Estimation of distribution algorithm combined with chaotic sequence for dynamic optimisation problems Online publication date: Tue, 21-Mar-2017
by Fahong Yu; Wenping Li; Jiang Tao; Kun Deng; Longhua Ma; Feng He
International Journal of Computing Science and Mathematics (IJCSM), Vol. 8, No. 1, 2017
Abstract: To track the optima in dynamic environments with estimation of distribution algorithm, maintenance of the diversity of the population is an essential requirement. Taking this point into consideration, this paper proposes an estimation of distribution algorithm combined with a chaotic sequence (CEDA) for dynamic optimisation problems. In CEDA, a chaotic sequence is introduced to maintain the diversity of population and enhance improve the local search ability. Many numerical experiments are reported in order to compare the performance of the CEDA with the self-adaptive approach by other authors. The numerical results show that the performance of our algorithm is superior to that of other published algorithms on two dynamic benchmark problems.
Online publication date: Tue, 21-Mar-2017
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