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An Adaptive Multi-Population Framework for Locating and Tracking Multiple Optima

Li, C, Nguyen, TT, Yang, M, Mavrovouniotis, M and Yang, S (2015) An Adaptive Multi-Population Framework for Locating and Tracking Multiple Optima. IEEE Transactions on Evolutionary Computation, 20 (4). pp. 590-605. ISSN 1089-778X

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Multi-population methods are important tools to solve dynamic optimization problems. However, to effectively track multiple optima, algorithm designers need to address a key issue: adaptation of the number of populations. In this paper, an adaptive multi-population framework is proposed to address this issue. A database is designed to collect heuristic information of algorithm behavior changes. The number of populations is adaptively adjusted according to statistic information related to the current evolving status in the database as well as a heuristic value. Several other techniques are also introduced, including a heuristic clustering method, a probabilistic prediction scheme, a population hibernation rule, and a peak hiding method. The particle swarm optimization and differential evolution algorithms are implemented into the framework, respectively. A set of multipopulation based algorithms are chosen to compare with the proposed algorithms on the moving peaks benchmark using four different performance measures. The proposed algorithms are also compared with two peer algorithms on a set of multi-modal problems in static environments. Experimental results show that the proposed algorithms outperform the other algorithms in most scenarios.

Item Type: Article
Additional Information: (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Uncontrolled Keywords: 0801 Artificial Intelligence And Image Processing, 0906 Electrical And Electronic Engineering, 0806 Information Systems
Subjects: Q Science > QA Mathematics
T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions: Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Publisher: IEEE
Related URLs:
Date Deposited: 23 Nov 2015 08:40
Last Modified: 04 Sep 2021 13:47
DOI or ID number: 10.1109/TEVC.2015.2504383
URI: https://researchonline.ljmu.ac.uk/id/eprint/2371
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