A survey of swarm intelligence for dynamic optimization: Algorithms and applications
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 unde...
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| Veröffentlicht in: | Swarm and evolutionary computation Jg. 33; S. 1 - 17 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.04.2017
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| Schlagworte: | |
| ISSN: | 2210-6502 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | 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.
•Existing dynamic optimisation surveys focus entirely on evolutionary algorithms and little on swarm intelligence algorithms. This survey provides a comprehensive survey dedicated to swarm intelligence algorithms to fill in the gap in the dynamic optimisation domain.•In addition to the mainstream ant colony optimisation and particle swarm optimisation algorithms; recent swarm intelligence applications to dynamic optimisation problems (DOPs) are included.•Provides several classifications related to both the algorithmic components and the application problem. |
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| ISSN: | 2210-6502 |
| DOI: | 10.1016/j.swevo.2016.12.005 |