摘要
针对粒子群优化算法易早熟和求解精度差等问题,提出一种融合局部搜索与二次插值的粒子群优化算法。首先由标准粒子群优化算法产生N个位置,从这N个位置中随机选取3个不同位置,进行二次插值操作产生每个粒子的新位置,更新每个粒子的历史最好位置的全局最好位置;然后经过一定迭代步后,利用Hooke-Jeeves局部搜索技术,对得到的当前全局最优位置进行局部搜索;最后,对9个典型测试函数进行仿真实验并与其它算法进行比较,数值结果表明所提出的算法具有较快的收敛速度和较强的全局搜索能力。
To the problems of premature convergence frequently appeared in Particle Swarm Optimization（PSO）algo- rithm and its poor convergence accuracy, a particle swarm optimization algorithm combining local search and quadratic interpolation was proposed. Firstly, we randomly chose three positions from the N positions which are generated by standard Particle Swarm Optimization algorithm, and the new position was generated by using quadratic interpolation operator for each particle, and the previous best position of each particle and the global best position of swarm were up- dated. Then after some iteration steps, the Hooke-Jeeves local search technique optimized the global best position of the swarm found so far. Finally, simulation experiment on a set of 9 benchmark functions was given, and the comparisons with other algorithms were provided. The numerical results show that the proposed algorithm has a fast convergence speed and good global search capability.
出处
《计算机科学》
CSCD
北大核心
2013年第9期204-207,共4页
Computer Science
基金
国家自然科学基金项目(10871033)
辽宁省自然科学基金项目(20102003)资助
关键词
粒子群优化
二次插值
局部搜索
全局优化
Particle swarm optimization, Quadratic interpolation, Local search, Global optimization
