MultiObjective Genetic Modified Algorithm (MOGMA)

Vandeva, Elica

doi:10.2478/cait-2012-0010

Abstract

Multiobjective optimization based on genetic algorithms and Pareto based approaches in solving multiobjective optimization problems is discussed in the paper. A Pareto based fitness assignment is used − non-dominated ranking and movement of a population towards the Pareto front in a multiobjective optimization problem. A MultiObjective Genetic Modified Algorithm (MOGMA) is proposed, which is an improvement of the existing algorithm.

References

1. Awad, A. R., I. V. Poser. Calibrating Conceptual Rainfall-Runoff Models Using a Real Genetic Algorithm Combined with a Local Search Method, Latest Trends on Computers. - In: 14th WSEAS International Conference on COMPUTERS, Corfu Island, Greece, WSEAS, WSEAS Press, Vol. I, 2010, 174-181.
Search in Google Scholar
2. Coello, C. A. C. An Empirical Study of Evolutionary Techniques for Multiobjective Optimization in Engineeting Design. Ph. D. Thesis. Department of Computer Science, Tulane University, New Orleans, LA, 1996. http://delta.cs.cinvestav.mx/~ccoello/Thesis.html
Search in Google Scholar
3. Deb, K. Multi-Objective Optimization Using Evolutionary Algorithms. Department of Mechanical Engineering. Indian Institute of Technology, Kanpur, India, 2001, 200-216.
Search in Google Scholar
4. Deb, K. Solving Goal Programming Problems Using Multi-Objective Genetic Algorithms. Indian Institute of Technology, Kanpur, 2002, 77-84.
Search in Google Scholar
5. Fonseca, C. M., P. J. Fleming. Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. - In: S. Forrest, Ed. Proceedings of the 5th International Conference on Genetic Algorithms (San Mateo, California, 1993), University of Illinois at Urbana-Champaign: Morgan Kauffman Publishers, 1993, 416-423.
Search in Google Scholar
6. Goldberg, D. E. Genetic Algorithms in Search Optimization and Machine Learning. - In: Reading, Massachusetts, Addison-Wesley Publishing Company, 1989.
Search in Google Scholar
7. Goldberg, D. E., J. Richardson. Genetic Algorithm with Sharing for Multi-Modal Function Optimization. - In: J. J. Grefenstette, Ed. Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms, Lawrence Erlbaum, 1987, 41-49.
Search in Google Scholar
8. Ivanova, D., M. Popova, N. Valov. Application of Genetic Algorithm for Tuning PID Regulators. - Scientific Works of the Ruse University, Vol. 48, 2009, Series 3.1, 123-127.
Search in Google Scholar
9. Keller, A. A. Genetic Search Algorithm to Fuzzy Multiobjective Games: A Mathematica Implementation for Multi-Objectives Problems under Its Objective Boundary. - In: Selected Topics in Applied Computer Science, 10th WSEAS International Conference on Applied Science, Iwate Prefectural University, Japan, WSEAS, WSEAS Press, 2010, 351-359.
Search in Google Scholar
10. Kubat, M., A. Rozsypal. Using the Genetic Algorithm to Reduce the Size of a Nearest- Neighbor Classifier and to Select Relevant Attributes. - In: Proceedings of ICML’2001, 2001, 449-456.
Search in Google Scholar
11. Podinovski, V. V., V. D. Noggin. Pareto-Optimal Solutions to Multicriteria Problems. Moskow, FIZMATLIT, 2007. 256 p.
Search in Google Scholar
12. Sornmuang, S., S. Sujitjorn. GA-Based PIDA Control Design Optimization with an Application to AC Motor Speed Control. - International Journal of Mathematics and Computers in Simulation, Vol. 4, 2010, Issue 3, NUAN, 67-80.
Search in Google Scholar
13. Stanimirovíc, Z. A Genetic Algorithm Approach for the Capacitated Single Allocation P-Hub Median Problem. - Computing and Informatics, Vol. 29, 2010, 117-132.
Search in Google Scholar

MultiObjective Genetic Modified Algorithm (MOGMA)

Abstract

Paradigm

My account