An adaptive fuzzy penalty method for constrained evolutionary optimization

DOI10.1016/j.ins.2021.03.055OpenAlexW3146654090MaRDI QIDQ6092068

Han-Xiong Li, Yun Feng, Bing-Chuan Wang, Wenjing Shen

Publication date: 23 November 2023

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2021.03.055

zbMATH Keywords

constrained optimizationpopulation levelindividual leveladaptive fuzzy penaltymutation scheme

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Fuzzy and other nonstochastic uncertainty mathematical programming (90C70)

Cites Work

• Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: A survey of the state-of-the-art
• Genetic algorithms in constrained optimization
• Differential evolution with adaptive trial vector generation strategy and cluster-replacement-based feasibility rule for constrained optimization
• Solving nonlinear constrained optimization problems: an immune evolutionary based two-phase approach
• Minimum penalty for constrained evolutionary optimization
• Individual-dependent feasibility rule for constrained differential evolution
• Exploration and exploitation in evolutionary algorithms

This page was built for publication: An adaptive fuzzy penalty method for constrained evolutionary optimization