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Parallel fractal decomposition based algorithm for big continuous optimization problems
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DOI：10.1016/j.jpdc.2018.06.002.png)
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Abstract
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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. (C) 2018 Elsevier Inc. All rights reserved.
Keywords:
Very-large-scale optimization
Metaheuristics
Geometric fractal decomposition
Local search
Continuous optimization
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