一种可逃逸的杂交粒子群算法  

Escapable Hybrid Particle Swarm Optimization

作　　者：李明[1] 曹德欣[1] 

机构地区：[1]中国矿业大学理学院,江苏徐州221116

出　　处：《微电子学与计算机》2012年第12期94-98,共5页Microelectronics & Computer

基　　金：国家自然科学基金(70901073);中央高校基本科研专项基金项目(JGK101676)

摘　　要：针对标准粒子群优化算法在优化复杂函数时容易早熟,收敛精度低等缺点,根据遗传学中优良个体之间杂交产生优良后代概率大的特性,提出一种改进方案,由于个体最优位置包含的有用信息多于粒子当前位置,在每一次迭代中,对所得到的个体最优位置进行交叉操作,以产生优良后代,并当粒子群陷入早熟收敛时,运用新的细菌觅食趋化操作使所有粒子在不破坏现有种群结构的情况下,逐步摆脱局部最优的束缚.将其应用于函数优化中,得到了较好的优化效果.The particle swarm optimization algorithm has a few disadvantages in solving complex functions, including low solving precisions and high possibilities of being trapped in local optimum. According to genetics good parent often produce hybrid good offspring, an improved program is proposed, as the personal best position of the particle contains more useful information than the current position, in each iteration, crossover is operated to the best personal particle, a better position may be got, and when the particle swarm trap premature convergence, without destroying the existing population structure by using new bacterial feeding chemotaxis and all particles gradually get rid of the shackles of local optimum. Numerical examples show the effectiveness of the proposed algorithm.

关 键 词：粒子群优化 个体最优 杂交 模拟退火 趋化 非线性方程组 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]