Modified estimation of Distribution algorithm with differential mutation for constrained optimization

Estimation of Distribution algorithms (EDAs) are probabilistic-model based optimization techniques that exploit promising solution candidates by developing particles around them in accordance to a pre-specified distribution. This paper attempts to approach constrained optimization problems by an interdependent parallel functioning of a modified Gaussian distribution based EDA with differential mutation on the lines of rand/1 perturbation scheme. A modified penalty function free from scaling parameters has been proposed to deal with the constraints associated. The results have been collected from functional landscapes defined by the CEC 2010 benchmark and have been compared with existing state-of-the-art methods for constrained optimization.

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