Abstract/Details

A self-adaptive genetic algorithm for constrained optimization


2006 2006

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Abstract (summary)

Scope and method of study. This study proposes a self-adaptive penalty function algorithm for solving constrained optimization problems using genetic algorithm (GA). Constrained optimization is a practically relevant and challenging field that deals with optimization of real world problems that involve complex constraints that make them difficult to tackle. GA is a stochastic search method based on the evolutionary ideas of natural selection and genetic. In GA candidate solutions to a certain problem, called individuals, will evolve from generation to generation toward finding better solutions. In this research GA based constraint handling algorithm is proposed that combines the merits of previously designed algorithms. In the proposed method a new fitness value, called distance value, and two penalties are applied to infeasible individuals that violate the constraints. The algorithm aims to encourage infeasible individuals with better objective function value and low constraint violation. The number of feasible individuals in the population is used to guide the search process either toward finding the optimum solution or toward finding more feasible solutions.

Findings and conclusions. The performance of the algorithm is tested on 22 benchmark functions in the literature. The results show that the approach is able to find very good solutions comparable to other state-of-the-art designs. Furthermore it is able to find feasible solutions in every run for all of the benchmark functions.

Indexing (details)


Subject
Electrical engineering;
Computer science
Classification
0544: Electrical engineering
0984: Computer science
Identifier / keyword
Applied sciences
Title
A self-adaptive genetic algorithm for constrained optimization
Author
Tessema, Biruk Girma
Number of pages
84
Publication year
2006
Degree date
2006
School code
0664
Source
MAI 45/03M, Masters Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
Advisor
Yen, Gary G.
University/institution
Oklahoma State University
University location
United States -- Oklahoma
Degree
M.S.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
1440394
ProQuest document ID
304940556
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304940556
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