Evolutionary Swarm Optimization Algorithm for Numerical Function Optimization

Author

Quan, Haiyan ; Shi, Xinling

Author_Institution

Fac. of Inf. & Autom., Yunnan Univ., Kunming, China

Volume

5

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

551

Lastpage

555

Abstract

The paper introduces an evolutionary swarm model (ESM), based on the model, an evolutionary swarm algorithm (ESA) is designed out using five elements. In this work, the performance of ESA is tested with 5 multivariable benchmark functions, and compared with the other optimization algorithms. The simulation results show that the algorithm has an excellent performance in the global optimization, and can be efficiently employed to solve the optimization problem for the multimodal function with high dimensionality.

Keywords

evolutionary computation; numerical analysis; particle swarm optimisation; evolutionary swarm optimization algorithm; multimodal function optimization; multivariable benchmark functions; numerical function optimization; Algorithm design and analysis; Ant colony optimization; Benchmark testing; Design automation; Design optimization; Educational institutions; Genetics; Paper technology; Particle swarm optimization; Stochastic processes; evolution algorithm; evolutionary swarm algorithm; evolutionary swarm model; numerical function optimization; swarm algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2009. ICNC '09. Fifth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3736-8

Type

conf

DOI

10.1109/ICNC.2009.79

Filename

5367094