A novel adaptive particle swarm optimization algorithm with foraging behavior for reliability redundancy allocation problem

2016 ◽  
Author(s):  
Qi Wang ◽  
Jiao Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Foraging Behavior ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Allocation Problem ◽  
Swarm Optimization ◽  
Redundancy Allocation Problem ◽  
Redundancy Allocation ◽  
Adaptive Particle Swarm Optimization

scholarly journals A Novel Adaptive Particle Swarm Optimization Algorithm with Foraging Behavior in Optimization Design

2018 ◽  
Vol 166 ◽  
pp. 02004
Author(s):  
Yan Liu ◽  
Qi Wang ◽  
Guoyi He ◽  
Li Zhang ◽  
Jiao Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Foraging Behavior ◽  
Optimization Algorithm ◽  
Optimization Design ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Continuous Variables ◽  
Good Reliability ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization

The method of repeated trial and proofreading is generally used to the convention reducer design, but these methods is low efficiency and the size of the reducer is often large. Aiming the problems, this paper presents an adaptive particle swarm optimization algorithm with foraging behavior, in this method, the bacterial foraging process is introduced into the adaptive particle swarm optimization algorithm, which can provide the function of particle chemotaxis, swarming, reproduction, elimination and dispersal, to improve the ability of local search and avoid premature behavior. By test verification through typical function and the application of the optimization design in the structure of the reducer with discrete and continuous variables, the results are shown that the new algorithm has the advantages of good reliability, strong searching ability and high accuracy. It can be used in engineering design, and has a strong applicability.

Self-Adaptive Particle Swarm Optimization Algorithm with Mutation Operation Based on K-Means

2013 ◽  
Vol 760-762 ◽  
pp. 2194-2198 ◽  
Author(s):  
Xue Mei Wang ◽  
Yi Zhuo Guo ◽  
Gui Jun Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Inertia Weight ◽  
Clustering Quality ◽  
Mutation Operation

Adaptive Particle Swarm Optimization algorithm with mutation operation based on K-means is proposed in this paper, this algorithm Combined the local searching optimization ability of K-means with the gobal searching optimization ability of Particle Swarm Optimization, the algorithm self-adaptively adjusted inertia weight according to fitness variance of population. Mutation operation was peocessed for the poor performative particle in population. The results showed that the algorithm had solved the poblems of slow convergence speed of traditional Particle Swarm Optimization algorithm and easy falling into the local optimum of K-Means, and more effectively improved clustering quality.

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