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• W4298002684 abstract "In this paper, we propose a multi-operator differentia evolution variant that incorporates three diverse mutation strategies in MOEA/D. Instead of exploiting the local region, the proposed approach continues to search for optimal solutions in the entire objective space. It explicitly maintains diversity of the population by relying on the benefit of clustering. To promowe convergence, the solutions close to the ideal position, in the objective space are given preference in the evolutionary process. The core idea is to ensure diversity of the population by applying multiple mutation schemes and a faster convergence rate, giving preference to solutions based on their proximity to the ideal position in the MOEA/D paradigm. The performance of the proposed algorithm is evaluated by two popular test suites. The experimental results demonstrate that the proposed approach outperforms other MOEA/D algorithms." @default.
• W4298002684 created "2022-10-01" @default.
• W4298002684 creator A5039785446 @default.
• W4298002684 creator A5050464239 @default.
• W4298002684 date "2022-09-29" @default.
• W4298002684 modified "2023-09-26" @default.
• W4298002684 title "Multi-operator Differential Evolution with MOEA/D for Solving Multi-objective Optimization Problems" @default.
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• W4298002684 doi "https://doi.org/10.26636/jtit.2022.161822" @default.
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