Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27854
Title: Quality and robustness improvement for real world industrial systems using a fuzzy particle swarm optimization
Authors: Ling, SH
Chan, KY
Leung, FHF 
Jiang, F
Nguyen, H
Keywords: Economic load dispatch
Email communication services
Fuzzy logic system
Particle swarm optimization
Issue Date: 2015
Publisher: Pergamon Press
Source: Engineering applications of artificial intelligence, 2015, v. 31, no. 3, p. 513-522 How to cite?
Journal: Engineering applications of artificial intelligence 
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.
URI: http://hdl.handle.net/10397/27854
ISSN: 0952-1976
EISSN: 1873-6769
DOI: 10.1016/j.engappai.2015.03.003
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