Parameter co-evolution mechanism of particle swarm optimisation algorithm

被引：0
|
作者：
Zhao M. [1 ]
Song X. [1 ]
Gao Y. [2 ]
机构：
[1] Information and Control Engineering Faculty, Shenyang Jianzhu University, Shenyang, Liaoning Province
[2] Information Technology Department, Shenyang Gas Co. Ltd., Shenyang, Liaoning Province
来源：
International Journal of Simulation and Process Modelling | 2020年 / 15卷 / 03期
关键词：
Acceleration factor; Adjustment mechanism; Benchmark functions; Inertia weight; Numerical optimisation; Parameter co-evolution; Particle swarm optimisation; Population evolution; PSO; Stochastic evolution speed;
D O I：
10.1504/IJSPM.2020.107327
中图分类号：
学科分类号：
摘要：
The running parameters are the important factors that influence the performance of PSO, and the optimisation of the selection and the adjustment strategy on them is one of the hot research directions. Based on the related research, this paper designs a co-evolution mechanism for the parameters of PSO including both the inertia weight and the acceleration factors, which defines stochastic evolution speed to reflect the current state of population evolution during the iterative process, and uses it as the feedback to set the inertia weight and the two acceleration factors. PSO with the parameter co-evolution mechanism can realise cooperative evolution of the running parameters with the population by dynamically adjusting parameter values according to population evolution state. Compared with five widely recognised parameter selection or adjustment strategies, on 20 numerical optimisation benchmark functions of different categories, the effectiveness and the efficiency of the proposed mechanism are verified. © 2020 Inderscience Enterprises Ltd.
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页码：255 / 267
页数：12
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