IJSTR

International Journal of Scientific & Technology Research

IJSTR@Facebook IJSTR@Twitter IJSTR@Linkedin
Home About Us Scope Editorial Board Blog/Latest News Contact Us
CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT
QR CODE
IJSTR-QR Code

IJSTR >> Volume 3- Issue 9, September 2014 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Multiobjective Programming With Continuous Genetic Algorithm

[Full Text]

 

AUTHOR(S)

Adugna Fita

 

KEYWORDS

Keywords: Chromosome, Crossover, Heuristics, Mutation, Optimization, Population, Ranking,Genetic Algorithms, Multi-Objective, Pareto Optimal Solutions, Parent selection.

 

ABSTRACT

Abstract: Nowadays, we want to have a good life, which may mean more wealth, more power, more respect and more time for our selves, together with a good health and a good second generation, etc. Indeed, all important political, economical and cultural events have involved multiple criteria in their evolution. Multiobjective optimization deals with the investigation of optimization problems that possess more than one objective function. Usually there is no single solution that optimizes all functions simultaneously, we have solution set that is called nondominated set and elements of this set are usually infinite. It is from this set decision is made by taking elements of nondominated set as alternatives, which is given by analysts. But practically extraction of nondominated solutions and setting fitness function are difficult. This Paper will try to solve problems that the decision maker face in extraction of Pareto optimal solution with continuous variable genetic algorithm and taking objective function as fitness function without modification by considering box constraint and generating initial solution within box constraint and penalty function for constrained one. Solutions will be kept in feasible region during mutation and recombination.

 

REFERENCES

[1]. David A. Coley, An Introduction to Genetic Algorithms for Scientists and Engineers, 1999 by World Scientific Publishing Co. Pte. Ltd.

[2]. R. I. Bot, S-M. Grad, G. Wanka, Duality in Vector Optimization, Springer-Verlag Berlin Heidelberg (2009)

[3]. Deb, K., Multi-objective optimization using evolutionary algorithms, (2001), Wiley.

[4]. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T. A Fast Elitist Non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, (2000).

[5]. M. Ehrgott: Multicriteria Optimization, Springer, 2005, New York

[6]. G. Eichfelder, Vector Optimization, Springer, 2008, Berlin Heidelberg

[7]. Fonseca, C. M. and Fleming, P. J,An overview of evolutionary algorithms in multiobjective optimization (1995).

[8]. C. J. Goh and X. Q. Yang, Duality in Optimization and Variational Inequalities, Taylor and Francis (2002), New York

[9]. Knowles, J. D. and Corne, D. W., The Pareto archived evolution strategy, A new baseline algorithm for multiobjective optimization, Proceedings of the 1999 Congress on Evolutionary Computation.

[10]. Kokolo, I., Kita, H., and Kobayashi, S, Failure of Pareto-based MOEAs: Does nondominated really mean near to optimal, Proceedings of the Congress on Evolutionary Computation 2001.

[11]. John R. Koza, Genetic Programming, 1992 Massachusetts Institute of Technology

[12]. Randy L. Haupt, Sue Ellen Haupt: practical genetic algorithms second edition, 2004 by John Wiley & Sons, Inc.

[13]. Y. Sawaragi, H. Nakayama and T. Tanino, Theory of Multiobjective optimization, academic press, 1985.

[14]. M. Semu (2003), On Cone d.c optimization and Conjugate Duality, Chin. Ann. Math, 24B:521-528.

[15]. R. E. Steuer, Multiple Criteria: Theory, computation and Application, John Wiley & Sons, New York (1986)

[16]. S.N. Sivanandam, S.N. Deepa, Introduction to Genetic Algorithms, Springer-Verlag Berlin Heidelberg 2008

[17]. http://www.matworks.com.