A general framework of multipopulation methods with clustering in undetectable dynamic environments

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dc.contributor.author Li, C. en
dc.contributor.author Yang, Shengxiang en
dc.date.accessioned 2012-08-20T08:43:12Z
dc.date.available 2012-08-20T08:43:12Z
dc.date.issued 2012-08
dc.identifier.citation Li, 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 556-577 en
dc.identifier.issn 1089-778X
dc.identifier.uri http://hdl.handle.net/2086/6848
dc.description.abstract To 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.iso en en
dc.subject clustering en
dc.subject differential evolution en
dc.subject dynamic optimization problem en
dc.subject genetic algorithm en
dc.subject multiple population methods en
dc.subject particle swarm optimization undetectable dynamism en
dc.title A general framework of multipopulation methods with clustering in undetectable dynamic environments en
dc.type Article en
dc.identifier.doi http://dx.doi.org/10.1109/TEVC.2011.2169966
dc.researchgroup Centre for Computational Intelligence en
dc.ref2014.selected 1367395509_9911340001952_11_3

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