Micro-differential evolution with vectorized random mutation factor

One of the main disadvantages of population-based evolutionary algorithms (EAs) is their high computational cost due to the nature of evaluation, specially when the population size is large. The micro-algorithms employ a very small number of individuals, which can accelerate the convergence speed of...

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Bibliographic Details
Published in2014 IEEE Congress on Evolutionary Computation (CEC) pp. 2055 - 2062
Main Authors Salehinejad, Hojjat, Rahnamayan, Shahryar, Tizhoosh, Hamid R., Chen, Stephen Y.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2014
Subjects
Benchmark testing
Convergence
Educational institutions
Sociology
Statistics
Vectors
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Summary:One of the main disadvantages of population-based evolutionary algorithms (EAs) is their high computational cost due to the nature of evaluation, specially when the population size is large. The micro-algorithms employ a very small number of individuals, which can accelerate the convergence speed of algorithms dramatically, while it highly increases the stagnation risk. One approach to overcome the stagnation problem can be increasing the diversity of the population. To do so, a micro-differential evolution with vectorized random mutation factor (MDEVM) algorithm is proposed in this paper, which utilizes the small size population benefit while preventing stagnation through diversification of the population. The proposed algorithm is tested on the 28 benchmark functions provided at the IEEE congress on evolutionary computation 2013 (CEC-2013). Simulation results on the benchmark functions demonstrate that the proposed algorithm improves the convergence speed of its parent algorithm.
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2014.6900606