Multimodal estimation of distribution algorithms

Yang, Q., Chen, W.-N., Li, Y. , Chen, C. L. P., Xu, X.-M. and Zhang, J. (2017) Multimodal estimation of distribution algorithms. IEEE Transactions on Cybernetics, 47(3), pp. 636-650. (doi: 10.1109/TCYB.2016.2523000) (PMID:26890945)

Abstract

Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed, which operate at the niche level. Then these two algorithms are equipped with three distinctive techniques: 1) a dynamic cluster sizing strategy; 2) an alternative utilization of Gaussian and Cauchy distributions to generate offspring; and 3) an adaptive local search. The dynamic cluster sizing affords a potential balance between exploration and exploitation and reduces the sensitivity to the cluster size in the niching methods. Taking advantages of Gaussian and Cauchy distributions, we generate the offspring at the niche level through alternatively using these two distributions. Such utilization can also potentially offer a balance between exploration and exploitation. Further, solution accuracy is enhanced through a new local search scheme probabilistically conducted around seeds of niches with probabilities determined self-adaptively according to fitness values of these seeds. Extensive experiments conducted on 20 benchmark multimodal problems confirm that both algorithms can achieve competitive performance compared with several state-of-the-art multimodal algorithms, which is supported by nonparametric tests. Especially, the proposed algorithms are very promising for complex problems with many local optima.

• Item Type: Articles
• Additional Information: This work was supported in part by the National Natural Science Foundation of China Project under Grant 61379061, Grant 61332002, and Grant 61511130078, in part by the Natural Science Foundation of Guangdong for Distinguished Young Scholars under Grant 2015A030306024, in part by the Guangdong Special Support Program under Grant 2014TQ01X550, and in part by the Guangzhou Pearl River New Star of Science and Technology Project under Grant 201506010002.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Li, Professor Yun
• Authors: Yang, Q., Chen, W.-N., Li, Y., Chen, C. L. P., Xu, X.-M., and Zhang, J.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: IEEE Transactions on Cybernetics
• Publisher: IEEE
• ISSN: 2168-2267
• Published Online: 15 February 2016
• Copyright Holders: Copyright © 2016 IEEE
• First Published: First published in IEEE Transactions on Cybernetics 47(3): 636-650
• Publisher Policy: Reproduced in accordance with the publisher copyright policy