Shakhnaz Akhmedova
ABSTRACT
In this study a new multi-swarm hybrid algorithm is designed for dynamic optimization problems. It is based on two well-known stochastic approaches such as the Particle Swarm Optimization (PSO) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) applied with additional modifications to make them able to find better solutions before the environmental changes. Additionally, an adaptation strategy is proposed to regulate the number of active swarms, so that new swarms would be brought into existence or redundant swarms would be removed to maintain the multi-swarm diversity. The Generalized Moving Peak Benchmark is used to evaluate the performance of the proposed algorithm and to compare it to the alternative approaches. Obtained results demonstrated usefulness of the new algorithm as it outperformed other alternative approaches.
- Michalis Mavrovouniotis, Changhe Li and Shengxiang Yang, 2017. A survey of swarm intelligence for dynamic optimization: Algorithms and applications. Swarm and Evolutionary Computation, 33, 1--17. Google Scholar
Cross Ref
- James Kennedy and Russell C. Eberhart. 1995. Particle swarm optimization. In Proceedings of the International Conference on Neural Networks (ICNN 1995). Vol.4, 1942--1948. Google ScholarCross Ref
- Nikolaus Hansen. 2016. The CMA evolution strategy: A tutorial. ArXiv, abs/1604.00772.Google Scholar
- Tim M. Blackwell and Jürgen Branke, 2006. Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Transactions on Evolutionary Computation, 10, 459--472. Google ScholarDigital Library
- Danial Yazdani, Mohammad Nabi Omidvar, Ran Cheng, Jürgen Branke, Trung-Thanh Nguyen and Xin Yao, 2020. Benchmarking continuous dynamic optimization: Survey and generalized test suite. IEEE transactions on cybernetics, PP. Google ScholarCross Ref
- Tim M. Blackwell and Jürgen Branke, 2006. Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Transactions on Evolutionary Computation, 10, 459--472. Google ScholarDigital Library
- Hui Wang, Yong Liu, Changhe Li and Sanyou Zeng. 2007. A hybrid particle swarm algorithm with Cauchy mutation. In Proceedings of the IEEE Swarm Intelligence Symposium (SIS 2007). 356--360. Google ScholarDigital Library
- Guanyu Hu and Pei-Li Qiao, 2015. An efficient improvement of CMA-ES algorithm for the network security situation prediction. The Open Automation and Control Systems Journal, 7. Google ScholarCross Ref
Index Terms
- A hybrid self-adapting multi-swarm algorithm based on PSO and CMA-ES for continuous dynamic optimization
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