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Bauman Moscow State Technical University. El № FS 77 - 48211. ISSN 1994-0408
Global Optimization Based on the Hybridization of Harmony Search and Particle Swarm Optimization Methods
# 07, July 2014
DOI: 10.7463/0714.0717653
Article file:
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SE-BMSTU...o242.pdf
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We consider a class of stochastic search algorithms of global optimization which in various publications are called behavioural, intellectual, metaheuristic, inspired by the nature, swarm, multi-agent, population, etc. We use the last term.
Experience in using the population algorithms to solve challenges of global optimization shows that application of one such algorithm may not always effective. Therefore now great attention is paid to hybridization of population algorithms of global optimization. Hybrid algorithms unite various algorithms or identical algorithms, but with various values of free parameters. Thus efficiency of one algorithm can compensate weakness of another.
The purposes of the work are development of hybrid algorithm of global optimization based on known algorithms of harmony search (HS) and swarm of particles (PSO), software implementation of algorithm, study of its efficiency using a number of known benchmark problems, and a problem of dimensional optimization of truss structure.
We set a problem of global optimization, consider basic algorithms of HS and PSO, give a flow chart of the offered hybrid algorithm called HSPSO, present results of computing experiments with developed algorithm and software, formulate main results of work and prospects of its development.
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Publications with keywords: global optimization, PSO, hybridization
Publications with words: global optimization, PSO, hybridization
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