Reference point-based particle swarm optimization using a steady-state approach

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5361 LNAI, Issue , 2008, Pages 200-209

  Allmendinger, Richard ^a   Li, Xiaodong ^b   Branke, Jürgen ^a  

^a UNIVERSITY OF KARLSRUHE   (Germany)

^b RMIT UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DATA FUSION; MULTIOBJECTIVE OPTIMIZATION; OPTIMAL SYSTEMS; OPTIMIZATION; SENSOR DATA FUSION; WIRELESS TELECOMMUNICATION SYSTEMS;

COMPUTATIONALLY EXPENSIVE; OPTIMAL SOLUTIONS; PARTICLE SWARMS; PREFERRED SOLUTIONS; PSO ALGORITHMS; REFERENCE POINTS; REPLACEMENT STRATEGIES; TEST PROBLEMS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 58349116514     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-89694-4_21     Document Type: Conference Paper

References (19)

1. 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)
2. 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)
3. 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)
4. Deb, K., Chaudhurid, S., Miettinen, K.: Estimating nadir objective vector quickly using evolutionary approaches. Technical Report No. 2005009, KanGAL, Kanpur India (2005)
5. 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)
6. 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)
7. Gembicki, F.W.: Vector optimization for control with performance and parameter sensitivity indices. PhD thesis, Case Western Reserve University, Ohio, USA (1974)
8. Chankong, V., Hahnes, Y.Y.: Multiobjective Decision Making Theory and Methodology. Elsevier Science Publishing Co., Amsterdam (1983)
9. Wierzbicki, A.P.: The use of reference objectives in multiobjective optimisation. Multiple Criteria Decision Making Theory and Applications, 468-486 (1980)
10. Mumford-Valenzuela, C.L.: A simple approach to evolutionary multiobjective optimization. Evolutionary Computation Based Multi-Criteria Optimization: Theoretical Advances and Applications, 55-79 (2004)
11. 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)
12. 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)
13. 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)
14. Sierra, M.R., Coello Coello, C.A.: Improving PSO-based multi-objective optimization using crowding, mutation and e-dominance. In: Proceedings of the Third Evolutionary Multi-Criterion Optimization Conference (EMO 2005), pp. 505-519 (2005)
15. Kuhn, H.W., Tucker, A.W.: Nonlinear programming. In: Proceedings of Second Berkeley Symposium on Mathematical Statistics and Probability, pp. 481-492 (1950)
16. 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)
17. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 173-195 (2002)
18. 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)
19. 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)