TY - JOUR
T1 - Opposition-based differential evolution
AU - Rahnamayan, Rahnamayan S.
AU - Tizhoosh, Hamid R.
AU - Salama, Magdy M.A.
N1 - Funding Information:
Manuscript received October 20, 2006; revised January 10, 2007. The work of S. Rahnamayan was supported by Ontario Graduate Scholarship (OGS). S. Rahnamayan is with the Pattern Analysis and Machine Intelligence Laboratory, University of Waterloo, Department of Systems Design Engineering, Waterloo, ON N2L 3G1, Canada (e-mail: shahryar@pami.uwaterloo.ca). H. R. Tizhoosh is with the Pattern Analysis and Machine Intelligence Laboratory, University of Waterloo, Department of Systems Design Engineering, Waterloo, ON N2L 3G1, Canada (e-mail: tizhoosh@uwaterloo.ca). M. M. A. Salama is with the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada (e-mail: m.salama@ece.uwaterloo.ca). Digital Object Identifier 10.1109/TEVC.2007.894200
PY - 2008/2
Y1 - 2008/2
N2 - Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. 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 initialization and also for generation jumping. In this work, opposite numbers have been utilized to improve the convergence rate of DE. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions is employed for experimental verification. The influence of dimensionality, population size, jumping rate, and various mutation strategies are also investigated. Additionally, the contribution of opposite numbers is empirically verified. We also provide a comparison of ODE to fuzzy adaptive DE (FADE). Experimental results confirm that the ODE outperforms the original DE and FADE in terms of convergence speed and solution accuracy.
AB - Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. 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 initialization and also for generation jumping. In this work, opposite numbers have been utilized to improve the convergence rate of DE. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions is employed for experimental verification. The influence of dimensionality, population size, jumping rate, and various mutation strategies are also investigated. Additionally, the contribution of opposite numbers is empirically verified. We also provide a comparison of ODE to fuzzy adaptive DE (FADE). Experimental results confirm that the ODE outperforms the original DE and FADE in terms of convergence speed and solution accuracy.
KW - Differential evolution (DE)
KW - Evolutionary algorithms
KW - Opposite numbers
KW - Opposition-based learning
KW - Optimiztion
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U2 - 10.1109/TEVC.2007.894200
DO - 10.1109/TEVC.2007.894200
M3 - Article
AN - SCOPUS:40249084706
SN - 1089-778X
VL - 12
SP - 64
EP - 79
JO - IEEE Transactions on Evolutionary Computation
JF - IEEE Transactions on Evolutionary Computation
IS - 1
ER -