Copyright and Licensing
Articles accepted for publication will be licensed under the Creative Commons BY-NC-SA. Authors must sign a non-exclusive distribution agreement after article acceptance.
A particle swarm optimizer for multi-objective optimization
Keywords:
Particle Swarm Optimization, Multi-objective Optimization, Pareto OptimalityAbstract
This paper proposes a hybrid particle swarm approach called Simple Multi-Objective Particle Swarm Optimizer (SMOPSO) which incorporates Pareto dominance, an elitist policy, and two techniques to maintain diversity: a mutation operator and a grid which is used as a geographical location over objective function space. In order to validate our approach we use three well-known test functions proposed in the specialized literature. Preliminary simulations results are presented and compared with those obtained with the Pareto Archived Evolution Strategy (PAES) and the Multi-Objective Genetic Algorithm 2 (MOGA2). These results also show that the SMOPSO algorithm is a promising alternative to tackle multiobjective optimization problems.
Downloads
Download data is not yet available.
References
[1] K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: Nsgaii. In Parallel Problem Solving from Nature, PPSN VI, pages 849–858. Springer, 2000.
[2] J. Schaffer. Multiple objective optimization with vector evaluated genetic algorithms. In First International Conference on Genetic Algorithms, pages 99–100, 1985.
[3] E. Zitzler, K. Deb, and L. Thiele. Comparision of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2):173–195, 2000.
[4] J. Knowles and D. Corne. Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation, 8(2):149–172, 2000.
[5] M. Laumanns, E. Zitzler, and L. Thiele. A unified model for multi-objective evolutionary algorithms with elitism. In Congress on Evolutionary Computation, pages 46–53, Piscataway, NJ, 2000. IEEE Service Center.
[6] J. Kennedy and R. Eberhart. Swarm Intelligence. Morgan Kaufmann Publishers, California, USA, 2001.
[7] J. Kennedy and R. Mendes. Population structure and particle swarm performance. In Congress on Evolutionary Computation, volume 2, pages 1671–1676, Piscataway, NJ, May 2002. IEEE Sevice Center.
[8] J. Kennedy and R. Eberhart. Particle swarm optimization. In International Conference on Neural Networks, pages 1942–1948, Piscataway, NJ., 1995. IEEE Sevice Center.
[9] C. Coello Coello and M. Lechuga. Mopso: A proposal for multiple objective particle swarm optimization. In Congress on Evolutionary Computation, pages 1051–1056, Piscataway, NJ., 2002. IEEE Service Center.
[10] J. Knowles and D. Corne. M-paes: A memetic algorithm for multiobjective optimization. In Congress on Evolutionary Computation, pages 325–332, Piscataway, NJ, 2000. IEEE Service Center.
[11] X. Hu and R. Eberhart. Multiobjective optimization using dynamic neigborhood particle swarm optimization. In Congress on Evolutionary Computation, pages 1677–1681, Piscataway, NJ., 2002. IEEE Service Center.
[12] T. Ray and K. Liew. A swarm metaphor for multiobjective design optimization. Engineering Optimization, 34(2):141–153, 2002.
[13] K. Parsopoulos, T. Bartz, and Vrahatis. Particle swarm optimizers for pareto optimization with enhanced archiving techniques. In Congress on Evolutionary Computation, pages 1780–1787, Piscatawy, NJ., 2003. IEEE Service Center.
[14] C. Coello Coello and G. Toscano Pulido. Using clustering techniques to improve the performance of a multi-objective particle swarm optimizer. In Genetic and Evolutionary Computation Conference, volume 3102 of Lecture Notes in Computer Science. Springer Verlag, 2004.
[15] T. Bäck, D. Fogel, and Z. Michalewicz, editors. Handandbook of Evolutionary Computation. IOP Publishing Ltd and Oxford University Press, 1997.
[16] K. Deb. Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Ltd., England, 2001.
[17] C. Coello Coello, D. Van Vedhuizen, and G. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York, USA, 2002.
[18] D. Veldhuizen. Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. PhD thesis, Air Force Institute of Technology, Dayton, 1999.
[19] J. Schott. Fault tolerant design using single and multi-criteria genetic algorithms. Master’s thesis, Massachusetts Institute of Technology, Boston, 1995.
[20] C. Fonseca and P. Fleming. Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In Fifth International Conference
on Genetic Algorithms, pages 416–423, San Mateo, California, 1993. Morgan Kaufmann Publishers.
[21] K. Deb, S. Agrawal, A. Pratab, and T. Meyarivan. A fast elitist non-dominated sorting genetic algorithm for multiobjective optimization: Nsga ii. Technical Report 200001, Indian Institute of Technology, Kanpur, India, 2000.
[22] K. Deb, S. Agrawal, A. Pratab, and T. Meyarivan. A fast elitist non-dominated sorting genetic algorithm for multiobjective optimization: Nsga ii. In Schoenauer M. et al., editor, Parallel Problem Solving from Nature, Lecture Notes in Computer Sciences 1917, pages 849–858. Springer, 2000.
