The week's pick
Random Articles
Reseach Article
Multi-Objective Particle Swarm Optimization (MOPSO) based on Pareto Dominance Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 107 - Number 4 |
| Year of Publication: 2014 |
| Authors: Dipti D. Patil, Bhagyashri D. Dangewar |
10.5120/18738-9983
|
Dipti D. Patil, Bhagyashri D. Dangewar . Multi-Objective Particle Swarm Optimization (MOPSO) based on Pareto Dominance Approach. International Journal of Computer Applications. 107, 4 ( December 2014), 13-15. DOI=10.5120/18738-9983
Abstract
This paper presents a comprehensive review of a multi-objective particle swarm optimization (MOPSO) reported in the specialized literature. The success of the Particle Swarm Optimization (PSO) algorithm as a single-objective optimizer has motivated researchers to extend the use of bio-inspired technique to other areas. One of them is multi-objective optimization. Multi-objective optimization is a class of problems with solutions that can be evaluated along two or more incomparable or conflicting objectives. These types of problems differ from standard optimization problems in that the end result is not a single est solution" but rather a set of alternatives, where for each member of the set, no other solution is completely better (the Pareto set). Multi-objective optimization problems occur in many different real-world domains such as automobile design and architecture. A multi-objective particle swarm optimization (MOPSO) method can be used to solve the problem of effective channel selection.
References
- K. Deb, A. Pratap, S. Agarwal, and T. Meyari- van. "A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation", IEEE Transactions on, 6(2):182{197, 2002.
- M. Reyes-Sierra and C. Coello. "Multi-objective particle swarm optimizers: A survey of the state-of-the-art. International Journal of Computational Intelligence Research" , 2(3):287{308, 2006
- Zitzler, M. Laumanns, and L. Thiele . "SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multi-objective Optimization", In EUROGEN 2001. CIMNE, 2002.
- Alvarez-Benitez, J. E. , Everson, R. M. , & Fieldsend, J. E. "A MOPSO algorithm based exclusively on Pareto dominance concepts" Lecture notes in computer science Springer Verlag, (Vol. 3410, pp. 459-473), 2005.
- Srinivas, N. , Deb, "Multi-objective Optimization Using Non-dominated Sorting in Genetic Algorithms", IEEE Transactions on Evolutionary Computation 2(3) (1995) 221–248
- Zitzler, E. , Thiele, " Multi-objective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach", IEEE Transactions on Evolution- ary Computation 3(4) (1999) 257–271.
- Hussein A. Abbass. "The self-adaptive pareto differential evolution algorithm" In Congress on Evolutionary Computation (CEC'2002), volume 1, pages 831–836, Piscataway, New Jersey, IEEE Service Center, May 2002.
- Julio E. Alvarez-Benitez, Richard M. Everson, and Jonathan E. Fieldsend. "A MOPSO algorithm based exclusively on pareto dominance concepts", In Third International Conference on Evolutionary Multi-Criterion Optimization, EMO 2005. , pages 459–473, Guanajuato, M´exico, 2005. LNCS 3410, Springer-Verlag.
- Peter J. Angeline, "Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences" In V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, editors, Evolutionary Programming. VII. 7th International Conference, EP 98, pages 601 610. Springer. Lecture Notes in Computer Science Vol. 1447, San Diego, California, USA, March 1998.
- Richard Balling. "The maximin fitness function; multi-objective city and regional planning", In Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele, editors, Second International Conference on Evolutionary Multi-Criterion Optimization, EMO 2003, pages 1–15, Faro, Portugal, April 2003.
- Thomas Bartz-Beielstein, Philipp Limbourg, Konstantinos E. Parsopoulos, Michael N. Vrahatis, J¨orn Mehnen, and Karlheinz Schmitt. "Particle swarm optimizers for pareto optimization with enhanced archiving techniques", In Congress on Evolutionary Computation (CEC'2003), volume 3, pages 1780–1787, Canberra, Australia, IEEE Press, December 2003
- U. Baumgartner, Ch. Magele, and W. Renhart. Pareto optimality and particle swarm optimization. IEEE Transactions on Magnetics, 40(2):1172–1175, March 2004.
- Dirk B¨uche, Sibylle M¨uller, and Petro Koumoutsakos. "Self-adaptation for multi-objective evolutionary algorithms" Second International Conference on Evolutionary Multi-Criterion Optimization, EMO ,pages 267–281, Faro, Portugal, April 2003.
- Chi-kin Chow and Hung-tat Tsui. "Autonomous agent response learning by a multi-species particle swarm optimization", In Congress on Evolutionary Computation (CEC'2004), volume 1, pages 778–785, June 2004.
- Maurice Clerc and James Kennedy. "The particle swarm–explosion, stability, and convergence in a multidimensional complex space", IEEE Transactions on Evolutionary Computation, 6(1):58–73, February 2002.
- Carlos A. Coello Coello. "A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information Systems", An International Journal, 1(3):269–308, August 1999.
- Carlos A. Coello Coello and Maximino Salazar Lechuga. "MOPSO: A proposal for multiple objective particle swarm optimization", In Congress on Evolutionary Computation (CEC'2002), volume 2, pages 1051– 1056, Piscataway, New Jersey, May 2002
- Carlos A. Coello Coello, Gregorio Toscano Pulido, and Maximino Salazar Lechuga. "Handling multiple objectives with particle swarm optimization", IEEE Transactions on Evolutionary Computation, 8(3):256–279, June 2004.
Index Terms
Keywords