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    Quality and robustness improvement for real world industrial systems using a fuzzy particle swarm optimization

    231688_231688.pdf (184.6Kb)
    Access Status
    Open access
    Authors
    Ling, S.
    Chan, Kit Yan
    Leung, F.
    Jiang, F.
    Nguyen, H.
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Ling, S. and Chan, K.Y. and Leung, F. and Jiang, F. and Nguyen, H. 2015. Quality and robustness improvement for real world industrial systems using a fuzzy particle swarm optimization. Engineering Applications of Artificial Intelligence. 47: pp. 68-80.
    Source Title
    Engineering Applications of Artificial Intelligence
    DOI
    10.1016/j.engappai.2015.03.003
    ISSN
    0952-1976
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/42764
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a novel fuzzy particle swarm optimization with cross-mutated (FPSOCM) operation, where a fuzzy logic system developed based on the knowledge of swarm intelligence is proposed to determine the inertia weight for the swarm movement of particle swarm optimization (PSO) and the control parameter of a newly introduced cross-mutated operation. Hence, the inertia weight of the PSO can be adaptive with respect to the search progress. The new cross-mutated operation intends to drive the solution to escape from local optima. A suite of benchmark test functions are employed to evaluate the performance of the proposed FPSOCM. Experimental results show empirically that the FPSOCM performs better than the existing hybrid PSO methods in terms of solution quality, robustness, and convergence rate. The proposed FPSOCM is evaluated by improving the quality and robustness of two real world industrial systems namely economic load dispatch system and self-provisioning systems for communication network services. These two systems are employed to evaluate the effectiveness of the proposed FPSOCM as they are multi-optima and non-convex problems. The performance of FPSOCM is found to be significantly better than that of the existing hybrid PSO methods in a statistical sense. These results demonstrate that the proposed FPSOCM is a good candidate for solving product or service engineering problems which have multi-optima or non-convex natures.

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