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An Adaptive Unified Differential Evolution Algorithm for Global Optimization

Ji Qiang(jqiang***at***lbl.gov)
Chad Mitchell(chadmitchell***at***lbl.gov)

Abstract: In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strategies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appropriate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in comparison to several conventional differential evolution algorithms.

Keywords: differential evolution, evolutionary optimization

Category 1: Global Optimization

Category 2: Global Optimization (Stochastic Approaches )

Citation: LBNL Report Number: LBNL-6853E

Download: [PDF]

Entry Submitted: 02/19/2015
Entry Accepted: 03/01/2015
Entry Last Modified: 02/19/2015

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