2015
DOI: 10.1016/j.neucom.2014.07.030
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A binary differential evolution algorithm learning from explored solutions

Abstract: Although real-coded differential evolution (DE) algorithms can perform well on continuous optimization problems (CoOPs), it is still a challenging task to design an efficient binary-coded DE algorithm. Inspired by the learning mechanism of particle swarm optimization (PSO) algorithms, we propose a binary learning differential evolution (BLDE) algorithm that can efficiently locate the global optimal solutions by learning from the last population. Then, we theoretically prove the global convergence of BLDE, and … Show more

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Cited by 69 publications
(28 citation statements)
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“…The algorithm successfully applied in model identification [4] , task scheduling [49] , mobile computing [30] , binary optimization [7] , just to mention a few.…”
Section: De Variantsmentioning
confidence: 99%
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“…The algorithm successfully applied in model identification [4] , task scheduling [49] , mobile computing [30] , binary optimization [7] , just to mention a few.…”
Section: De Variantsmentioning
confidence: 99%
“…The normDE normalize the real variables lying them into the interval [0, 1], and then the value below 0.5 is absorbed to zero, and obviously the values more than that are absorbed to one. The AMDE is efficient on low dimensional optimization problems however it suffers from slow convergence speed [7] .…”
Section: Binary Dementioning
confidence: 99%
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