[23] C. Coello Coello and G. Toscano Pulido. Multiobjective optimization using a micro genetic algorithm. In Goodman L. et al., editor, Genetic and Evolutionary Conference, pages 274–282, San Francisco, California, 2001. Morgan Kaufmann Publishers.
[2] J. Schaffer. Multiple objective optimization with vector evaluated genetic algorithms. In First International Conference on Genetic Algorithms, pages 99–100, 1985.
[3] E. Zitzler, K. Deb, and L. Thiele. Comparision of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2):173–195, 2000.
[4] J. Knowles and D. Corne. Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation, 8(2):149–172, 2000.
[5] M. Laumanns, E. Zitzler, and L. Thiele. A unified model for multi-objective evolutionary algorithms with elitism. In Congress on Evolutionary Computation, pages 46–53, Piscataway, NJ, 2000. IEEE Service Center.
[6] J. Kennedy and R. Eberhart. Swarm Intelligence. Morgan Kaufmann Publishers, California, USA, 2001.
[7] J. Kennedy and R. Mendes. Population structure and particle swarm performance. In Congress on Evolutionary Computation, volume 2, pages 1671–1676, Piscataway, NJ, May 2002. IEEE Sevice Center.
[8] J. Kennedy and R. Eberhart. Particle swarm optimization. In International Conference on Neural Networks, pages 1942–1948, Piscataway, NJ., 1995. IEEE Sevice Center.
[9] C. Coello Coello and M. Lechuga. Mopso: A proposal for multiple objective particle swarm optimization. In Congress on Evolutionary Computation, pages 1051–1056, Piscataway, NJ., 2002. IEEE Service Center.
[10] J. Knowles and D. Corne. M-paes: A memetic algorithm for multiobjective optimization. In Congress on Evolutionary Computation, pages 325–332, Piscataway, NJ, 2000. IEEE Service Center.
[11] X. Hu and R. Eberhart. Multiobjective optimization using dynamic neigborhood particle swarm optimization. In Congress on Evolutionary Computation, pages 1677–1681, Piscataway, NJ., 2002. IEEE Service Center.
[12] T. Ray and K. Liew. A swarm metaphor for multiobjective design optimization. Engineering Optimization, 34(2):141–153, 2002.
[13] K. Parsopoulos, T. Bartz, and Vrahatis. Particle swarm optimizers for pareto optimization with enhanced archiving techniques. In Congress on Evolutionary Computation, pages 1780–1787, Piscatawy, NJ., 2003. IEEE Service Center.
[14] C. Coello Coello and G. Toscano Pulido. Using clustering techniques to improve the performance of a multi-objective particle swarm optimizer. In Genetic and Evolutionary Computation Conference, volume 3102 of Lecture Notes in Computer Science. Springer Verlag, 2004.
[15] T. Bäck, D. Fogel, and Z. Michalewicz, editors. Handandbook of Evolutionary Computation. IOP Publishing Ltd and Oxford University Press, 1997.
[16] K. Deb. Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Ltd., England, 2001.
[17] C. Coello Coello, D. Van Vedhuizen, and G. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York, USA, 2002.
[18] D. Veldhuizen. Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. PhD thesis, Air Force Institute of Technology, Dayton, 1999.
[19] J. Schott. Fault tolerant design using single and multi-criteria genetic algorithms. Master’s thesis, Massachusetts Institute of Technology, Boston, 1995.
[20] C. Fonseca and P. Fleming. Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In Fifth International Conference
on Genetic Algorithms, pages 416–423, San Mateo, California, 1993. Morgan Kaufmann Publishers.
[21] K. Deb, S. Agrawal, A. Pratab, and T. Meyarivan. A fast elitist non-dominated sorting genetic algorithm for multiobjective optimization: Nsga ii. Technical Report 200001, Indian Institute of Technology, Kanpur, India, 2000.
[22] K. Deb, S. Agrawal, A. Pratab, and T. Meyarivan. A fast elitist non-dominated sorting genetic algorithm for multiobjective optimization: Nsga ii. In Schoenauer M. et al., editor, Parallel Problem Solving from Nature, Lecture Notes in Computer Sciences 1917, pages 849–858. Springer, 2000.
[23] C. Coello Coello and G. Toscano Pulido. Multiobjective optimization using a micro genetic algorithm. In Goodman L. et al., editor, Genetic and Evolutionary Conference, pages 274–282, San Francisco, California, 2001. Morgan Kaufmann Publishers.
Downloads
Published
2005-12-01
How to Cite
Cagnina, L., Esquivel, S. C., & Coello Coello, C. (2005). A particle swarm optimizer for multi-objective optimization. Journal of Computer Science and Technology, 5(04), p. 204–210. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/837
Issue
Section
Original Articles
