## MATLAB混合微粒群算法的性能仿真

Performance simulation of Hybrid Particle Swarm Optimization for MATLAB
ABSTRACT
Real life optimization problems are often so complex that finding the best solution becomes computationally infeasible. Therefore, an intelligent approach is to search for a good approximate solution consuming lesser computational resources. Several engineering problems contain multiple objectives that need to be addressed simultaneously. Many techniques have been proposed that imitate nature’s own ingenious ways to explore optimal solutions for both single and multi-objective
optimization problems. Earliest of the nature inspired techniques are genetic and other evolutionary heuristics that evoke Darwinian evolution principles.

This dissertation presents the persity instance study of swarm intelligence through discussing a typical realization mode-particle swarm optimization(PSO).he systematical study on optimization efficiency evaluation of swarm intelligence is addressed firstly．Based on the basal evaluation index system in the field of intelligent optimization，a kind of evaluation model used to evaluate synthetically the general optimization performance and population dynamics of particle swarm optimization is proposed．This evaluation model including optimum value dynamics，population aggregation dynamics，population persity dynamics，as well as convergence dynamics of population center is simulated and validated by function optimization problems．
Keywords:
Particle swarm optimization algorithm,Chaotic particles,Swarm intelligence

1    绪论    6
1.1    课题的目的和意义    6
1.2    算法的拓展与改进    7
1.2.1 研究背景    7
1.2.2 PSO算法的改进研究    7
1.3    发展趋势    9
1.4    本课题的基本内容    9
2    基本粒子群算法    12
2.1 粒子群算法概述    12
2.1.1 粒子群算法发展    12
2.1.2 粒子群算法简介    12
4.1.3 粒子群算法的特点    13
2.2 基本粒子群算法    14
2.3 粒子群算法的关键    17
2.3.1 粒子状态向量形式的确定    17
2.3.2 适应度函数的建立    17
2.3.3 粒子多样性的保证    18
2.3.4 粒子群算法的参数设置    18
3    混合粒子群算法    19 源￥自%六:维;论-文'网=www.lwfree.cn
3．1 PSO早熟收敛判断    19
3．2基于基因换位算子改进策略    20
3．3多适应值函数机制    20 MATLAB混合微粒群算法的性能仿真:http://www.lwfree.cn/tongxin/20190614/34588.html
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