A three-strategy based differential evolution algorithm for constrained optimization

Authors:
Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam
Affiliations:
School of Engineering and Information Technology, University of New South Wales at ADFA, Canberra, Australia;School of Engineering and Information Technology, University of New South Wales at ADFA, Canberra, Australia;School of Engineering and Information Technology, University of New South Wales at ADFA, Canberra, Australia
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Year:
2010

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Abstract

Constrained Optimization is one of the most active research areas in the computer science, operation research and optimization fields. The Differential Evolution (DE) algorithm is widely used for solving continuous optimization problems. However, no single DE algorithm performs consistently over a range of Constrained Optimization Problems (COPs). In this research, we propose a Self-Adaptive Operator Mix Differential Evolution algorithm, indicated as SAOMDE, for solving a variety of COPs. SAOMDE utilizes the strengths of three well-known DE variants through an adaptive learning process. SAOMDE is tested by solving 13 test problems. The results showed that SAOMDE is not only superior to three single mutation based DE, but also better than the state-of-the-art algorithms.