Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: International Workshop “Hybrid methods of modeling and optimization in complex systems” (HMMOCS 2022); Krasnoyarsk; Krasnoyarsk
Год издания: 2022
Идентификатор DOI: 10.15405/epct.23021.23
Ключевые слова: particle swarm optimization, dynamic optimization, local search, parameter adaptation
Аннотация: 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 sПоказать полностьюet 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.
Журнал: HYBRID METHODS OF MODELING AND OPTIMIZATION IN COMPLEX SYSTEMS
Номера страниц: 186-193
Место издания: London, United Kingdom
Издатель: European Proceedings