[1]米永强,高岳林.求解约束优化问题的改进粒子群优化算法[J].江西师范大学学报(自然科学版),2015,(01):59-63.
 MI Yongqiang,GAO Yuelin.The Improved Particle Swarm Optimization Algorithm for Solving Constrained Optimization Problems[J].,2015,(01):59-63.
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求解约束优化问题的改进粒子群优化算法()
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《江西师范大学学报》(自然科学版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2015年01期
页码:
59-63
栏目:
出版日期:
2015-02-10

文章信息/Info

Title:
The Improved Particle Swarm Optimization Algorithm for Solving Constrained Optimization Problems
作者:
米永强;高岳林
1.宁夏大学数学计算机学院,宁夏 银川 750021; 2.北方民族大学信息与系统科学研究所,宁夏 银川 750021
Author(s):
MI YongqiangGAO Yuelin
关键词:
约束优化问题 粒子群优化 全局优化 罚函数 可行基规则
Keywords:
constrained optimization problems particle swarm optimization global optimization penalty function feasibility based rule
分类号:
TP 18
文献标志码:
A
摘要:
针对约束优化问题,提出了一种改进的粒子群优化算法.该算法利用罚函数法将约束优化问题处理为无约束优化问题,并利用可行基规则来更新个体极值和全局极值,使不可行的粒子尽快飞向可行域,显著提高了算法的全局搜索能力.在标准粒子群算法研究基础上,为了提高粒子群算法求解非线性复杂优化问题的性能,对速度方程和惯性权重做了改进.数值算例表明,该算法是求解约束优化问题的一种较为有效的全局优化算法.
Abstract:
For constrained optimization problems,an improved particle swarm optimization algorithm is proposed.The algorithm uses the penalty function method to handle the problem of constrained optimization to unconstrained optimization problems,and the feasibility based rule is used to update individual optimal and global extremum,it makes the infeasible particles to fly to the feasible region as soon as possible,the global search ability of the algorithm is improved significantly.On the basis of the particle swarm algorithm research,the velocity equation and inertia weight is improved so that to improve the performance of particle swarm optimization algorithm to solve the complicated and nonlinear optimization problem.Numerical experiments show that the proposed algorithm is a global optimization algorithm with higher efficiency for unconstrained optimization.

参考文献/References:

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备注/Memo

备注/Memo:
国家自然科学基金(60962006,11161001);国家民委科研课题(12BFZ005)
更新日期/Last Update: 1900-01-01