An improved design optimisation algorithm based on swarm intelligence

被引:6
|
作者
Wu, Qinghua [1 ]
Liu, Hanmin [2 ]
Yan, Xuesong [3 ]
机构
[1] Wuhan Inst Technol, Sch Comp Sci & Engn, Hubei Prov Key Lab Intelligent Robot, Wuhan, Hubei, Peoples R China
[2] Wuhan Inst Ship Bldg Technol, Wuhan 430050, Hubei, Peoples R China
[3] China Univ Geosci, Sch Comp Sci, Wuhan, Hubei 430074, Peoples R China
关键词
design optimisation; particle swarm optimisation; PSO; particle; genetic algorithms;
D O I
10.1504/IJCSM.2014.059382
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In design optimisation field, there are many non-linear optimisation problems and the traditional algorithms cannot deal with these problems well. In this paper, we improve the standard particle swarm optimisation (PSO) and propose a new algorithm to solve the overcome of standard PSO algorithm like being trapped easily into a local optimum. The new algorithm keeps not only the fast convergence speed characteristic of PSO, but effectively improves the capability of global searching as well. Compared with standard PSO on the benchmark functions, the results show that the new algorithm is efficient. We also used the new algorithm to solve design optimisation problems and the experiment results show the new algorithm is effective for these problems.
引用
收藏
页码:27 / 36
页数:10
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