Author : Aparna Potbhare 1
Date of Publication :7th April 2016
Abstract: Differential evolution (DE) has been proven to be one of the most powerful global numerical optimization algorithms in the evolutionary algorithm family. The core operator of DE is the differential mutation operator. Generally, the parents in the mutation operator are randomly chosen from the current population. In nature, good species always contain good information, and hence, they have more chance to be utilized to guide other species. Inspired by this phenomenon, To improve the efficiency of the original differential evolution algorithm, a new differential evolution algorithm was proposed. This paper presents a novel algorithm to accelerate the differential evolution (DE). The proposed opposition-based DE (ODE) employs opposition-based learning (OBL) for population generation jumping. In this work, opposite numbers have been utilized to improve the convergence rate of DE.
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