Abstract
The multi-objective evolutionary algorithm based on decomposition (MOEA/D) has a remarkable overall performance for multi-objective optimization problems, but still consumes much time when solving complicated problems. A parallel MOEA/D (pMOEA/D) is proposed to solve bi-objective optimization problems on message-passing clusters more efficiently in this paper. The population is partitioned evenly over processors on a cluster by a partitioned island model. Besides, the sub-populations cooperate among separate processors on the cluster by the hybrid migration of both elitist individuals and utopian points. Experimental results on five bi-objective benchmark problems demonstrate that pMOEA/D achieves the satisfactory overall performance in terms of both speedup and quality of solutions on message-passing clusters.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No.61203310, 61503087), the Fundamental Research Funds for the Central Universities, SCUT (No.2013ZZ0048), the Pearl River S&T Nova Program of Guang-zhou (No.2014J2200052), the Natural Science Foundation of Guangdong Pro-vince, China (No.2015A030313204) and the China Scholarship Council (CSC) (No.201406155076, 201408440193).
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Xie, Y., Ying, W., Wu, Y., Wu, B., Chen, S., He, W. (2016). Partitioned Parallelization of MOEA/D for Bi-objective Optimization on Clusters. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_39
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DOI: https://doi.org/10.1007/978-981-10-0356-1_39
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