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Improved RM-MEDA with local learning

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Abstract

In this paper, local learning is proposed to improve the speed and the accuracy of convergence performance of regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA), a typical multi-objective optimization algorithm via estimation of distribution. RM-MEDA employs a model-based method to generate new solutions, however, this method is easy to generate poor solutions when the population has no obvious regularity. To overcome this drawback, our proposed method add a new solution generation strategy, local learning, to the original RM-MEDA. Local learning produces solutions by sampling some solutions from the neighborhood of elitist solutions in the parent population. As it is easy to search some promising solutions in the neighborhood of an elitist solution, local learning can get some useful solutions which help the population attain a fast and accurate convergence. The experimental results on a set of test instances with variable linkages show that the implement of local learning can accelerate convergence speed and add a more accurate convergence to the Pareto optimal.

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Acknowledgments

This work was supported by the Program for New Century Excellent Talents in University (No. NCET-12-0920), the National Natural Science Foundation of China (Nos. 61272279, 61001202 and 61203303), the Fundamental Research Funds for the Central Universities (Nos. K5051302049, K5051302023, K5051302002 and K5051302028), the Provincial Natural Science Foundation of Shaanxi of China (No. 2011JQ8020) and the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (No. B07048).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an, 710071, China

    Yangyang Li, Xia Xu, Peidao Li & Licheng Jiao

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  1. Yangyang Li
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  2. Xia Xu
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  3. Peidao Li
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  4. Licheng Jiao
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Correspondence to Yangyang Li.

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Communicated by Y.-S. Ong.

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Li, Y., Xu, X., Li, P. et al. Improved RM-MEDA with local learning. Soft Comput 18, 1383–1397 (2014). https://doi.org/10.1007/s00500-013-1151-2

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