Aug 2026· Cluster Computing· Vol 29· 0 citations· 47 references
Computer Science
TL;DR
A dynamic two-population co-evolutionary algorithm (CHEA), which balances feasibility, convergence and diversity at different stages by dynamically adjusting the number of offspring of the two populations by dynamically adjusting the number of offspring of the two populations.
A novel evolutionary algorithm termed CMOEA-TSD is proposed, which adopts a three-population, two-stage co-evolutionary framework to enhance diversity and coordinate multiple search behaviors and significantly outperforms seven state-of-the-art algorithms.
A reference vector-based diversity-enhancing strategy in the environmental selection of the diversity-enhancing auxiliary population is proposed for the main population, which assists the population in progressively traversing vast infeasible regions by dynamically activating constraints step by step.
The proposed Multi-Objective Narwhal Optimizer (MONO), a Pareto-based extension of the recently developed Narwhal Optimizer, incorporates Pareto dominance, external archive management, adaptive multi-leader guidance, and crowding-distance-based diversity preservation to effectively balance convergence and exploration t...
S. Medjahed, Mourad Bouatouche, Fatima Boukhatem· Journal of Supercomputing· 0 citations
An information-assisted solution generation based dual population constrained multi-objective evolutionary algorithm, called GSCMO, which dynamically optimizes the mutation strength, focusing on enhancing solution diversity and maintaining extensive exploration of the population in the decision space, thereby improving...
Jun-Hua Liu, Lei Fan, Wei Zhang et al.· Cluster Computing· 0 citations
Results show that integrating local search significantly enhances performance, while a principled method for setting hybrid parameters ensures robustness and reproducibility, highlighting the potential of combining mathematical programming techniques with evolutionary algorithms for high-dimensional many-objective opti...
Regina C. L. C. de Sousa, Dênis E. C. Vargas, Elizabeth F. Wanner et al.· Journal of Heuristics· 0 citations
TSEA-SR is proposed, a two-stage MaOEA with survival-ratio-based switching, in which the stage transition is determined by offspring survival after environmental selection, and the results demonstrate the competitive performance of TSEA-SR across the evaluated problems.
Wei Li, Si-En Ouyang, N. Yang et al.· International journal of sof...· 0 citations
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