A feasible-ratio control technique for constrained optimization

DOI10.1016/j.ins.2019.06.030zbMath1453.90165OpenAlexW2950241207WikidataQ127735103 ScholiaQ127735103MaRDI QIDQ2224871

Sanyou Zeng, Changhe Li, Ruwang Jiao

Publication date: 4 February 2021

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

Full work available at URL: https://doi.org/10.1016/j.ins.2019.06.030

zbMATH Keywords

constrained optimizationevolutionary computationconstraint-handlingconstrained multi-objective optimization

Mathematics Subject Classification ID

Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Multi-objective and goal programming (90C29) Nonlinear programming (90C30) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (5)

A multi-stage evolutionary algorithm for multi-objective optimization with complex constraints ⋮ An efficient constrained global optimization algorithm with a clustering-assisted multiobjective infill criterion using Gaussian process regression for expensive problems ⋮ Two-type weight adjustments in MOEA/d for highly constrained many-objective optimization ⋮ A constrained multiobjective differential evolution algorithm based on the fusion of two rankings ⋮ SMGO-\(\Delta\): balancing caution and reward in global optimization with black-box constraints

Cites Work

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• An efficient constraint handling method for genetic algorithms
• Differential evolution with adaptive trial vector generation strategy and cluster-replacement-based feasibility rule for constrained optimization
• On a feasible-infeasible two-population (FI-2Pop) genetic algorithm for constrained optimization: Distance tracing and no free lunch
• Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimization

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