摘要
针对复杂约束优化问题,提出一种改进的粒子群方法。该粒子群算法对于不满足约束条件的粒子实行全概率接收,但令其目标函数值同为一个很小的常数,以保持粒子的多样性并使最优解在可行域内。另外,在PSO算法的基础上,使惯性权值按对数规律单调递减,同时引进选择遗传算子,以增强其全局寻优性能。数值实验表明:与PSO算法和一些其它优化算法相比,改进算法具有较强的寻优能力和寻优效率。工程应用表明,改进算法具有一定的优越性。
An improved particle swarm optimization（PSO） algorithm is developed to solve complicated constrained optimization problems.This algorithm accepts all the particles that do not satisfy the constrained conditions and made their objective function values a very small constant to keep the diversity of the particles,and helps the optimum solution in feasible region.In addition,based on the PSO algorithm,the inertial weight is reduced by the logarithm regular.At the same time,a selected genetic operator is used to improve the optimization capability of the PSO algorithm.Several numerical tests indicate that the improved PSO algorithm has better optimization capability and efficiency than the PSO algorithm and some other optimization algorithms.Applied to engineering practice indicates that the improved algorithm has some superiority.
出处
《工程热物理学报》
EI
CAS
CSCD
北大核心
2010年第1期32-35,共4页
Journal of Engineering Thermophysics
关键词
约束优化问题
粒子群算法
选择遗传
惯性权值
寻优能力
constrained optimization problems
particle swarm optimization algorithm
selected genetic
inertial weight
optimization capability
