基于目标相似性驱动与双端变量引导搜索的大规模多目标进化算法

doi:10.11896/jsjkx.250200091

Abstract

Abstract: Large-scale multi-objective optimization problems (LSMOPs) involve a large number of decision variables,resulting in expansive search spaces that make them challenging for traditional evolutionary algorithms to find good solutions efficiently within limited resources.To address this,a large-scale multi-objective evolutionary algorithm based on objective similarity and dual-end variable guided search (LMOEA/OS-DES) is proposed.LMOEA/OS-DES includes three strategies.The first strategy is the co-evolution of multiple swarms driven by objective similarity in order to quickly obtain solutions that reflects the distribution characteristics of Pareto optimal solution set.The second strategy is to design various variable grouping schemes based on the distribution characteristics of elite solutions in the decision space,so as to adapt to the differences between the distribution of optimal solutions for different objective vector directions.Combined with the grouping schemes,the dual-end variable guided search generates new solutions with distribution characteristics similar to the previous elite solutions,which enables it to adopt larger variations than the previous strategy,explore more regions faster,and accelerate the optimization of convergence and diversity.In the final strategy,it uses the competitive swarm optimization to explore the regions around elite solutions,so as to rapidly optimize diversity.Comparative experiments with eight other competitive algorithms on LSMOP and UF with dimensions ranging from 100 to 5 000 demonstrate that LMOEA/OS-DES has strong advantages.

Key words: Evolutionary algorithm, Large-scale multi-objective optimization, Multi-swarm optimization, Problem transformation, Competitive swarm optimization

CLC Number:

TP301

YANG Changhao, QIN Jin, WANG Hao. Large-scale Multi-objective Evolutionary Algorithm Based on Objective Similarity and Dual-EndVariable Guided Search[J].Computer Science, 2026, 53(3): 351-365.

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Large-scale Multi-objective Evolutionary Algorithm Based on Objective Similarity and Dual-EndVariable Guided Search

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