Opposition-based differential evolution

被引:1234
作者
Rahnamayan, Shahryar [1 ]
Tizhoosh, Hamid R. [1 ]
Salama, Magdy M. A. [2 ]
机构
[1] Univ Waterloo, Dept Syst Design Engn, Pattern Anal & Machine Intelligence Lab, Waterloo, ON N2L 3G1, Canada
[2] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada
关键词
differential evolution (DE); evolutionary algorithms; opposition-based learning; opposite numbers; optimiztion;
D O I
10.1109/TEVC.2007.894200
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:64 / 79
页数:16
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