Short PapersNo Access

A DYNAMIC POLYNOMIAL MUTATION FOR EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION ALGORITHMS

MOHAMMAD HAMDAN

Computer Science, Faculty of IT, Yarmouk University, Irbid 21163, Jordan

https://doi.org/10.1142/S0218213011000097Cited by:15 (Source: Crossref)

Abstract

Polynomial mutation is widely used in evolutionary optimization algorithms as a variation operator. In previous work on the use of evolutionary algorithms for solving multi-objective problems, two versions of polynomial mutations were introduced. The first is non-highly disruptive that is not prone to local optima and the second is highly disruptive polynomial mutation. This paper looks at the two variants and proposes a dynamic version of polynomial mutation. The experimental results show that the proposed adaptive algorithm is doing well for three evolutionary multiobjective algorithms on well known multiobjective optimization problems in terms of convergence speed, generational distance and hypervolume performance metrics.

Keywords:

Remember to check out the Most Cited Articles!
Check out Notable Titles in Artificial Intelligence.

References

Carlos A. Coello Coello , David A. Van Veldhuizen and Gary B. Lamont , Evolutionary Algorithms for Solving Multi-Objective Problems ( Kluwer Academic Publishers , New York , 2002 ) . Google Scholar
K. Deb , Multi-Objective Optimization Using Evolutionary Algorithms ( Wiley , Chichester, UK , 2001 ) . Google Scholar
K. Deb and R. Agrawal, Complex Systems 9, 115 (1995). Web of Science, Google Scholar
K. Deb and M. Goyal, Computer Science and Informatics 26(4), 30 (1996). Google Scholar
K. Debet al., IEEE Transactions on Evolutionary Computation 6(2), 182 (2002), DOI: 10.1109/4235.996017. Web of Science, Google Scholar
K. Debet al., Evolutionary Multiobjective Optimization: Theoretical Advances and Applications, eds. L. Jain, A. Abraham and R. Goldberg (Springer, Hiedelberg, 2005) pp. 105–145. Google Scholar
K. Deb and S. Tiwari, European Journal of Operational Research 185 (2008). Google Scholar
Juan J. Durillo, Antonio J. Nebro, Francisco Luna, Bernabé Dorronsoro, and Enrique Alba, jMetal: A Java framework for developing multi-objective optimization metaheuristics. Technical Report ITI-2006-10, Departamento de Lenguajes y Ciencias de la Computación, University of Málaga, E.T.S.I. Informática, Campus de Teatinos, December 2006 . Google Scholar
A. Nebro et al., The deme project., [URL] Accessed December 2008 , http://neo.lcc.uma.es/Software/deme/html/ . Google Scholar
D. Goldberg , Genetic Algorithms in Search, Optimization and Machine Learning ( Addison-Wesley Publishing Company, Inc. , 1989 ) . Google Scholar
Carlos A. Coello Coello et al. , A comparative study of the effect of parameter scalability in multi-objective metaheuristics , IEEE Congress on Evolutionary Computing . Google Scholar
Antonio J. Nebroet al., A cellular genetic algorithm for multiobjective optimization, Proceedings of the Workshop on Nature Inspired Co-operative Strategies for Optimization (NICSO 2006), eds. David A. Pelta and Natalio Krasnogor (Granada, Spain, 2006) pp. 25–36. Google Scholar
N. Srinivas and K. Deb, Evolutionary Computation 2, 221 (1995), DOI: 10.1162/evco.1994.2.3.221. Web of Science, Google Scholar
David A. Van Veldhuizen and Gary B. Lamont, Multiobjective evolutionary algorithm research: A history and analysis. Technical Report TR-98-03, Department of Electrical and Computer Engineering, Air Force Institute of Technology,Wright-Patterson AFB,Ohio, 1998 . Google Scholar
E. Zitzler, K. Deb and L. Thiele, Evolutionary Computing 8(2), 173 (2000), DOI: 10.1162/106365600568202. Web of Science, Google Scholar
E. Zitzler, M. Laumanns, and L. Thiele, Spea2: Improving the strength pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, 2001 . Google Scholar