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dc.contributor.authorLi, C.en
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2012-08-20T08:43:12Z
dc.date.available2012-08-20T08:43:12Z
dc.date.issued2012-08
dc.identifier.citationLi, C. and Yang, S. (2012) A general framework of multipopulation methods with clustering in undetectable dynamic environments. IEEE Transactions on Evolutionary Computation, 16 (4), pp 556-577en
dc.identifier.issn1089-778X
dc.identifier.urihttp://hdl.handle.net/2086/6848
dc.description.abstractTo solve dynamic optimization problems, multiple population methods are used to enhance the population diversity for an algorithm with the aim of maintaining multiple populations in different subareas in the fitness landscape. Many experimental studies have shown that locating and tracking multiple relatively good optima rather than a single global optimum is an effective idea in dynamic environments. However, several challenges need to be addressed when multipopulation methods are applied, e.g., how to create multiple populations, how to maintain them in different subareas, and how to deal with the situation where changes cannot be detected or predicted. To address these issues, this paper investigates a hierarchical clustering method to locate and track multiple optima for dynamic optimization problems. To deal with undetectable dynamic environments, this paper applies the random immigrants method without change detection based on a mechanism that can automatically reduce redundant individuals in the search space throughout the run. These methods are implemented into several research areas, including particle swarm optimization, genetic algorithm, and differential evolution. An experimental study is conducted based on the moving peaks benchmark to test the performance with several other algorithms from the literature. The experimental results show the efficiency of the clustering method for locating and tracking multiple optima in comparison with other algorithms based on multipopulation methods on the moving peaks benchmark.en
dc.language.isoenen
dc.subjectclusteringen
dc.subjectdifferential evolutionen
dc.subjectdynamic optimization problemen
dc.subjectgenetic algorithmen
dc.subjectmultiple population methodsen
dc.subjectparticle swarm optimization undetectable dynamismen
dc.titleA general framework of multipopulation methods with clustering in undetectable dynamic environmentsen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1109/TEVC.2011.2169966
dc.researchgroupCentre for Computational Intelligenceen
dc.ref2014.selected1367395509_9911340001952_11_3
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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