Aug 2026· Cluster Computing· Vol 29· 0 citations· 52 references
TL;DR
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 the algorithm’s ability to explore potential feasible solution regions.
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.
Experimental results demonstrate that the proposed hybrid strategy effectively overcomes the individual limitations of CMA-ES and GWO, making HCG a reliable method for solving complex continuous optimization problems.
Elias Ahmad Ahmadi, Besmillah Danish, Ahmad Ramin Rahnaward· Journal of Mathematics and S...· 0 citations
Dynamic multi-objective optimization with a variable number of objectives is difficult because objectivedimensional variations may significantly change the Pareto front and degrade algorithm adaptability. This paper proposes an unbounded archive-based transfer strategy (UATS), which maintains an unbounded archive of of...
Zhi-Yun Xiao, Ke Shang, Ya-Jun Liu et al.· 2026 8th International Confe...· 0 citations
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
Experimental results show that MOAEO achieves competitive convergence and diversity across multiple benchmark suites, and highlights MOAEO’s potential as an effective approach for solving complex multi-objective optimization problems.
A dual-population MOEA with Exploration and Exploitation Decoupled (MOEA/EED) is proposed: the exploration population uses a simple aging mechanism, while the exploitation population preserves the currently optimal solutions.
Cheng-Lin Jiang, Sheng-Jie Ren, Zi-Min Liang et al.· Proceedings of the Thirty-Fi...· 3 citations
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