Opposition-based differential evolution
Opposition-based differential evolution
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DOI：
10.1109/tevc.2007.894200
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发表时间：
2008-02-01
影响因子：
14.3
通讯作者：
Salama, Magdy M. A.
中科院分区：
文献类型：
--
作者：
Rahnamayan, Shahryar;Tizhoosh, Hamid R.;Salama, Magdy M. A.
进化算法（EAS）是处理非线性和复杂问题的众所周知的优化方法。但是，由于进化过程的缓慢性质，这些基于人群的算法在计算上昂贵。本文提出了一种新型算法，以加速差异进化（DE）。拟议的基于反对的DE（ODE）采用基于反对的学习（OBL）来进行人口初始化以及发电。在这项工作中，已经利用相反的数字来提高DE的收敛速率。一组全面的58个复杂的基准函数，包括广泛的维度进行实验验证。还研究了维数，人口规模，跳跃率和各种突变策略的影响。另外，相反数字的贡献得到了经验验证。我们还提供了ODE与模糊自适应DE（vade）的比较。实验结果证实，在收敛速度和溶液的准确性方面，ODE优于原始DE和褪色。
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.