Differential evolution combined with constraint consensus for constrained optimization

Author

Hamza, Noha M. ; Elsayed, Saber M. ; Essam, Daryl L. ; Sarker, Ruhul A.

Author_Institution

Sch. of Eng. & Inf. Technol., Univ. of New South Wales, Canberra, ACT, Australia

fYear

2011

fDate

5-8 June 2011

Firstpage

865

Lastpage

872

Abstract

Solving a Constrained Optimization Problem (COP) is much more challenging than its unconstrained counterpart. In solving COPs, the feasibility of a solution is a prime condition that requires the conversion of one or more infeasible individuals to feasible individuals. In this paper, to encourage the effective movement of infeasible individuals towards a feasible region, we introduce a Constraint Consensus (CC) method within the Differential Evolution (DE) algorithm for solving COPs. The algorithm has been tested by solving 13 well-known benchmark problems. The experimental results show that the solutions are competitive, if not better, as compared to the state of the art algorithms.

Keywords

benchmark testing; constraint theory; evolutionary computation; optimisation; CC method; COP; DE algorithm; benchmark problems; constrained optimization problem; constraint consensus; differential evolution; unconstrained counterpart; Algorithm design and analysis; Asynchronous transfer mode; Equations; Indexes; Optimization; Projection algorithms; Vectors; Constrained optimization; constraint consensus; differential evolution;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation (CEC), 2011 IEEE Congress on

Conference_Location

New Orleans, LA

ISSN

Pending

Print_ISBN

978-1-4244-7834-7

Type

conf

DOI

10.1109/CEC.2011.5949709

Filename

5949709