Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution

DOI10.1016/j.cor.2011.11.014zbMath1348.90635OpenAlexW2012452381MaRDI QIDQ336318

Antonio J. Nebro, Bernabé Dorronsoro, Pascal Bouvry, Grégoire Danoy

Publication date: 10 November 2016

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cor.2011.11.014

zbMATH Keywords

multi-objective optimizationparallel algorithmscooperative coevolutionary EAs

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Parallel algorithms in computer science (68W10)

Related Items (1)

Parallel computational optimization in operations research: a new integrative framework, literature review and research directions

Uses Software

• SPEA2
• jMetal
• JCell
• MOCell

Cites Work

• Unnamed Item
• Efficient batch job scheduling in grids using cellular memetic algorithms
• Cellular genetic algorithms
• Evolutionary multi-objective optimization in uncertain environments. Issues and algorithms
• MOCell: A cellular genetic algorithm for multiobjective optimization
• Evolutionary Algorithms for Solving Multi-Objective Problems

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