Abstract
An adaptive Pareto differential evolution algorithm for multi-objective optimization is proposed. Its effectiveness on approximating the Pareto front is compared with that of SPEA [9] and of SPDE [2]. A parallel implementation, based on an island model with a random connection topology, is also analyzed. The parallelization efficiency derives from the simple migration strategy. Numerical tests were performed on a cluster of workstations.
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References
Abbass, H.A., Sarker, R., Newton, C.: PDE: A Pareto-frontier Differential Evolution Approach for Multi-objective Optimization Problems. In: IEEE Proc. of the Congress on Evolutionary Computation 2001 (CEC 2001), vol. 2, pp. 971–978 (2001)
Abbass, H.A.: The Self-Adaptive Pareto Differential Evolution Algorithm. In: IEEE Proc. of Congress on Evolutionary Computation (CEC 2002), vol. 1, pp. 831–836 (2002)
Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization. NSGA-II, KanGAL report 200001, Indian Institute of Technology, Kanpur (2000)
Hiroyasu, T., Miki, M., Watanabe, S.: The New Model of Parallel Generic Algorithm in Multi-Objective Optimization Problems - Divide Range Multi-Objective Genetic Algorithm. In: IEEE Proc. of Congress on Evolutionary Computation (CEC 2000), vol. 1, pp. 333–340 (2000)
Madavan, N.K.: Multiobjective Optimization using a Pareto Differential Evolution Approach. In: IEEE Proc. of Congress on Evolutionary Computation (CEC 2002), vol. 1, pp. 1145–1150 (2002)
Storn, R., Price, K.: Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, Techn. Rep. TR-95-012, ICSI (1995)
Tomassini, M.: Parallel and Distributed Evolutionary Algorithms: A Review. In: Miettinen, K., et al. (eds.) Evolutionary Algorithms in Engineering and Computer Science, pp. 113–133. J. Wiley and Sons, Chichester (1999)
Toro, F., Ortega, J., Fernandez, J., Diaz, A.: PSFGA: A Parallel Genetic Algorithm for Multiobjective Optimization. In: Proc. 10th Euromicro Workshop on Parallel, Distributed & Network-based Processing (EuroMicro-PDP 2002) (2002)
Zitzler, E., Thiele, L.: An Evolutionary Algorithm for Multiobjective Optimization: The Strength Pareto Approach. Tech. Rep. 43, Computer Eng. and Comm. Networks Lab (TIK), Swiss Federal Institute of Technology, ETH (1998)
Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2), 173–195 (2000)
Zaharie, D.: Control of Population Diversity and Adaptation in Differential Evolution Algorithms. In: Matoušek, R., Ošmera, P. (eds.) Proc. of Mendel 2003, 9th International Conference on Soft Computing, pp. 41–46 (2003)
Zaharie, D., Petcu, D.: Parallel Implementation of Multi-population Differential Evolution. In: Grigoraş, D., et al. (eds.) Proc. 2th Workshop on Concurrent Information Processing and Computing, Sinaia (2003) (in print)
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Zaharie, D., Petcu, D. (2004). Adaptive Pareto Differential Evolution and Its Parallelization. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2003. Lecture Notes in Computer Science, vol 3019. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24669-5_34
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DOI: https://doi.org/10.1007/978-3-540-24669-5_34
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