Multi-Strategy Particle Swarm Optimization Algorithm Based on Evolution Ability

被引：3

作者：

Wang, Xiaoyan ^{[1
]}

Cao, Dexin ^{[1
]}

机构：

[1] School of Mathematics, China University of Mining and Technology, Jiangsu, Xuzhou

来源：

Computer Engineering and Applications | 2024年 / 59卷 / 05期

关键词：

fitness value change direction; inertial weight; particle activity; particle swarm optimization; semi-infinite programming;

D O I：

10.3778/j.issn.1002-8331.2111-0423

中图分类号：

学科分类号：

摘要：

Aiming at the shortcomings of particle swarm optimization algorithm, such as easy premature convergence and low solution accuracy, a multi-strategy particle swarm optimization algorithm based on evolution ability is proposed. According to the change direction of fitness value, particles are divided into progressive particles and retrogressive particles. The progressive particles are updated according to the original evolution strategy, retaining the advantages of the original algorithm. For the retrogressive particles, it is further divided into temporarily retrogressive particles and long-term retrogressive particles according to the particle activity. For temporarily retrogressive particles, the dependence on individual historical speed is reduced or even learning in the opposite direction. For the long-term retrogressive particles, different evolution strategies are adopted according to the fitness value of the particles to improve the global optimization ability. At the same time, it designs a kind of inertia weight with random fluctuations, so that the particles still have the ability to jump out of the current area in the later stage of the algorithm, which is conducive to the global search. Comparing the optimization results with other algorithms in 10 test functions in different dimensions shows that this algorithm has advantages in both the convergence speed and accuracy of solving low-dimensional and high-dimensional problems. The EAMSPSO algorithm is applied to solve semi-infinite programming problems. Experimental results show that this algorithm is suitable for solving semi-infinite programming problems and has advantages. © The Author(s) 2024.

引用

页码：78 / 86

页数：8

共 25 条

[1]

KENNEDY J, EBERHART R C., Particle swarm optimization[C], Proceedings of the IEEE International Conference on Neural Networks, pp. 1942-1948, (1995)

[2]

CHEN K, ZHOU F Y，, YUAN X F., Hybrid particle swarm optimization with spiral- shaped mechanism for feature selection[J], Expert Systems with Applications, 128, pp. 140-156, (2019)

[3]

ZHANG Q B, WANG P, CHEN Z H., An improved particle filter for mobile robot localization based on particle swarm optimization[J], Expert Systems with Applications, 135, pp. 181-193, (2019)

[4]

LIU H, WANG Y, TU L, Et al., A modified particle swarm optimization for large-scale numerical optimizations and engineering design problems[J], Journal of Intelligent Manufacturing, 30, 6, pp. 2407-2433, (2019)

[5]

SHI Y H, EBERHART R C., A modified particle swarm optimizer[C], Proceedings of 1998 IEEE International Conference on Evolutionary Computation, pp. 69-73, (1998)

[6]

EBERHART R C, SHI Y H., Tracking and optimizing dynamic systems with particle swarms[C], Proceedings of the 2001 Congress on Evolutionary Computation, pp. 94-100, (2001)

[7]

LI L S, ZHANG X J., New chaos particle swarm optimization based on adaptive inertia weight[J], Computer Engineering and Applications, 54, 9, pp. 139-144, (2018)

[8]

LIANG J J, QIN A K, SUGANTHAN P N, Et al., Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J], IEEE Transactions on Evolutionary Computation, 10, 3, pp. 281-295, (2006)

[9]

ZHANG T Z, LI Y X, XIANG Z L，, Et al., Particle swarm optimization algorithm based on RMSprop method[J], Computer Engineering and Design, 42, 3, pp. 642-648, (2021)

[10]

ZHANG J Y，, ZHANG J, JI W D, Et al., Particle swarm optimization algorithm with self- correcting and dimension by dimension learning capabilities[J], Journal of Chinese Computer Systems, 42, 5, pp. 919-926, (2021)

← 1 2 3 →