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Journal Articles Journal of Parallel and Distributed Computing Year : 2019

Parallel fractal decomposition based algorithm for big continuous optimization problems

Abstract

Fractal Decomposition Algorithm (FDA) is a metaheuristic that was recently proposed to solve high dimensional continuous optimization problems. This approach is based on a geometric fractal decomposition which divides the search space while looking for the optimal solution. While FDA and its fractal decomposition has shown to be an effective optimization algorithm, its running time grows significantly as the problems dimension increases. To overcome this expensive computational time, a parallelized version of FDA, called Parallel Fractal Decomposition Algorithm (PFDA) is proposed. The focus was on parallelizing the exploration and exploitation phases of the original algorithm on a multi-threaded environment. The performances of PFDA were evaluated on the same Benchmark used to illustrate FDA efficiency, the SOCO 2011. It is composed of 19 functions with dimensions going from 50 to 5000. Results show that PFDA reaches similar performances as the original version with a significantly reduced computational time.
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Dates and versions

hal-02304882 , version 1 (20-07-2022)

Licence

Attribution - NonCommercial

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Amir Nakib, Léo Souquet, El-Ghazali Talbi. Parallel fractal decomposition based algorithm for big continuous optimization problems. Journal of Parallel and Distributed Computing, 2019, 133, pp.297-306. ⟨10.1016/j.jpdc.2018.06.002⟩. ⟨hal-02304882⟩
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