| 摘 要: 针对粒子群算法在进化后期收敛精度低、收敛速度慢，尤其是高维时候容易早熟等问题，提出了一种新的混合粒子群优化算法。新算法首先设计了一种新的惯性权重，使惯性权重取值在进化初期和后期都较为适中；其次，为了有效抑制粒子陷入局部极值，引入了粒子最优速度和最差适应值的概念，并以此为基础，设计了粒子的一种新的自适应变异方式；最后引入了平均收敛率和最小平均收敛代数两个概念，可以更好地评价和比较本文算法的性能。八个标准测试函数在100 维、200 维进行的数值实验证实，新算法收敛精度高，收敛速度快，且有效预防了早熟现象。 |
| 关键词: 粒子群优化；惯性权重；早熟；变异 |
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中图分类号: TP183
文献标识码: A
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| 基金项目: 东莞市社会科技发展项目(202050715806). |
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| Algorithm of a New Hybrid Particle Swarm Optimization |
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XU Shengbing, JIAN Ke, XIA Wenjie
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(School of Computer and Information, City College of Dongguan, Dongguan 523419, China )
xusb@ccdgut.edu.cn; jianke@ccdgut.edu.cn; xiawj@ccdgut.edu.cn
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| Abstract: This paper proposes a new hybrid particle swarm optimization algorithm to solve the problems of low convergence accuracy, slow convergence speed of particle swarm optimization algorithm in the late evolution stage, and being prone to mature early especially in the high-dimensional case. The new algorithm first proposes to design a new inertia weight, which makes the value selection of inertia weight moderate in the early and late evolution. Secondly, in order to effectively restrain the particles from falling into the local extreme value, the concepts of particle optimal velocity and the worst fitness of particles are introduced. Based on this, a new adaptive mutation method of particles is designed. Finally, the concepts of average convergence rate minimum average convergence algebra are introduced, which can better evaluate and compare the performance of the proposed algorithm. The numerical experiments of 8 standard test functions in 100 and 200 dimensions verify that the new algorithm has high convergence accuracy, fast convergence speed, and effectively prevents premature phenomenon. |
| Keywords: particle swarm optimization; inertia weight; premature; mutation |
