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Soft Computing

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08.03.2019 | Methodologies and Application

A nested particle swarm algorithm based on sphere mutation to solve bi-level optimization

verfasst von: Long Zhao, JingXuan Wei

Erschienen in: Soft Computing | Ausgabe 21/2019

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Abstract

The problem of bi-level optimization has always been a hot topic due to its extensive application. Increasing size and complexity have prompted theoretical and practical interest in the design of effective algorithm. This paper adopts particle swarm algorithm (PSO) at both level. First, given the nested nature of bi-level problem, we introduce a hyper-sphere search into PSO as mutation operator to maintain the swarms diversity. Second, for complex constraints processing, the proposed algorithm adopts a dynamic constraint handling strategy, which makes the solution located on the constraint boundary easier to be obtained. Third, a quadratic approximation mutation is introduced into PSO, which guides particles to a better search area. Finally, the convergence is proved and the simulation results show that the proposed algorithm is effective.

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Titel: A nested particle swarm algorithm based on sphere mutation to solve bi-level optimization
verfasst von: Long Zhao
JingXuan Wei
Publikationsdatum: 08.03.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 21/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-03888-6

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