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A multi-population co-evolutionary algorithm based on dual-space division for dynamic multi-objective optimization problems
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DOI：10.1016/j.ins.2025.122712.png)
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Effective optimization algorithms are urgently needed for dynamic multi-objective optimization problems (DMOPs) to efficiently track the Pareto optimal front (POF) or Pareto optimal set (POS) in response to environmental changes. This paper proposes a multi-population co-evolutionary algorithm based on dual-space division (MPDS) for solving DMOPs, which aims to achieve rapid convergence while maintaining population diversity. The algorithm divides the subpopulations within both the objective space and the decision space, integrating information from both spaces to more accurately predict the updated positions of the new population after environmental changes. The algorithm consists of three essential steps: first, it divides the subpopulations within both the objective space and the decision space after detecting an environmental change. Second, new individuals adapted to the updated POF are generated based on the evolutionary directions of the center points in each subpopulation, promoting rapid population convergence. Meanwhile, to prevent the population from getting trapped in local optima, new individuals are randomly generated within a certain region of the subpopulation to enhance diversity. Third, the original population is combined with the two populations generated in the previous steps. Finally, the solutions with good convergence and diversity are selected after performing a non-dominated sort on the combined solutions. Experiments demonstrate that our algorithm outperforms five state-of-the-art algorithms in terms of balancing convergence and diversity.
Keyword:
Dynamic multi-objective optimization
Subpopulation division
Multi-population co-evolutionary
Prediction
Multi-population diversity maintenance
期刊
IF:
6.8
论文数:
553
被引数:
6.2W
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