Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary ComputationPublished 1 February 2008
Shahryar Rahnamayan, Hamid R. Tizhoosh, M.M.A. Salama
Citations1,602
SJR quartileQ1
SJR score3.50
SNIP3.12
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TL;DR
This paper presents a novel algorithm to accelerate the differential evolution (DE), which employs opposition-based learning (OBL) for population initialization and also for generation jumping and results confirm that the ODE outperforms the original DE and FADE in terms of convergence speed and solution accuracy.
Abstract
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.
Keywords
Computer Science
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