Multiobjective Adaptive Representation Evolutionary Algorithm (MAREA) - a new evolutionary algorithm for multiobjective optimization
Many algorithms for multiobjective optimization have been proposed in the last years. In the recent past a great importance have the MOEAs able to solve problems with more than two objectives and with a large number of decision vectors (space dimensions). The diffculties occur when problems with more than three objectives (higher dimensional problems) are considered. In this paper, a new algorithm for multiobjective optimization called Multiobjective Adaptive Representation Evolutionary Algorithm (MAREA) is proposed. MAREA combines an evolution strategy and an steady-state algorithm. The performance of the MAREA algorithm is assessed by using several well-known test functions having more than two objectives. MAREA is compared with the best present day algorithms: SPEA2, PESA and NSGA II. Results show that MAREA has a very good convergence.
Unable to display preview. Download preview PDF.
- 1.Corne, D., Knowles, J., Oates, M. The Pareto-Envelope based Selection Algorithm for Multiobjective Optimization. In Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature, Springer-Verlag, Berlin, (2000) 839–848.Google Scholar
- 2.K. Deb, S. Agrawal, A Pratap and T. Meyarivan, A fast elitist non – dominated sorting genetic algorithm for multi-objective optimization: NSGA II. In M. S. et al. (Ed), Parallel Problem Solving From Nature – PPSN VI, Springer-Verlag, Berlin (2000) 849–858.Google Scholar
- 3.K. Deb, L. Thiele, M. Laumanns and E. Zitzler. Scalable Multi-Objective Optimization Test Problems. Proceeding of IEEE Congress on Evolutionary Computation, Hawaii, (2002).Google Scholar
- 4.Deb, S. Jain, Running performance metrics for evolutionary multi-objective optimization, KanGAL Report 2002004, Indian Institute of Technology, Kanpur, India, (2002).Google Scholar
- 5.Grosan, C., Oltean, M. Adaptive Representation Evolutionary Algorithm – a new technique for single objective optimization. In Proceedings of First Balcanic Conference on Informatics (BCI), Thessaloniki, Greece, (2003) 345–355.Google Scholar
- 6.V. Khare, X. Yao, K. Deb. Performance Scaling on Multi-objective Evolutionary Algorithms. Technical Report 2002009, Kanpur Genetic Algorithm Laboratory (KanGAL), Indian Institute of Technology Kanpur, India (2002).Google Scholar
- 7.12.Kingdon J, Dekker L. The Shape of Space, Proceedings of the First IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA ′95) IEE, London, (1995) 543–548.Google Scholar
- 8.Zitzler, E., Marco Laumanns and Thiele, L., SPEA 2: Improving the Strength Pareto Evolutionary Algorithm, TIK Report 103, Computer Engineering and Networks Laboratory (TIK), Departament of Electrical Engineering Swiss federal Institute of Technology (ETH) Zurich, (2001).Google Scholar