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

dc.cclicenceCC-BY-NC-NDen
dc.contributor.authorMavrovouniotis, Michalisen
dc.contributor.authorLi, Changeen
dc.contributor.authorYang, Shengxiangen
dc.date.acceptance2016-12-31en
dc.date.accessioned2017-01-24T09:49:03Z
dc.date.available2017-01-24T09:49:03Z
dc.date.issued2017-01-11
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractSwarm 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, constraint, multi-objective and classification, 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.en
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.identifier.citationMavrovouniotis, M., Li, C. and Yang, S. (2017) A survey of swarm intelligence for dynamic optimization: Algorithms and applications. Swarm and Evolutionary Computation, 33, pp.1-17en
dc.identifier.doihttps://doi.org/10.1016/j.swevo.2016.12.005
dc.identifier.urihttp://hdl.handle.net/2086/13203
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectidEP/K001310/1en
dc.publisherElsevieren
dc.researchgroupCentre for Computational Intelligenceen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectSwarm intelligenceen
dc.subjectDynamic optimizationen
dc.subjectAnt colony optimizationen
dc.subjectParticle swarm optimizationen
dc.titleA survey of swarm intelligence for dynamic optimization: Algorithms and applicationsen
dc.typeArticleen

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