survival in mountain peak ecology (PEPSO) for feature selection. Based on the pyramid topology, the algorithm simulates the features of mountain peak ecology in nature and the competitive-cooperative strategies among species. According to the principles of the algorithm, the population is first adaptively divided into many subgroups based on the fitness level of particles. Then, particles within each subgroup are divided into three different types based on their evolutionary levels, employing different adaptive inertia weight rules and dynamic learning mechanisms to define distinct learning modes. Consequently, all particles play their respective roles in promoting the global optimization performance of the algorithm, similar to different species in the ecological pattern of mountain peaks. Experimental validation of the PEPSO performance was conducted on 18 public datasets. The experimental results demonstrate that the PEPSO outperforms other PSO variant-based feature selection methods and mainstream feature selection methods based on intelligent optimization algorithms in terms of overall performance in global search capability, classification accuracy, and reduction of feature space dimensions. Wilcoxon signed-rank test also confirms the excellent performance of the PEPSO.
基金jointly supported by the Jiangsu Postgraduate Research and Practice Innovation Project under Grant KYCX22_1030,SJCX22_0283 and SJCX23_0293the NUPTSF under Grant NY220201.
文摘Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.
基金the National Natural Science Foundation of China(No.40839902)
文摘The optimal allocation model of regional water resources is built with the purpose of maximizing the comprehensive economic,social and environmental benefits of regional water consumption.In order to solve the problems that easily appear during the model solution of regional water resource optimal allocation with multiple water sources,multiple users and multiple objectives like"curse of dimensionality"or sinking into local optimum,this paper proposes a particle swarm optimization(PSO)algorithm based on immune evolutionary algorithm(IEA).This algorithm introduces immunology principle into particle swarm algorithm.Its immune memorizing and self-adjusting mechanism is utilized to keep the particles in the fitness level at a certain concentration and guarantee the diversity of population.Also,the global search characteristics of IEA and the local search capacity of particle swarm algorithm have been fully utilized to overcome the dependence of PSO on initial swarm and the deficiency of vulnerability to local optimum.After applying this model to the allocation of water resources in Zhoukou,we obtain the scheme for optimization allocation of water resources in the planning level years,i.e.2015and 2025 under the guarantee rate of 50%.The calculation results indicate that the application of this algorithm to solve the issue of optimal allocation of regional water resources is reliable and reasonable.Thus it ofers a new idea for solving the issue of optimal allocation of water resources.
文摘An efficient method is proposed for the design of finite impulse response(FIR) filter with arbitrary pass band edge,stop band edge frequencies and transition width.The proposed FIR band stop filter is designed using craziness based particle swarm optimization(CRPSO) approach.Given the filter specifications to be realized,the CRPSO algorithm generates a set of optimal filter coefficients and tries to meet the ideal frequency response characteristics.In this paper,for the given problem,the realizations of the optimal FIR band pass filters of different orders have been performed.The simulation results have been compared with those obtained by the well accepted evolutionary algorithms,such as Parks and McClellan algorithm(PMA),genetic algorithm(GA) and classical particle swarm optimization(PSO).Several numerical design examples justify that the proposed optimal filter design approach using CRPSO outperforms PMA and PSO,not only in the accuracy of the designed filter but also in the convergence speed and solution quality.
基金supported by the Natural Science Foundation of China(No.10772070)National Basic Research Program of China(No.2011CB013800)
文摘A surrogate based particle swarm optimization （SBPSO） algorithm which combines the surrogate modeling technique and particle swarm optimization is applied to the reliability- based robust design （RBRD） of composite pressure vessels. The algorithm and efficiency of SBPSO are displayed through numerical examples. A model for filament-wound composite pressure vessels with metallic liner is then studied by netting analysis and its responses are analyzed by using Finite element method （performed by software ANSYS）. An optimization problem for maximizing the performance factor is formulated by choosing the winding orientation of the helical plies in the cylindrical portion, the thickness of metal liner and the drop off region size as the design variables. Strength constraints for composite layers and the metal liner are constructed by using Tsai-Wu failure criterion and Mises failure criterion respectively. Numerical examples show that the method proposed can effectively solve the RBRD problem, and the optimal results of the proposed model can satisfy certain reliability requirement and have the robustness to the fluctuation of design variables.
基金Major State Basic Research Development Program of China(No.2012CB720500)National Natural Science Foundations of China(Nos.61174118,21376077,61222303)the Fundamental Research Funds for the Central Universities and Shanghai Leading Academic Discipline Project,China(No.B504)
文摘The collective behavior of certain animals and insects has the characteristic of self-organization. The simple interactions among individuals can produce complex adaptive patterns at the level of the group. Recently,new scientific investigation pointed out that desert locusts show extreme phenotypic plasticity in transforming between the lonely phase and the swarming gregarious phase depending on the population density,which is controlled by a serotonin called 5-hydroxytryptamine( 5HT). In this paper,based on the mechanism of the locusts' collective behavior,a new particle swarm optimization technique called LBPSO is studied. The number of swarms is selfadaptively adjusted by the acquired outstanding particles coming from behind the previous global best solution. The swarm sizes are related to the corresponding serotonin 5HT,which is determined by the optimization parameters such as global best and iteration number. And each swarm adopts one of three rules below according to its density, generalized social evolution strategy, generalized cognition evolution strategy and the independent moving strategy. A comparative study of LBPSO,social particle swarm optimization( SPSO), improved SPSO and the standard particle swarm optimization( StdPSO) on their abilities of tracking optima is carried out. And the results under four static benchmark functions and a dynamic function generator moving peaks benchmark( MPB)show that LBPSO outperforms the other three functions in both static and dynamic landscapes due to the introduced locusts' collective behavior.
