NDLI: A Self Adaptive Penalty Function Based Algorithm for Constrained Optimization

Content Provider	IEEE Xplore Digital Library
Author	Tessema, B. Yen, G.G.
Copyright Year	2006
Description	Author affiliation: Oklahoma State Univ., Stillwater (Tessema, B.)
Abstract	This paper proposes a self adaptive penalty function for solving constrained optimization problems using genetic algorithms. In the proposed method, a new fitness value, called distance value, in the normalized fitness-constraint violation space, and two penalty values are applied to infeasible individuals so that the algorithm would be able to identify the best infeasible individuals in the current population. The method aims to encourage infeasible individuals with low 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 more feasible solutions or toward finding the optimum solution. The proposed method is simple to implement and does not need parameter tuning. The performance of the algorithm is tested on 13 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.
Starting Page	246
Ending Page	253
File Size	178262
Page Count	8
File Format	PDF
ISBN	0780394879
DOI	10.1109/CEC.2006.1688315
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Constraint optimization Interference constraints Genetic algorithms Benchmark testing Genetic engineering Production Costs Search methods
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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