摘要
粒子群优化算法规则简单,收敛速度较快,但易陷入局部最优值,在噪声问题中也显示出较差的寻优能力.针对算法存在的不足,本文结合反向学习机制较快的学习速度及优化能力,并在算法进化过程中引入交叉因子,提出一种新的改进的反向粒子群算法(COPSO).实验表明,该算法在噪声问题中要优于反向粒子群算法.
Particle swarm optimization was proved to have the fast convergence and simple regulatioza. However itwas easily trapped into the local optima and shown the poor ability in the noisy problems. An improved algorithm is presented in this paper. Opposition-based learning which accelerate the learning and searching process and corm-factor are employed in the algorithm. Experimental results on the test functions are shown that the algorithm is superior to OPSO in the noise problems.
出处
《西安工程大学学报》
CAS
2011年第5期721-725,共5页
Journal of Xi’an Polytechnic University
基金
陕西省教育厅自然科学基金项目(2010JK563)
关键词
粒子群算法
反向学习算法
交叉因子
噪声
particle swarm optimization
opposition-based learning
corro-factor
noise