文摘Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and quality.Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity.To address this issue,an efficient weed classification model is proposed with the Deep Convolutional Neural Network(CNN)that implements automatic feature extraction and performs complex feature learning for image classification.Throughout this work,weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds based on the two publicly available weed datasets.The Tamil Nadu Agricultural University(TNAU)dataset used as afirst dataset that consists of 40 classes of weed images and the other dataset is from Indian Council of Agriculture Research–Directorate of Weed Research(ICAR-DWR)which contains 50 classes of weed images.An effective Particle Swarm Optimization(PSO)technique is applied in the proposed CNN to automa-tically evolve and improve its classification accuracy.The proposed model was evaluated and compared with pre-trained transfer learning models such as GoogLeNet,AlexNet,Residual neural Network(ResNet)and Visual Geometry Group Network(VGGNet)for weed classification.This work shows that the performance of the PSO assisted proposed CNN model is significantly improved the success rate by 98.58%for TNAU and 97.79%for ICAR-DWR weed datasets.
文摘A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and optimization problems are typicallyvery complex with a huge solution space, large number of variables, and multiple objectives. Heuristic algorithms can solve theseproblems in an acceptable time but are usually limited to some particular problem circumstances. On the other hand, evolutionaryalgorithms(EAs), which are general stochastic algorithms inspired by the natural biological evolution and/or social behavior of species, can theoretically be used to solve any complex optimization problems including those found in SDNs. This paper reviewsfour types of EAs that are widely applied in current SDNs: Genetic Algorithms(GAs), Particle Swarm Optimization(PSO), Ant Colony Optimization(ACO), and Simulated Annealing(SA) by discussing their techniques, summarizing their representative applications, and highlighting their issues and future works. To the best of our knowledge, our work is the first that compares the tech-niques and categorizes the applications of these four EAs in SDNs.
基金This work was partially supported by the National Natural Science Foundation of China(61876089,61876185,61902281,61375121)the Opening Project of Jiangsu Key Laboratory of Data Science and Smart Software(No.2019DS301)+1 种基金the Engineering Research Center of Digital Forensics,Ministry of Education,the Key Research and Development Program of Jiangsu Province(BE2020633)the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘One of the main problems of machine learning and data mining is to develop a basic model with a few features,to reduce the algorithms involved in classification’s computational complexity.In this paper,the collection of features has an essential importance in the classification process to be able minimize computational time,which decreases data size and increases the precision and effectiveness of specific machine learning activities.Due to its superiority to conventional optimization methods,several metaheuristics have been used to resolve FS issues.This is why hybrid metaheuristics help increase the search and convergence rate of the critical algorithms.A modern hybrid selection algorithm combining the two algorithms;the genetic algorithm(GA)and the Particle Swarm Optimization(PSO)to enhance search capabilities is developed in this paper.The efficacy of our proposed method is illustrated in a series of simulation phases,using the UCI learning array as a benchmark dataset.
文摘The previous particle swarm optimizers lack direct mechanism to prevent particles beyond predefined search space, which results in invalid solutions in some special cases. A momentum factor is introduced into the original particle swarm optimizer to resolve this problem. Furthermore, in order to accelerate convergence, a new strategy about updating velocities is given. The resulting approach is mromentum-PSO which guarantees that particles are never beyond predefined search space without checking boundary in every iteration. In addition, linearly decreasing wight PSO （LDW-PSO） equipped with a boundary checking strategy is also discussed, which is denoted as LDWBC-PSO. LDW-PSO, LDWBC-PSO and momentum-PSO are compared in optimization on five test functions. The experimental results show that in some special cases LDW-PSO finds invalid solutions and LDWBC-PSO has poor performance, while momentum-PSO not only exhibits good performance but also reduces computational cost for updating velocities.
基金supported by the deanship of research at Jordan University of Science and Technology.
文摘Purpose–The purpose of this paper is to propose an algorithm that combines the particle swarm optimization(PSO)with the biogeography-based optimization(BBO)algorithm.Design/methodology/approach–The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both algorithms.The efficiency of the proposed algorithm is tested using some selected standard benchmark functions.The performance of the proposed algorithm is compared with that of the differential evolutionary(DE),genetic algorithm(GA),PSO,BBO,blended BBO and hybrid BBO-DE algorithms.Findings–Experimental results indicate that the proposed algorithm outperforms the BBO,PSO,DE,GA,and the blended BBO algorithms and has comparable performance to that of the hybrid BBO-DE algorithm.However,the proposed algorithm is simpler than the BBO-DE algorithm since the PSO does not have complex operations such as mutation and crossover used in the DE algorithm.Originality/value–The proposed algorithm is a generic algorithm that can be used to efficiently solve optimization problems similar to that solved using other popular evolutionary algorithms but with better performance.
文摘为解决在IIoT(industrial internet of things)环境下,现有的调度算法调度工作流中通信频繁、数据传输量大的任务所带来的完工时间上升、成本增加等影响的问题,提出一种基于聚类的工作流多雾协同调度算法。通过二分K均值算法对工作流中的任务进行聚类,基于聚类结果,在多个雾服务器之间使用改进的免疫粒子群优化算法进行任务调度。实验结果表明,该算法相比其它一些传统的调度算法在完工时间、成本、负载均衡方面都有一定提升。
