Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (10): 2766-2770.DOI: 10.11772/j.issn.1001-9081.2015.10.2766

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Enhanced differential evolution algorithm with non-prior-knowledge DFP local search under Memetic framework

MA Zhenyuan1, YE Shujin2, LIN Zhiyong1, LIANG Yubin1, HUANG Han2   

  1. 1. School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou Guangdong 510665, China;
    2. School of Software Engineering, South China University of Technology, Guangzhou Guangdong 510006, China
  • Received:2015-06-01 Revised:2015-06-29 Online:2015-10-10 Published:2015-10-14


马震远1, 叶树锦2, 林智勇1, 梁钰彬1, 黄翰2   

  1. 1. 广东技术师范学院 计算机科学学院, 广州 510665;
    2. 华南理工大学 软件学院, 广州 510006
  • 通讯作者: 黄翰(1980-),男,广东汕头人,教授,博士,主要研究方向:进化计算,
  • 作者简介:马震远(1980-),男,山东青岛人,讲师,博士,CCF会员,主要研究方向:进化算法、路由算法;叶树锦(1989-),男,广东广州人,硕士研究生,主要研究方向:进化计算;林智勇(1977-),男,广东梅州人,教授,博士,主要研究方向:机器学习、智能计算;梁钰彬(1993-),男,广东湛江人,主要研究方向:进化计算。
  • 基金资助:

Abstract: In order to improve the performance of Differential Evolution (DE) algorithm and extend its adaptability for solving continuous optimization problems, an enhanced DE algorithm was proposed by using efficient local search under the Memetic framework. Specifically, based on the Davidon-Fletcher-Powell (DFP) method, an improved local search method named NDFP was put forward, which could speed up finding locally optimal solutions based on excellent individuals explored by the DE algorithm. Furthermore, a strategy on when and how to run the NDFP local search was also given, so as to strike a good balance between global search (i.e., DE) and local search (i.e., NDFP). The proposed strategy was also enhanced the adaptability of NDFP local search in the range of DE algorithm. To verify the efficiency of the proposed algorithm, extensive simulation experiments were conducted on up to 53 test functions from CEC2005 and CEC2013 Benchmarks. The experimental results show that, compared with DE/current-to-best/1, SaDE and EPSDE algorithms, the proposed algorithm can achieve better performance in terms of both precision and stability.

Key words: Memetic framework, Differential Evolution (DE), local search, Davidon-Fletcher-Powell (DFP) method, approximate grad

摘要: 为提高差分进化(DE)算法对性连续优化问题的求解能力、增强算法的适应性,提出了一种基于局部快速收敛算法的Memetic进化算法。改进了Davidon-Fletcher-Powell方法,得到了具有强搜索能力的局部搜索算法——NDFP。当进化过程中出现具有优秀特质的个体时,NDFP可以使该个体沿着局部最优解的方向快速进化。为综合NDFP和DE的优势,提出局部搜索的执行策略来平衡全局搜索和局部搜索的关系,使得NDFP对DE的优化具有更为广泛的适应性。在CEC2005和CEC2013 Benchmark的53个测试函数上的实验结果表明,同DE/current-to-best/1、SaDE和EPSDE算法相比,NDFP-DE进化算法具有更高的求解精度和稳定性。

关键词: Memetic框架, 差分进化, 局部搜索, DFP方法, 近似梯度

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