Abstract
This chapter introduces two algorithms for multiobjective optimization. These algorithms are based on a state-of-the-art Multiobjective Evolutionary Algorithm (MOEA) called Strength Pareto Evolutionary Algorithm 2 (SPEA2). The first proposed algorithm implements a competitive coevolution technique within SPEA2. In contrast, the second algorithm introduces a cooperative coevolution technique to SPEA2. Both novel coevolutionary approaches are then compared to the original SPEA2 in seven scalable DTLZ test problems with 3 to 5 objectives. Overall, the optimization results show that the two proposed approaches are superior to the original SPEA2 with regard to the average distance of the nondominated solutions to the true Pareto front, the diversity of the obtained solutions and also the coverage level. In addition, t-tests have been conducted to validate the significance of the improvements obtained by the augmented algorithms over the original SPEA2. Finally, cooperative coevolution is found to be better than competitive coevolution in terms of enhancing the performance of the original SPEA2.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Coello Coello, C.A.: Evolutionary multi-objective optimization: a critical review. In: Sarker, R., Mohammadian, M., Yao, X. (eds.) Evolutionary optimization, pp. 117–146. Kluwer Academic, Massachusetts (2002)
Coello Coello, C.A.: Recent trends in evolutionary multiobjective optimization. In: Abraham, A., Jain, L., Goldberg, R. (eds.) Evolutionary multiobjective optimization: theoretical advances and applications, pp. 7–32. Springer, Berlin (2005)
Coello Coello, C.A., Reyes Sierra, M.: A coevolutionary multi-objective evolutionary algorithm. In: Proceedings of the congress on evolutionary computation, pp. 482–489 (2003)
Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, New York (2001)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. KanGAL report 2001001. Kanpur Genetic Algorithms Laboratory (KanGAL), Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur (2001)
Durillo, J.J., Nebro, A.J., Coello Coello, C.A., Luna, F., Alba, E.: A comparative study of the effect of parameter scalability in multi-objective evolutionary algorithms. In: Proceedings of the IEEE congress on evolutionary computation, pp. 1893–1900 (2008)
Fonseca, C.M., Fleming, P.J.: An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation 3(1), 1–16 (1995)
Groşan, C.: Multiobjective adaptive representation evolutionary algorithm (marea) – a new evolutionary algorithm for multiobjective optimization. In: Proceedings of the world on-line conference on soft computing in industrial application, applied soft computing technologies: the challenge of complexity advances in soft computing, pp. 113–121. Springer, Berlin (2006)
Iorio, A.W., Li, X.-D.: A cooperative coevolutionary multiobjective algorithm using non-dominated sorting. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 537–548. Springer, Heidelberg (2004)
Keerativuttitumrong, N., Chaiyaratana, N., Varavithya, V.: Multi-objective co-operative co-evolutionary genetic algorithm. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 288–297. Springer, Heidelberg (2002)
Knowles, J., Thiele, L., Zitzler, E.: A tutorial on the performance assessment of stochastic multiobjective optimizers. TIK Report 214. Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Zurich (2006)
Lohn, J.D., Kraus, W.F., Haith, G.L.: Comparing a coevolutionary genetic algorithm for multiobjective optimization. In: Proceedings of the IEEE congress on evolutionary computation, pp. 1157–1162 (2002)
Maneeratana, K., Boonlong, K., Chaiyaratana, N.: Multi-objective optimisation by co-operative co-evolution. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 772–781. Springer, Heidelberg (2004)
Maneeratana, K., Boonlong, K., Chaiyaratana, N.: Compressed-objective genetic algorithm. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 473–482. Springer, Heidelberg (2006)
McFadden, C.H., Keeton, W.T.: Biology: an exploration of life. W. W. Norton & Company, New York (1995)
Panait, L., Luke, S.: A comparative study of two competitive fitness functions. In: Langdon, W.B., Cantú-Paz, E., Mathias, K.E., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M.A., Schultz, A.C., Miller, J.F., Burke, E.K., Jonoska, N. (eds.) Proceedings of the genetic and evolutionary computation conference, pp. 503–511. Morgan Kaufmann, California (2002)
Potter, M.A., De Jong, K.A.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)
Potter, M.A., De Jong, K.A.: Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 1–29 (2000)
Santana-Quintero, L.V., Coello Coello, C.A.: An algorithm based on differential evolution for multi-objective problems. International Journal of Computational Intelligence Research 1(2), 151–169 (2005)
Schott, J.R.: Fault tolerant design using single and multicriteria genetic algorithm optimization. Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Massachusetts (1995)
Tan, K.C., Lee, T.H., Yang, Y.J., Liu, D.S.: A cooperative coevolutionary algorithm for multiobjective optimization. In: Proceedings of the IEEE international conference on systems, man and cybernetics, pp. 1926–1931 (2004)
Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: Proceedings of the international conference on computational intelligence for modelling control and automation, vol. 1, pp. 695–701 (2005)
Tomassini, M.: Evolutionary algorithms. In: Sanchez, E., Tomassini, M. (eds.) Proceedings of the international workshop on towards evolvable hardware, the evolutionary engineering approach, pp. 19–47. Springer, Berlin (1996)
Van Veldhuizen, D.A., Lamont, G.B.: On measuring multiobjective evolutionary algorithm performance. In: Proceedings of the congress on evolutionary computation, vol. 1, pp. 204–211. IEEE Service Center, Piscataway (2000)
Walker, M.: Literature review (2003) (accessed on December 12, 2007), http://www.massey.ac.nz/~mgwalker/work/lit-review.pdf
Wiegand, R.P., De Jong, K.A.: Understanding coevolution-theory and analysis of coevolutionary algorithms: preface. In: Barry, A.M. (ed.) Proceedings of the genetic and evolutionary computation conference workshop on coevolution: understanding coevolution. AAAI, New York (2002)
Wiegand, R.P., Liles, W.C., De Jong, K.A.: An empirical analysis of collaboration methods in cooperative coevolutionary algorithms. In: Spector, L., Goodman, E.D., Wu, A., Langdon, W., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings of the genetic and evolutionary computation conference, pp. 1235–1242 (2001)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evolutionary Computation 8(2), 173–195 (2000)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. Technical report 103. Computer Engineering and Network Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Zurich (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Tan, T.G., Teo, J. (2009). Improving the Performance of Multiobjective Evolutionary Optimization Algorithms Using Coevolutionary Learning. In: Chiong, R. (eds) Nature-Inspired Algorithms for Optimisation. Studies in Computational Intelligence, vol 193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00267-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-00267-0_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00266-3
Online ISBN: 978-3-642-00267-0
eBook Packages: EngineeringEngineering (R0)