Using Parallel Strategies to Speed Up Pareto Local Search

Abstract : Pareto Local Search (PLS) is a basic building block in many state-of-the-art multiobjective combinatorial optimization algorithms. However, the basic PLS requires a long time to find high-quality solutions. In this paper, we propose and investigate several parallel strategies to speed up PLS. These strategies are based on a parallel multi-search framework. In our experiments, we investigate the performances of different parallel variants of PLS on the multiobjective unconstrained binary quadratic programming problem. Each PLS variant is a combination of the proposed parallel strategies. The experimental results show that the proposed approaches can significantly speed up PLS while maintaining about the same solution quality. In addition, we introduce a new way to visualize the search process of PLS on two-objective problems, which is helpful to understand the behaviors of PLS algorithms.
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Communication dans un congrès
11th International Conference on Simulated Evolution and Learning (SEAL 2017), Nov 2017, Shenzhen, China. Lecture Notes in Computer Science. 〈http://www.seal2017.com〉
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Soumis le : lundi 4 septembre 2017 - 14:45:43
Dernière modification le : vendredi 22 mars 2019 - 01:34:08

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  • HAL Id : hal-01581257, version 1

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Jialong Shi, Qingfu Zhang, Bilel Derbel, Arnaud Liefooghe, Sébastien Verel. Using Parallel Strategies to Speed Up Pareto Local Search. 11th International Conference on Simulated Evolution and Learning (SEAL 2017), Nov 2017, Shenzhen, China. Lecture Notes in Computer Science. 〈http://www.seal2017.com〉. 〈hal-01581257〉

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