In this paper, a success-history based parameter adaptation is proposed for the local search procedure used in particle swarm optimization algorithm with multiple swarms. The proposed modified version of the mQSO algorithm is considered within the generalized moving peaks dynamic optimization benchmark set. For the experiments, a set of 12 benchmark problems are chosen from the CEC 2022 competition on dynamic optimization. The experiments involving parameter sensitivity analysis have shown that the adaptive local search with particles generated next to the global best of each swarm with normal distribution allows improving the overall performance of the algorithm in terms of current error and best error before each environment change. An additional set of experiments with increased shifts of peaks between environmental changes has been performed to test the influence of the initial settings of local search with quantum or normally distributed particles. The results have shown that applying adaptive sampling width for normal distribution allows improving performance in cases of bad choice of population size or number of local search particles on every iteration.
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27 February 2023
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Hybrid methods, modeling and optimization, complex systems, mathematical models, data mining, computational intelligence
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Stanovov, V., Akhmedova, S., Vakhnin, A., Sopov, E., & Semenkin, E. (2023). Multi-Swarm PSO With Success-History Based Adaptive Local Search for Dynamic Environments. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 186-193). European Publisher. https://doi.org/10.15405/epct.23021.23