A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems

Yazdani，Delaram1; Yazdani，Danial2; Yazdani，Donya3; Omidvar，Mohammad Nabi4; Gandomi，Amir H.2,5; Yao，Xin6,7

2023-12-12

10.1145/3604812

ACM Transactions on Evolutionary Learning and Optimization   影响因子和分区

2688-299X

2688-3007

Population clustering methods, which consider the position and fitness of individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most of these methods use a fixed population size, making them inflexible and inefficient when the number of promising regions is unknown. The lack of a functional relationship between the population size and the number of promising regions significantly degrades performance and limits an algorithm’s agility to respond to dynamic changes. To address this issue, we propose a new species-based particle swarm optimization with adaptive population size and number of sub-populations for solving dynamic optimization problems. The proposed algorithm also benefits from a novel systematic adaptive deactivation component that, unlike the previous deactivation components, adapts the computational resource allocation to the sub-populations by considering various characteristics of both the problem and the sub-populations. We evaluate the performance of our proposed algorithm for the Generalized Moving Peaks Benchmark and compare the results with several peer approaches. The results indicate the superiority of the proposed method.

Computational resource allocation Evolutionary dynamic optimization Particle swarm optimization Single-objective dynamic optimization problems Tracking moving global optimum

EI

英语

通讯

20240115309808

Benchmarking ; Cluster analysis ; Particle swarm optimization (PSO) ; Population statistics

Computer Software, Data Handling and Applications:723 ; Management:912.2 ; Optimization Techniques:921.5

2-s2.0-85181002907

Scopus

1.Department of Computer Engineering,Mashhad Branch,Azad University,Mashhad,9187147578,Iran
2.Faculty of Engineering & Information Technology,University of Technology Sydney,Ultimo,2007,Australia
3.AI Lab,British Antarctic Survey,Cambridge,CB3 0ET,United Kingdom
4.School of Computing,Leeds University Business School,University of Leeds,Leeds,LS2 9JT,United Kingdom
5.University Research and Innovation Center (EKIK),Obuda University,Budapest,1034,Hungary
6.Research Institute of Trustworthy Autonomous Systems (RITAS),Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
7.The Center of Excellence for Research in Computational Intelligence and Applications (CERCIA),School of Computer Science,University of Birmingham,Birmingham,B15 2TT,United Kingdom

Yazdani，Delaram,Yazdani，Danial,Yazdani，Donya,et al. A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems[J]. ACM Transactions on Evolutionary Learning and Optimization,2023,3(4).

Yazdani，Delaram,Yazdani，Danial,Yazdani，Donya,Omidvar，Mohammad Nabi,Gandomi，Amir H.,&Yao，Xin.(2023).A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems.ACM Transactions on Evolutionary Learning and Optimization,3(4).

Yazdani，Delaram,et al."A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems".ACM Transactions on Evolutionary Learning and Optimization 3.4(2023).