Differential evolution algorithm: recent advances

Authors:
Ponnuthurai Nagaratnam Suganthan
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Venue:
TPNC'12 Proceedings of the First international conference on Theory and Practice of Natural Computing
Year:
2012

Citing 16
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Abstract

Differential Evolution (DE) has been a competitive stochastic realparameter optimization algorithm since it was introduced in 1995. DE possesses computational steps similar to a standard Evolutionary Algorithm (EA). DE perturbs the population members with the scaled differences of distinct population members. Hence, a step-size parameter used in algorithms such as evolutionary programming and evolution strategy is not required to be specified. Due to its consistent robust performance, DE has drawn the attention of many researchers all over the world. This article presents a brief review of the recent DE-variants for bound constrained single objective, multi-objective and multimodal optimization problems. It also suggests potential applications of DE in remanufacturing.