A survey of swarm intelligence for dynamic optimization: algorithms and applications

Mavrovouniotis, M. ORCID: 0000-0002-5281-4175, Li, C. and Yang, S., 2017. A survey of swarm intelligence for dynamic optimization: algorithms and applications. Swarm and Evolutionary Computation, 33, pp. 1-17. ISSN 2210-6502

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

Item Type: Journal article
Publication Title: Swarm and Evolutionary Computation
Creators: Mavrovouniotis, M., Li, C. and Yang, S.
Publisher: Elsevier
Date: April 2017
Volume: 33
ISSN: 2210-6502
S2210650216302541Publisher Item Identifier
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 18 Jan 2017 13:59
Last Modified: 09 Jun 2017 14:11
URI: https://irep.ntu.ac.uk/id/eprint/29816

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