Multi-sub-swarm particle swarm optimization algorithm for multimodal function optimization

2007 ◽  
Author(s):  
Jun Zhang ◽  
De-Shuang Huang ◽  
Kun-Hong Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Function Optimization ◽  
Swarm Optimization ◽  
Multimodal Function Optimization ◽  
Multimodal Function

An Improved Particle Swarm Optimization Based on Biological Chemotaxis

2014 ◽  
Vol 1015 ◽  
pp. 737-740
Author(s):  
Hui Xia
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Function Optimization ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Multimodal Function ◽  
Swarm Algorithm ◽  
Improved Algorithm

Standard particle swarm algorithm for function optimization prone to local optimal and premature convergence, and thus the biological chemotaxis principle introduction to particle swarm optimization algorithm, this paper proposed an improved algorithm to maintain the diversity of the populationand the choice of key parameters. Simulation results show that, compared with the traditional particle swarm optimization algorithm, an improved particle swarm algorithm for dealing with complex multimodal function optimization problem can be significantly improved algorithm for global optimization.

Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree

2013 ◽  
Vol 427-429 ◽  
pp. 1934-1938
Author(s):  
Zhong Rong Zhang ◽  
Jin Peng Liu ◽  
Ke De Fei ◽  
Zhao Shan Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.

Optimization Design of Gear Train Based on Particle Swarm Optimization Algorithm

2013 ◽  
Vol 373-375 ◽  
pp. 1072-1075 ◽  
Author(s):  
Chang Wei Wu ◽  
Yong Hai Wu ◽  
Cong Bin Ma ◽  
Cheng Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Optimization Design ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Function Optimization ◽  
Gear Train ◽  
Multidimensional Space ◽  
Swarm Optimization ◽  
Tooth Width

Particle swarm optimization algorithms have lots of advantages such as fast convergence speed, good quality of solution and robustness in multidimensional space function optimization and dynamic target optimization. It is suitable for structural optimization design. In this paper, manual transmission gear train of a tractor is taken as research object, the minimum quality and minimum center distance of the gear train is taken as optimization goal, the gear ratio, modulus, helix angle, tooth width and equilibrium conditions of the axial force are taken as the constraints, a multi-objective optimization model of the gear train is established. The optimal structure design programs and Pareto optimal solution are obtained by using particle swarm optimization algorithm.

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