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A constrained multi-objective evolutionary algorithm based on constraint-diversity dominance sorting
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AI
DOI：10.1016/j.asoc.2026.115386.png)
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Abstract
En 中文
• A novel sorting method that considers both feasibility and diversity is proposed. In this approach, the dominance relationship is categorized into weak dominance and strong dominance. By comparing each pair of solutions, individuals can be quantitatively evaluated and selected based on their penalty values. This method effectively eliminates unpromising feasible solutions while preserving promising infeasible ones by simultaneously considering feasibility, convergence, and diversity. • A strategy for setting the mutation distribution index of the evolutionary operator is proposed. For the update of the constraint-free population, a random mutation distribution index is employed to expand the mutation range in the decision space, thereby enhancing the algorithm’s exploration capability. • Based on the above contributions, a novel constrained multiobjective evolutionary algorithm based on constraint-diversity dominance sorting, termed CMOEA-CDS, is proposed. This algorithm is built upon a coevolutionary framework, with innovations focusing on the application of constraint-diversity dominance sorting and the setting of the mutation distribution index in constraint-free optimization.
Keywords:
constraint-diversity dominance sorting
multi-objective evolutionary algorithm
feasibility
diversity
mutation distribution index
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
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