Artificial Plant Optimization Algorithm for Constrained Optimization Problems

Author

Zhao, Ziqiang ; Cui, Zhihua ; Zeng, Jianchao ; Yue, Xiaoguang

Author_Institution

Complex Syst. & Comput. Intell. Lab., Taiyuan Univ. of Sci. & Technol., Taiyuan, China

fYear

2011

fDate

16-18 Dec. 2011

Firstpage

120

Lastpage

123

Abstract

Artificial plant optimization algorithm is proposed to solve constrained optimization problems in this paper. In APOA, a shrinkage coefficient is introduce to ensure that all dimensions of a branch are within lower and upper bounds, and a new function to determine whether the particle is within the feasible region. One dimensional search optimization methods are selected in algorithm to produce a new position which is guaranteed to be in the feasible region for the branch which escapes from the feasible region. The experimental results show that artificial plant optimization algorithm is effective and efficient for constrained optimization problems.

Keywords

constraint theory; optimisation; search problems; artificial plant optimization algorithm; constrained optimization problems; search optimization; shrinkage coefficient; Algorithm design and analysis; Educational institutions; Optimization methods; Particle swarm optimization; Search problems; Upper bound; artificial plant optimization algorithm; constraint; feasible region; shrinkage coefficient;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovations in Bio-inspired Computing and Applications (IBICA), 2011 Second International Conference on

Conference_Location

Shenzhan

Print_ISBN

978-1-4577-1219-7

Type

conf

DOI

10.1109/IBICA.2011.34

Filename

6118680