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

Sai Ho Ling, Kit Yan Chan, Hung Fat Frank Leung, Frank Jiang, Hung Nguyen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

28 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)68-80
Number of pages13
JournalEngineering Applications of Artificial Intelligence
Publication statusPublished - 1 Jan 2016


  • Economic load dispatch
  • Email communication services
  • Fuzzy logic system
  • Particle swarm optimization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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