An evolutionary algorithm based on fully connected weight networks for mixed-variable multi-objective optimization-Reference-Cited by-同舟云学术

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An evolutionary algorithm based on fully connected weight networks for mixed-variable multi-objective optimization

Published:2024-02 Issue: Volume:659 Page:120053
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Dong Nanjiang,Zhang Tao^ORCID,Wang Rui^ORCID,Liao Xiangke,Wang Ling

Publisher

Elsevier BV

Reference45 articles.

1. An efficient multi-objective model and algorithm for sizing a stand-alone hybrid renewable energy system;Wang;Energy,2017

2. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II;Deb,2000

3. Indicator-based selection in multiobjective search;Zitzler,2004

4. MOEA/D: a multiobjective evolutionary algorithm based on decomposition;Zhang;IEEE Trans. Evol. Comput.,2007

5. Self-adaptive simulated binary crossover for real-parameter optimization;Deb,2007

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