Improved Particle Swarm Optimization Algorithm Based on Last-Eliminated Principle and Enhanced Information Sharing. (5th December 2018)

• Record Type: Journal Article
• Authors: Lv, Xueying; Wang, Yitian; Deng, Junyi; Zhang, Guanyu; Zhang, Liu
• Other Names: Yáñez-Márquez Cornelio Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2018(2018)
• Publication Date: 2018-12-05
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2018/5025672
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : In this study, an improved eliminate particle swarm optimization (IEPSO) is proposed on the basis of the last-eliminated principle to solve optimization problems in engineering design. During optimization, the IEPSO enhances information communication among populations and maintains population diversity to overcome the limitations of classical optimization algorithms in solving multiparameter, strong coupling, and nonlinear engineering optimization problems. These limitations include advanced convergence and the tendency to easily fall into local optimization. The parameters involved in the imported "local-global information sharing" term are analyzed, and the principle of parameter selection for performance is determined. The performances of the IEPSO and classical optimization algorithms are then tested by using multiple sets of classical functions to verify the global search performance of the IEPSO. The simulation test results and those of the improved classical optimization algorithms are compared and analyzed to verify the advanced performance of the IEPSO algorithm.