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Convergence and diversity cooperative-based coevolutionary algorithm for many-objective optimization
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DOI：10.1007/s40747-026-02466-2.png)
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Abstract
En 中文
As the number of objectives of the optimization problem increases, traditional many-objective evolutionary optimization algorithms face great challenges when balancing convergence and diversity. This is due to the low selection pressure towards the Pareto front and the high proportion of non-dominated and dominance-resistant solutions in the population. Moreover, existing indicator-based many-objective algorithms often employ a single indicator to evaluate solutions, which makes the quality of solutions largely determined by how well the indicator is designed. To address the aforementioned issues, this paper employs a cooperative coevolutionary approach by dividing the decision variables into convergence-related and diversity-related decision variables, each optimized in its respective subpopulation with distinct evaluation indicators. The two subpopulations coevolve cooperatively to better balance convergence and diversity. The proposed algorithm is compared with eight MaOEAs on three benchmark problems of DTLZ, WFG, and MaF. Experimental results demonstrate that the proposed algorithm exhibits competitive performance in solving many-objective optimization problems.
Keywords:
Many-objective optimization
Coevolution
Indicator
Decision variable analyses
Multi-population
Journal
C
IF:
4.6
Papers:
235
Citations:
0
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