A novel particle swarm-based symbiotic evolutionary algorithm for a class of multi-modal functions

Jhen Jia Hu; Tzuu-Hseng S. Li; Yu Te Su

A novel particle swarm-based symbiotic evolutionary algorithm for a class of multi-modal functions

Jhen Jia Hu, Tzuu-Hseng S. Li, Yu Te Su

Department of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

Particle swarm-based symbiotic evolutionary (PSSE) algorithm is a novel symbiotic evolution (SE) that incorporates particle swarm optimization (PSO). Different from the conventional genetic algorithm (GA), PSSE established the coevolution and the cooperation between symbiotic relationship and swarm intelligence. Moreover, due to the adoption of sexual selection and biological arms race, PSSE is easily added into a global optimum when solving multi-modal numerical optimization problems. In this novel algorithm, gene hierarchy and gene particle swarm (GPS) model are defined. Except for the prototype PSSE, two collocating strategies of inertia weight for PSSE-LTI (linear time invariant) and PSSE-NTI (nonlinear time invariant) are also considered when the genes lie on a different level of hierarchy. Four famous benchmark functions are used to test the performance. Simulation results show this algorithm can improve the performance significantly. ICIC International

Original language	English
Pages (from-to)	1905-1920
Number of pages	16
Journal	International Journal of Innovative Computing, Information and Control
Volume	7
Issue number	4
Publication status	Published - 2011 Apr 1

All Science Journal Classification (ASJC) codes

Software
Theoretical Computer Science
Information Systems
Computational Theory and Mathematics

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abstract = "Particle swarm-based symbiotic evolutionary (PSSE) algorithm is a novel symbiotic evolution (SE) that incorporates particle swarm optimization (PSO). Different from the conventional genetic algorithm (GA), PSSE established the coevolution and the cooperation between symbiotic relationship and swarm intelligence. Moreover, due to the adoption of sexual selection and biological arms race, PSSE is easily added into a global optimum when solving multi-modal numerical optimization problems. In this novel algorithm, gene hierarchy and gene particle swarm (GPS) model are defined. Except for the prototype PSSE, two collocating strategies of inertia weight for PSSE-LTI (linear time invariant) and PSSE-NTI (nonlinear time invariant) are also considered when the genes lie on a different level of hierarchy. Four famous benchmark functions are used to test the performance. Simulation results show this algorithm can improve the performance significantly. ICIC International",

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