Comparison of several types of methods for solving constrained function optimization problems

Author

Hu, Kangxiu ; Wang, Bingxian

Author_Institution

Sch. of Math. & Informational Sci., East China Inst. of Technol., Fuzhou, China

fYear

2012

fDate

3-5 June 2012

Firstpage

821

Lastpage

824

Abstract

Several types of methods for solving constrained function optimization problems are discussed in this paper including elite-subspace evolutionary algorithm (ESEA), multi-parent crossover evolutionary algorithm (MPCEA), smooth scheme and line search based particle swarm optimization (SLPSO) and Constrained Differential evolutionary algorithm (CDEA). Numerical simulation experiments show that CDEA is the best method. The approach can maintain population diversity and simple parameter setting and enable us to find the optimal solution within a fairly short period of time.

Keywords

constraint satisfaction problems; evolutionary computation; numerical analysis; particle swarm optimisation; search problems; CDEA; ESEA; MPCEA; SLPSO; constrained differential evolutionary algorithm; constrained function optimization problem solving; elite subspace evolutionary algorithm; line search based particle swarm optimization; multiparent crossover evolutionary algorithm; numerical simulation experiments; optimal solution; parameter setting; population diversity; smooth scheme; Algorithm design and analysis; Numerical simulation; Optimization methods; Particle swarm optimization; Search problems; Numerical simulation; constrained function; optimization Problems;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Applications (ISRA), 2012 IEEE Symposium on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4673-2205-8

Type

conf

DOI

10.1109/ISRA.2012.6219317

Filename

6219317