An Adaptive Penalty Formulation for Constrained Evolutionary Optimization

This paper proposes an adaptive penalty function for solving constrained optimization problems using genetic algorithms. The proposed method aims to exploit infeasible individuals with low objective value and low constraint violation. The number of feasible individuals in the population is used to g...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 39; no. 3; pp. 565 - 578
Main Authors Tessema, B., Yen, G.G.
Format Journal Article
LanguageEnglish
Published IEEE 01.05.2009
Subjects
Adaptive algorithms
Benchmark testing
Benchmarking
Constrained optimization
constraint handling
Constraint optimization
Constraints
Cybernetics
Design engineering
genetic algorithm (GA)
Genetic algorithms
Human
Humans
Hypercubes
Interference constraints
Optimization
penalty function
Search methods
Stochastic processes
Tuning
Upper bound
Online AccessGet full text

Cover

More Information
Summary:This paper proposes an adaptive penalty function for solving constrained optimization problems using genetic algorithms. The proposed method aims to exploit infeasible individuals with low objective value and low constraint violation. The number of feasible individuals in the population is used to guide the search process either toward finding more feasible individuals or searching for the optimum solution. The proposed method is simple to implement and does not need any parameter tuning. The performance of the algorithm is tested on 22 benchmark functions in the literature. The results show that the proposed approach is able to find very good solutions comparable to the chosen state-of-the-art designs. Furthermore, it is able to find feasible solutions in every run for all of the benchmark functions tested.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1083-4427
1558-2426
DOI:10.1109/TSMCA.2009.2013333