Improved Algorithms Based on the Simple Particle Swarm Optimization

摘要：

　　As one of the representative algorithms in swarm intelligence, particle swarm optimization has been applied to many fields because of its several merits, such as simple concept, easy realizing and fast convergence rate in the early evolutionary.However, it still has some disadvantages such as easy falling into the local extremum, slow convergence velocity and low convergence precision in the late evolutionary.Two new algorithms based on the simple particle swarm optimization are proposed to try to improve the precision of the algorithm in a certain error range of the length of time.The algorithms have been simulated and compared with the particle swarm optimization and the simple particle swarm optimization.The simulations show that the algorithms have a higher convergence precision for some functions or a particular issue.

关键词： Swarm Intelligence Particle Swarm Optimization Swarm Robots

作者: Lei Liu Xiaomeng Zhang Zhiguo Shi Tianyu Zhang

作者单位: School of Computer and Communication Engineering,University of Science and Technology Beijing,100083 Beijing, P.R.China

会议类型: 国际会议

会议名称: 4th international Conference,ICSI2013(第4届群体智能国际会议)

会议地点: 哈尔滨

会议语种:英文

页码: 96-103

在线出版日期: 2013-06-12（万方平台首次上网日期，不代表论文的发表时间）