摘要
带有约束条件的非线性优化数学模型,采用传统仿生智能优化算法在迭代优化中易于早熟并陷入局部最优,难以找到其全局最优解。提出了一种基于混合蛙跳和粒子群融合的改进优化新算法,并给出了算法改进思路、数学模型及实现流程。该改进算法增强了粒子的多样性,在算法初期具有较高的全局搜索能力,在后期具有很高的局部搜索能力,从而避免陷入局部最优。理论分析及仿真对比均表明,所提改进算法具有比经典算法更好的优化性能。
Considering the constrained nonlinear optimization model,conventional bionical optimization algorithm is proved to be premature and falls into locally optimal during iteration.An improved optimization algorithm using hybrid of leapfrog and particle swarm is proposed.The improvement theme,mathematical model and the implementation details of the algorithm are presented.The improved algorithm enhances the particle diversity,in the early stage of the algorithm it has a high global search capabilities,and in the late has a high local search capabilities,so as to avoid falling into locally optimal.Both the theoretical analysis and the simulation comparision show that the improved algorithm outperforms the classical algorithm in optimization performance.
作者
周林
陶冠宏
王佩
ZHOU Lin;TAO Guan-hong;WANG Pei(Science and Technology on Electronic Information Control Laboratory,Chengdu 610036,China;Patent Examination Cooperation Sichuan Center of the Patent Office,Chengdu 610036,China)
出处
《电子信息对抗技术》
2019年第6期27-31,共5页
Electronic Information Warfare Technology
作者简介
周林(1987-),博士,工程师;陶冠宏(1986-),博士,工程师;王佩(1990-),硕士,审查员。
