Abstract
Conventional multi-objective Particle Swarm Optimization (PSO) algorithms aim to find a representative set of Pareto-optimal solutions from which the user may choose preferred solutions. For this purpose, most multi-objective PSO algorithms employ computationally expensive comparison procedures such as non-dominated sorting. We propose a PSO algorithm, Reference point-based PSO using a Steady-State approach (RPSO-SS), that finds a preferred set of solutions near user-provided reference points, instead of the entire set of Pareto-optimal solutions. RPSO-SS uses simple replacement strategies within a steady-state environment. The efficacy of RPSO-SS in finding desired regions of solutions is illustrated using some well-known two and three-objective test problems.
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References
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)
Li, X.: A non-dominated sorting particle swarm optimizer for multiobjective optimization. In: Proceedings of Genetic and Evolutionary Computation Conference 2003 (GECCO 2003), pp. 37–48 (2003)
Li, X.: Better spread and convergence: Particle swarm multiobjective optimization using the maximin fitness function. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 117–128. Springer, Heidelberg (2004)
Deb, K., Chaudhurid, S., Miettinen, K.: Estimating nadir objective vector quickly using evolutionary approaches. Technical Report No. 2005009, KanGAL, Kanpur India (2005)
Corne, D.W., Knowles, J.D.: Techniques for highly multiobjective optimisation: Some nondominated points are better than others. In: Proceedings of Genetic and Evolutionary Computation Conference 2007 (GECCO 2007), pp. 773–780 (2007)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Gembicki, F.W.: Vector optimization for control with performance and parameter sensitivity indices. PhD thesis, Case Western Reserve University, Ohio, USA (1974)
Chankong, V., Haimes, Y.Y.: Multiobjective Decision Making Theory and Methodology. Elsevier Science Publishing Co., Amsterdam (1983)
Wierzbicki, A.P.: The use of reference objectives in multiobjective optimisation. Multiple Criteria Decision Making Theory and Applications, 468–486 (1980)
Mumford-Valenzuela, C.L.: A simple approach to evolutionary multiobjective optimization. Evolutionary Computation Based Multi-Criteria Optimization: Theoretical Advances and Applications, 55–79 (2004)
Coello Coello, C.A.: Handling preferences in evolutionary multiobjective optimization: A survey. In: Proceedings of the 2000 Congress on Evolutionary Computation (CEC 2000), pp. 30–37 (2000)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science (MHS 1995), pp. 39–43 (1995)
Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization method in multiobjective problems. In: Proceedings of the 2002 ACM Symposium on Applied Computing (SAC 2002), pp. 603–607 (2002)
Sierra, M.R., Coello Coello, C.A.: Improving PSO-based multi-objective optimization using crowding, mutation and ε-dominance. In: Proceedings of the Third Evolutionary Multi-Criterion Optimization Conference (EMO 2005), pp. 505–519 (2005)
Kuhn, H.W., Tucker, A.W.: Nonlinear programming. In: Proceedings of Second Berkeley Symposium on Mathematical Statistics and Probability, pp. 481–492 (1950)
Mumford-Valenzuela, C.L.: Simple population replacement strategies for a steady-state multi-objective evolutionary algorithm. In: Proceedings of Genetic and Evolutionary Computation Conference 2004 (GECCO 2004), pp. 1389–1400 (2004)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 173–195 (2002)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. Technical Report No. 112, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2001)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)
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Allmendinger, R., Li, X., Branke, J. (2008). Reference Point-Based Particle Swarm Optimization Using a Steady-State Approach. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_21
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DOI: https://doi.org/10.1007/978-3-540-89694-4_21
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