An improved multi-objective particle swarm optimization and its application in raw ore dispatching

An improved multi-objective particle swarm optimization with time-varying parameter and follower bee search is proposed in this article. In this algorithm, the weight of personal best solution decreases gradually as the iteration continues. This approach eliminates the effect caused by its poorer qu...

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Vydáno v:Advances in mechanical engineering Ročník 10; číslo 2
Hlavní autoři: Zhang, Chao, Li, Qing, Chen, Peng, Feng, Qian, Cui, Jiarui
Médium: Journal Article
Jazyk:angličtina
Vydáno: London, England SAGE Publications 01.02.2018
Sage Publications Ltd
SAGE Publishing
Témata:
Algorithms
Authorship
Computer simulation
Convergence
Iron
Mining
Multiple objective analysis
Mutation
Particle swarm optimization
Search algorithms
Multi-objective particle swarm optimization
time-varying parameter
follower bee search
raw ore dispatching
ISSN:1687-8132, 1687-8140
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Shrnutí:An improved multi-objective particle swarm optimization with time-varying parameter and follower bee search is proposed in this article. In this algorithm, the weight of personal best solution decreases gradually as the iteration continues. This approach eliminates the effect caused by its poorer quality compared to global best solution so that the convergence ability of the algorithm is improved. Furthermore, the follower bee search in artificial bee colony algorithm is introduced to strengthen the randomness of the algorithm and discover more non-dominated solutions. A comparative simulation study is carried out using internal raw ore dispatching in an iron mining group that contains multiple stopes and concentrating mills. The results show that the proposed algorithm can significantly improve convergence and diversity.
Bibliografie:ObjectType-Article-1
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ISSN:1687-8132
1687-8140
DOI:10.1177/1687814018757376