A reference set based many-objective co-evolutionary algorithm with an application to the knapsack problem

DOI10.1016/j.ejor.2021.10.033zbMath1495.90184OpenAlexW3210020537MaRDI QIDQ2116840

H. Mert Sahinkoc, Ümit Bilge

Publication date: 18 March 2022

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2021.10.033

zbMATH Keywords

combinatorial optimizationmultiple objective programmingevolutionary computationsco-evolutionmany-objective

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Approximation methods and heuristics in mathematical programming (90C59) Combinatorial optimization (90C27)

Uses Software

• NBI
• MOEA/D
• HypE

Cites Work

• Preference-inspired co-evolutionary algorithms using weight vectors
• Local dominance and local recombination in MOEAs on \(0/1\) multiobjective knapsack problems
• A parallel multiple reference point approach for multi-objective optimization
• A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design
• Entropy based evolutionary algorithm with adaptive reference points for many-objective optimization problems
• Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems
• An empirical study on similarity-based mating for evolutionary multiobjective combinatorial optimization
• Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems
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