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摘要：由于许多科学以及工程实践问题追求参数优化来寻得更好的优化效果，所以国内外学者研究了大量优化算法。其中粒子群优化算法（PSO）是一种得到广泛认可较好的算法。混合粒子群算法是在基本粒子群算法的基础上，借鉴其他一些智能优化算法的思想而形成的粒子群算法。遗传算法、模拟退火算法以及神经网络等智能算法，这些算法是目前应用比较广泛的智能算法，由于每种智能算法都有其特点，因此自然而然就有了结合各种智能算法的优点而形成的混合智能算法。57938
本文介绍了三种混合粒子群算法，第一种是基于遗传算法中的自然选择机制改进的粒子群算法，第二种是基于杂交机制而改进的粒子群算法，第三种是基于模拟退火算法的粒子群算法。并且通过仿真研究比较这三种混合粒子群算法的精度以及稳定性等方面比较优劣。
毕业论文关键词：混合粒子群算法；算法仿真；精度比较
Hybrid particle swarm optimization algorithm
Abstract: Since many scientific and engineering practice problems from either the pursuit of better-parameter optimization optimization results, so a large number of domestic and foreign scholars have studied the optimization algorithm.Wherein the particle swarm optimization (PSO) is a widely recognized good algorithm. Hybrid particle swarm algorithm is based on the PSO, drawing on a number of other intelligent optimization algorithm thought and the formation of particle swarm optimization.For example: genetic algorithms, simulated annealing algorithm and neural network intelligent algorithms that are currently used widely in intelligent algorithms, intelligent algorithms as each has its own characteristics, so naturally there is formed combining the advantages of a variety of intelligent algorithms hybrid intelligent algorithm.
This article describes three hybrid particle swarm algorithm, the first genetic algorithm mechanism of natural selection improved particle swarm algorithm based on the second mechanism is based on the hybridization improved particle swarm algorithm, and the third is based on simulated annealing algorithm particle swarm optimization. Through simulation and comparative study of the three hybrid particle swarm algorithm accuracy and stability and other aspects of comparative advantages and disadvantages.
Keywords: Hybrid Particle Swarm Optimization；Algorithm simulation；Accuracy Comparison
目录
混合粒子群算法 2
Hybrid particle swarm optimization algorithm 2
第一章 绪论 6
1.1 课题研究的背景及意义 6
1.2 国内外研究现状 8
1.3 水平和发展趋势 9
第二章 基于自然选择的算法 10
2.1 算法原理 10
2.2 算法步骤 10
2.3 算法的MATLAB实现 10
第三章 基于杂交的算法 10
3.1 算法原理 11
3.2 算法步骤 11
3.3 算法的MATLAB实现 11
第四章 基于模拟退火的算法 12
4.1 算法原理 12
4.2 算法步骤 13
4.3 算法的MATLAB实现 13
第五章 三种算法仿真分析及比较