摘要
提出一种基于遗传交叉因子的改进粒子群优化算法,通过自适应变化惯性权重来改善算法的收敛性能,借鉴遗传算法中的选择交叉操作增加粒子多样性,通过引入交叉因子增强群体粒子的优良特性,减小了算法陷入局部极值的可能。对几个典型的测试函数进行仿真表明,该算法较标准粒子群优化算法(PSO)提高了全局搜索能力和收敛速度,改善了优化性能。
An improved Particle Swarm Optimization（PSO） based on genetic hybrid gene is presented. In the new arithmetic, the inertial weight is adaptively adjusted to improve the convergence speed. The particles are mulriple by the selection and hybridization of genetic arithmetic. The import of hybrid genes improves excellent performance of particles and reduces likelihood on getting into local optimization. Experimental results show that the new algorithm can greatly improve the global convergence ability and enhance the rate of convergence.
出处
《计算机工程》
CAS
CSCD
北大核心
2008年第2期181-183,共3页
Computer Engineering
关键词
粒子群优化算法
交叉因子
演化计算
适应度
遗传算法
Particle Swarm Optimization（PSO）
hybrid genes
evolutionary computation
adaptive degree
genetic arithmetic
