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Journal Articles Journal of Parallel and Distributed Computing Year : 2019

A unified view of parallel multi-objective evolutionary algorithms

Abstract

This paper describes a unified view of parallel evolutionary algorithms for multi-objective optimization problems. The parallel optimization algorithms are detailed from both design and implementation aspects. The proposed taxonomy is based on three hierarchical parallel models. Moreover, various parallel architectures are taken into account. The performance assessment issue of parallel multi-objective evolutionary algorithms (MOEA) is also presented. This work can be extended to any population-based metaheuristics such as particle swarm and scatter search.
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Dates and versions

hal-02304734 , version 1 (21-12-2021)

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Attribution - NonCommercial

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El-Ghazali Talbi. A unified view of parallel multi-objective evolutionary algorithms. Journal of Parallel and Distributed Computing, 2019, 133, pp.349-358. ⟨10.1016/j.jpdc.2018.04.012⟩. ⟨hal-02304734⟩
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