MATEC Web of Conferences
Volume 166, 2018The 2nd International Conference on Mechanical, Aeronautical and Automotive Engineering (ICMAA 2018)
|Number of page(s)||5|
|Section||Vehicle Design and System Control Engineering|
|Published online||23 April 2018|
A Novel Adaptive Particle Swarm Optimization Algorithm with Foraging Behavior in Optimization Design
Nanchang Hangkong University School of Aircraft Engineering, student, 2017, China
2 Nanchang Hangkong University School of Aircraft Engineering, professor, 2017, China
3 Nanchang Hangkong University School of Aircraft Engineering, associate professor, 2017, China
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.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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