Abstract
In this paper, we propose the integration between Strength Pareto Evolutionary Algorithm 2 (SPEA2) with two types of coevolution concept, Competitive Coevolution (CE) and Cooperative Coevolution (CC), to solve 3 dimensional multiobjective optimization problems. The resulting algorithms are referred to as Strength Pareto Evolutionary Algorithm 2 with Competitive Coevolution (SPEA2-CE) and Strength Pareto Evolutionary Algorithm 2 with Cooperative Coevolution (SPEA2-CC). The main objective of this paper is to compare competitive against cooperative coevolution to ascertain which coevolutionary approach is preferable for multiobjective optimization. The competitive coevolution will be implemented with K-Random Opponents strategy. The performances of SPEA2-CE and SPEA2-CC for solving tri-objective problems using the DTLZ suite of test problems are presented. The results show that the cooperative approach far outperforms the competitive approach when used to augment SPEA2 for tri-objective optimization in terms of all the metrics (generational distance, spacing and coverage).
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References
Angeline, P.J., Pollack, J.B.: Competitive Environments Evolve Better Solutions for Complex Tasks. In: Forrest, S. (ed.) Proc. 5th International Conference on Genetic Algorithm, pp. 264–270. Morgan Kaufmann, San Francisco (1993)
Coello Coello, C.A., Reyes Sierra, M.: A Coevolutionary Multi-Objective Evolutionary Algorithm. Evolutionary Computation 1, 482–489 (2003)
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, India (2001)
Hillis, W.D.: Co-evolving Parasites Improve Simulated Evolution as an Optimization Procedure, pp. 228–234. MIT Press, Cambridge (1991)
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 VII. LNCS, vol. 2439, pp. 288–297. Springer, Heidelberg (2002)
Lohn, J., Kraus, W., Haith, G.: Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization. In: Fogel, D., et al. (eds.) CEC 2002. Proc. 2002 Congress on Evolutionary Computation, pp. 1157–1162. IEEE Computer Society Press, Los Alamitos (2002)
Panait, L., Luke, S.: A Comparative Study of Two Competitive Fitness Functions. In: Langdon, W.B., et al. (eds.) GECCO 2002. Proc. Genetic and Evolutionary Computation Conference, pp. 503–511. Morgan Kaufmann, San Francisco (2002)
Parmee, I.C., Watson, A.H.: Preliminary Airframe Design Using Co-evolutionary Multiobjective Genetic Algorithms. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) GECCO 1999. Proc. Genetic and Evolutionary Computation Conference, vol. 2, pp. 1657–1665. Morgan Kaufmann, San Francisco (1999)
Potter, M.A., DeJong, K.A.: A Cooperative Coevolutionary Approach to Function Optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN III. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)
Rosin, C.D., Belew, R.K.: Methods for Competitive Co-evolution: Finding Opponents Worth Beating. In: Eshelman, L. (ed.) Proc. 6th International Conference on Genetic Algorithms, pp. 373–380. Morgan Kaufmann, San Francisco (1995)
Schott, J.R.: Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. Master’s thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts (1995)
Van Veldhuizen, D.A., Lamont, G.B.: On Measuring Multiobjective Evolutionary Algorithm Performance. Evolutionary Computation 1, 204–211 (2000)
Wiegand, R.P., Liles, W.C., DeJong, K.A.: An Empirical Analysis of Collaboration Methods in Cooperative Coevolutionary Algorhtms. In: Spector, L., et al. (eds.) Proc. Genetic and Evolutionary Computation Conference, pp. 1235–1242. Morgan Kaufmann, San Francisco (2001)
Yao, X.: Evolutionary Computation. In: Sarker, R., Mohammadian, M., Yao, X. (eds.) Evolutionary Optimization. International Series in Operations Research and Management Science, pp. 27–46. Kluwer, United States (2002)
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, Switzerland (2001)
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Tan, T.G., Lau, H.K., Teo, J. (2007). Cooperative Versus Competitive Coevolution for Pareto Multiobjective Optimization. In: Li, K., Fei, M., Irwin, G.W., Ma, S. (eds) Bio-Inspired Computational Intelligence and Applications. LSMS 2007. Lecture Notes in Computer Science, vol 4688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74769-7_8
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