Improved hybrid particle swarm optimisation for image segmentation. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Liu, Shuo; Zhou, Kang; Qi, Huaqing; Liu, Jiangrong
• Is Part Of: International journal of parallel, emergent and distributed systems. Volume 36:Issue 1(2021)
• Page Start: 44
• Page End: 50
• Publication Date: 2021-01-02
• Subjects: Hybrid particle swarm optimisation -- region equilibrium -- compression factor -- image segmentation; Parallel computers -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Computer algorithms -- Periodicals; 004.35
• DOI: 10.1080/17445760.2019.1689568
• Languages: English
• ISSNs: 1744-5760

Abstract

Abstract : A method for image segmentation based on improved hybrid particle swarm optimisation (PSO) is proposed. In view of the shortcoming that the traditional PSO algorithm is easy to fall into local optimal solution, we update the particle velocity based on the combination of global optimisation, region equilibrium and compression factor. By this way, the searchability of the particle and optimisation performance of the improved PSO is improved. Experiments results on three classic test functions show that the algorithm can greatly improve the searchability. Experiments also show that it performs well on image segmentation. GRAPHICAL ABSTRACT: