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Universal partially evolved parallelization of MOEA/D for multi-objective optimization on message-passing clusters

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Abstract

This paper presents a universal partially evolved parallelization of the multi-objective evolutionary algorithm based on decomposition (MOEA/D) for multi-objective optimization on message-passing clusters to reduce its computation time. The partially evolved MOEA/D (peMOEA/D) is suitable not only for the bi-objective space, but also for higher dimensional objective spaces by using a partially evolved island model. This model improves the algorithm universality and population diversity by keeping a subpopulation equal in size to the entire population, but only evolving a partial (equal to a partition size) subpopulation on each separate processor in a cluster. Furthermore, a nearest-neighbor partitioning approach, hybrid migration policy and adaptive neighbor-based topology are adopted in the peMOEA/D. The fat partitions generated by the nearest-neighbor partitioning can reduce migration traffic of elitist individuals across subpopulations. Then, hybrid migration of both elitist individuals and utopian points helps separate subpopulations to cooperate in guiding the search quickly towards a complete front. Next, the adaptive neighbor-based topology connects neighbor partitions only and achieves an excellent balance between convergence speed and migration traffic. Experimental results on benchmark multi-objective optimization problems with two or more objectives demonstrate the satisfactory overall performance of the peMOEA/D in terms of both convergence performance and speedup on message-passing clusters.

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Acknowledgments

This work was supported in part by the Natural Science Foundation of Guangdong Province, China (No. 2015A030313204), the Pearl River Science & Technology Nova Program of Guangzhou (No. 2014J2200052), the National Natural Science Foundation of China (Nos. 61203310 and 61503087), the China Scholarship Council (CSC) (Nos. 201406155076 and 201408440193) and the Fundamental Research Funds for the Central Universities, SCUT (No. 2013ZZ0048).

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Authors and Affiliations

  1. School of Software Engineering, South China University of Technology, Guangzhou, 510006, China

    Weiqin Ying, Yuehong Xie, Bingshen Wu, Shiyun Chen & Weipeng He

  2. School of Computer Science and Educational Software, Guangzhou University, Guangzhou, 510006, China

    Yu Wu

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  1. Weiqin Ying
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  2. Yuehong Xie
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  4. Bingshen Wu
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Correspondence to Weiqin Ying.

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Communicated by V. Loia.

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Ying, W., Xie, Y., Wu, Y. et al. Universal partially evolved parallelization of MOEA/D for multi-objective optimization on message-passing clusters. Soft Comput 21, 5399–5412 (2017). https://doi.org/10.1007/s00500-016-2125-y

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