Fusion prediction strategy-based dynamic multi-objective sparrow search algorithm

Solving dynamic multi-objective optimization problems with time-varying Pareto front (PF) or Pareto set (PS) is a challenging task. Such problems require algorithms to react to environmental changes and efficiently track optimal solutions. For this purpose, a dynamic multi-objective sparrow search a...

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Vydáno v:	Applied soft computing Ročník 165; s. 112071
Hlavní autoři:	Wu, Rui, Huang, Haisong, Wei, Jianan, Huang, Hefan, Wang, Shixin, Zhu, Yunwei, Han, Zhenggong, Gu, Qiang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.11.2024
Témata:	Dominance degree sorting Dynamic multi-objective optimization Fusion prediction strategy Kernel Extreme Learning Machine Sparrow Search Algorithm Fusion prediction strategy Dominance degree sorting Kernel Extreme Learning Machine Dynamic multi-objective optimization Sparrow Search Algorithm
ISSN:	1568-4946
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Popis
Shrnutí:	Solving dynamic multi-objective optimization problems with time-varying Pareto front (PF) or Pareto set (PS) is a challenging task. Such problems require algorithms to react to environmental changes and efficiently track optimal solutions. For this purpose, a dynamic multi-objective sparrow search algorithm (SSA) with fusion prediction strategy, based on difference model and kernel extreme learning machine (DMOSSA-FPS), is proposed. Given the diversity of change characteristics, a single prediction model is insufficient. Therefore, based on the historical information of the population, a difference model and a kernel extreme learning machine are integrated for PS prediction. The former is used to predict the solutions of some individuals under approximate linear changes and the latter is employed for nonlinear predictions. In a new environment, the combined predictions increase the diversity of the initial population. Additionally, a new static optimizer is proposed, which combines decomposition- and dominance-based approaches to constitute a new individual screening mechanism. Then the optimization mode of SSA is introduced to enhance both algorithmic diversity and convergence rate. The experimental results on the DF test suite demonstrate that, compared with several other advanced algorithms, DMOSSA-FPS exhibits stronger convergence and robustness. •A novel method to solve dynamic multi-objective problems is proposed (DMOSSA-FPS).•Designing a fusion prediction strategy to complete the dynamic response.•Dominance degree sorting (DDS) is introduced to speed up the sorting.•A new static optimizer based on dominance and decomposition is proposed (MOSSA/DD).
ISSN:	1568-4946
DOI:	10.1016/j.asoc.2024.112071