Pareto Multiobjective Optimization

Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems

Volumn , Issue , 2007, Pages 189-207

  Ngatchou, Patrick N ^a   Zarei, Anahita ^b   Fox, Warren L J ^c   El Sharkawi, Mohamed A ^b  

^a Intermolecular Inc   (United States)

^b University of Washington   (United States)

^c UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Multicriteria decision making (MCDM); Pareto optimality concepts; Single objective (SO) optimization problems

Indexed keywords

EID: 84889421723     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470225868.ch10     Document Type: Chapter

References (27)

1. Stadler W. Multicriteria optimization in engineering and in the sciences. New York: Plenum Press; 1988.
2. Tabucanon MT. Multiple criteria decision making industry. Amsterdam: Elsevier Science; 1988.
3. Coello Coello CA, Van Veldhuizen DA, Lamont GB. Evolutionary algorithms for solving multi-objective problems. New York: Kluwer Academic Publishers; 2002.
4. Bagchi TP. Multiobjective scheduling by genetic algorithms. Boston: Kluwer Academic Publishers; 1999.
5. Abido MA. Environmental/economic power dispatch using multiobjective evolutionary algorithms: a comparative stu. In: 2003 IEEE Power Engineering Society General Meeting, 13-17 July 2003, Toronto, Canada. IEEE: 2003.
6. Begovic M, Radibratovic B, Lambert F. On multiple Volt-VAR optimization in power system. Proceedings of the 37th Hawaii International Conference on System Sciences, 2004.
7. Krami N, El-Sharkawi MA, Akherraz M. Multi-objective particle swarm optimization for reactive power planning. Accepted at the IEEE Swarm Intelligence Symposium, Indianapolis, 2006.
8. Jin Y, Olhofer M, Sendhoff B. Dynamic weighted aggregation for evolutionary multiobjective optimization: why does it work and how? Proc. Genetic and Evolutionary Computation Conf. (GECCO), 2001.
9. Parsopoulos KE, Vrahatis MN. Particle swarm optimization method in multiobjective problems. In: Proc. of the 2002 ACM Symposium on Applied Computing (SAC'02), Madrid, Spain. ACM Press: New York; 2002. p. 603-607.
10. Coello CAC. A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information Systems 1999; 1(3):269-308.
11. Zitzler E, Deb K, Thiele L. Comparison of multiobjective evolutionary algorithms: empirical results. Evolutionary Computation 2000; 8(2):149-172.
12. Jones DF, Mirrazavi SK, Tamiz M. Multi-objective meta-heuristics: an overview of the current state-of-the-art. Eur J Operational Res 2002; 137(1):1-9.
13. Eiben AE, Smith JE. Introduction to evolutionary computing. Berlin: Springer; 2003.
14. Schaffer JD. Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the International Conference on Genetic Algorithms and Their Applications, Pittsburgh, IEEE; July 24-26, 1985. p. 93-100.
15. Fonseca C, Fleming PJ. Genetic algorithms for multiobjective optimization: formulation, discussion, generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, CA, IEEE; 1993. p. 416-423.
16. Zitzler E, Thiele L. An evolutionary algorithm for multiobjective optimization: the strength Pareto approac. TIK Tech. Report No. 43, Swiss Federal Institute of Technology (ETH); 1998.
17. Srinivas N, Deb K. Multiobjective function optimization using nondominated sorting genetic algorithms. Evolutionary Computation 1994; 2(3):221-248.
18. Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Computat 2002; 6(2):182-197.
19. Horn J, Nafpliotis N, Goldberg DE. A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, IEEEWorld Congress on Computational Intelligence, Piscataway, NJ. Vol. 1. IEEE; 1994, p. 82-87.
20. Zitzler E, Laumanns M, Thiele L. SPEA2: improving the strength Pareto evolutionary algorith. TIK Report No. 103, ETH Zurich; 2001.
21. Knowles J, Corne D. The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation. In: Proceedings of the 1999 Congress on Evolutionary Computation, IEEE; 1999, Vol. 1, p. 98-105.
22. Kennedy J, Eberhart RC. Swarm intelligence. Morgan Kaufmann; 2001.
23. Song M-P, Gu G-C. Research on particle swarm optimization: a review. In: Proceedings of the Third International Conference on Machine Learning and Cybernetics. IEEE; 2004; p. 2216-2241.
24. Coello Coello CA, Pulido GT, Lechuga MS. Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Computat 2004; 8(3):256-279.
25. Knowles JD, Thiele L, Zitzler E. A tutorial on the performance assessment of stochastic multiobjective optimizers. TIK Report No. 214, ETH Zurich; July 2005.
26. da Fonseca VG, Fonseca CM, Hall AO. Inferential performance assessment of stochastic optimisers and the attainment function. Lecture Notes in Computer Science, Volume 1993, IEEE; Jan 2001, p. 213-225.
27. Knowles JD. A summary-attainment-surface plotting method for visualizing the performance of stochastic multiobjective optimizers. Proceedings 5th International Conference on Intelligent Systems Design and Applications, IEEE; 2005. p. 552-557.