Adaptive multimodal continuous ant colony optimization

Yang, Q., Chen, W.-N., Yu, Z., Gu, T., Li, Y. , Zhang, H. and Zhang, J. (2017) Adaptive multimodal continuous ant colony optimization. IEEE Transactions on Evolutionary Computation, 21(2), pp. 191-205. (doi: 10.1109/TEVC.2016.2591064)

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Abstract

Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization algorithms in preserving high diversity, this paper intends to extend ant colony optimization algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ant colony optimization algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Li, Professor Yun
Authors: Yang, Q., Chen, W.-N., Yu, Z., Gu, T., Li, Y., Zhang, H., and Zhang, J.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Transactions on Evolutionary Computation
Publisher:IEEE
ISSN:1089-778X
ISSN (Online):1941-0026
Published Online:13 July 2016
Copyright Holders:Copyright © 2016 IEEE
First Published:First published in IEEE Transactions on Evolutionary Computation 21(2):191-205
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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