A Novel Hybrid Differential Evolution and Particle Swarm Optimization Algorithm for Binary CSPs

摘要：

Heuristic optimization is an efficient approach and robust. A novel hybrid algorithm DE-PSO is proposed in this paper, which combines differential evolution(DE) with the particle swarm optimization(PSO) algorithm. In order to balance of an individuals exploration and exploitation capability for different evolving phase, F and CR equal to two different selfadjusted nonlinear functions. DE adjusts the mutation rate F and the crossover rate CR adaptively, taking account of the different distribution of population. Updating particle not only by DE operators but also by mechanisms of PSO. DE-PSO maintains the diversity of population and improves the global convergence ability. It also improves the efficiency and success rate and avoids the premature convergence. Simulation and comparisons based on test-sets of CSPs demonstrate the effectiveness, efficiency and robustness of the proposed algorithm.

关键词： particle swarm optimization differential evolution unconstrained optimization CSPs

作者: Hongjie Fu

作者单位: College of computer science and technology Jilin Teachers Institute of Engineering and Technology Changchun, China

会议类型: 国际会议

会议名称: 2012 International Conference on Computer Science and Electronic Engineering(2012 IEEE计算机科学与电子工程国际会议 ICCSEE 2012)

会议地点: 杭州

会议语种:英文

页码: 545-549

在线出版日期: 2012-03-23（万方平台首次上网日期，不代表论文的发表时间）