Implementing co-evolution and parallelization in a multi-objective particle swarm optimizer

Engineering Optimization

Volumn 43, Issue 6, 2011, Pages 635-656

  Kotinis, Miltiadis ^a  

^a Old Dominion University   (United States)

Author keywords

co evolution; multi objective optimization; on line adaptation; parallel algorithm; swarm intelligence

Indexed keywords

ADAPTIVE SCHEME; ALGORITHMIC PARAMETERS; CO-EVOLUTION; CO-EVOLUTIONARY ALGORITHM; DESIGN OPTIMIZATION PROBLEM; DISTINCTIVE FEATURES; EXPLORATION AND EXPLOITATION; MULTI OBJECTIVE; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; MULTIPLE SWARMS; ON-LINE ADAPTATION; OPTIMIZERS; PARALLEL EFFICIENCY; PARALLELIZATIONS; PARAMETRIC INVESTIGATIONS; PARETO-OPTIMAL FRONT; PARTICLE SWARM OPTIMIZERS; SEARCH SPACES; SOCIAL COMPONENTS; SWARM INTELLIGENCE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CELLULAR AUTOMATA; PARTICLE SWARM OPTIMIZATION (PSO);

MULTIOBJECTIVE OPTIMIZATION;

EID: 79957469535     PISSN: 0305215X     EISSN: 10290273     Source Type: Journal    
DOI: 10.1080/0305215X.2010.508522     Document Type: Article

References (39)

1. Abbott, J.H. and von Doenhoff, A.E., 1959. Theory of wing sections, NewYork: Dover.
2. Branke, J., Schmeck, H., Deb, K. and Reddy, M., 2004. Parallelizing multi-objective evolutionary algorithms: Cone separation. Proceedings of the 2004 congress on evolutionary computation, 20-23 June, Portland, OR. Piscataway, NJ: IEEE Service Center, Vol. 2, 1952-1957.
3. Chapman, B., Jost, G. and van der Pas, R., 2008. Using OpenMP: Portable shared memory parallel programming. Cambridge, MA: The MIT Press.
4. Clerc, M., 1999. The swarm and the queen:Towards a deterministic and adaptive particle swarm optimization. Proceedings of the 1999 congress on evolutionary computation, 6-9 July,Washington, DC. Piscataway, NJ: IEEE Service Center, Vol. 3, 1951-1957.
5. Clerc, M. andKennedy, J., 2002. The particle swarm -explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6 (1), 58-73. (Pubitemid 34404564)
6. Coello Coello, C.A., Lamont, G.B. and Van Veldhuizen, D.A., 2007. Evolutionary algorithms for solving multi-objective problems. NewYork: Springer Science+Business Media.
7. Coello Coello, C.A. and Reyes Sierra, M., 2003.Aco-evolutionary multi-objective evolutionary algorithm. Proceedings of the 2003 congress on evolutionary computation, 8-12 December, Canberra, Australia. Piscataway, NJ: IEEE Service Center, Vol. 1, 482-489.
8. Coello Coello, C. A., Toscano Pulido, G. and Salazar Lechuga, M., 2004. Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8 (3), 256-279.
9. Deb, K., 2001. Multi-objective optimization using evolutionary algorithms. Chichester, UK:Wiley.
10. Deb, K., Pratap, A. and Meyarivan, T., 2001. Constrained test problems for multi-objective evolutionary optimization. Proceedings of the 1st international conference on evolutionary multi-criterion optimization, 7-9 March, Zurich, Switzerland. Lecture Notes in Computer Science, Vol. 1993. London, UK: Springer-Verlag, 284-298. (Pubitemid 33241394)
11. Deb, K., Pratap,A. and Moitra, S., 2000. Mechanical component design for multiple objectives using elitist non-dominated sorting GA. Proceedings of the parallel problem solving from nature VI, 18-20 September, Paris, France. Lecture Notes in Computer Science, Vol. 1917. Berlin/Heidelberg: Springer-Verlag, 859-868.
12. Deb, K., Thiele, L., Laumanns, M. and Zitzler, E. 2002. Scalable test problems for evolutionary multi-objective optimization. Proceedings of the 2002 congress on evolutionary computation, 12-17 May, Honolulu, HI. Piscataway, NJ: IEEE Service Center, Vol. 1, 825-830.
13. Eberhart, R.C., Simpson, P.K. and Dobbins, R.W., 1996. Computational intelligence PC tools. Boston: Academic Press.
14. Eberhart, R.C. and Shi, Y., 2000. Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the 2000 congress on evolutionary computation, 16-19 July, San Diego, CA. Piscataway, NJ: IEEE Service Center, Vol. 1, 84-88.
15. Goh, C.K., Tan, K.C., Liu, D.S. and Chiam, S.C., 2010. A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design. European Journal of Operational Research, 202 (1), 42-54.
16. Hess, J.L. and Smith, A.M.O., 1967. Calculation of potential flow about arbitrary bodies. Progress in Aerospace Sciences, 8, 1-138.
17. Iorio, A.W. and Li, X., 2004. A cooperative co-evolutionary multiobjective algorithm using non-dominated sorting. Proceedings of the 2004 genetic and evolutionary computation conference, 26-30 June, Seattle,WA. Lecture Notes in Computer Science, Vol. 3102. Berlin/Heidelberg: Springer-Verlag, 537-548. (Pubitemid 39747248)
18. Kennedy, J. 1997. The particle swarm: Social adaptation of knowledge. Proceedings of the IEEE international conference on evolutionary computation, 13-16 April, Indianapolis, IN. Piscataway, NJ: IEEE Service Center, Vol. 1, 303-308.
19. Kita, H., Yabumoto, Y., Mori, N. and Nishikawa, Y., 1996. Multi-objective optimization by means of thermodynamical genetic algorithm. Proceedings of the parallel problem solving from nature IV, 22-26 September, Berlin, Germany. Lecture Notes in Computer Science, Vol. 1141. Berlin/Heidelberg: Springer-Verlag, 504-512. (Pubitemid 126128338)
20. Kumar, V., Grama, A., Gupta, A. and Karypis, G., 1994. Introduction to parallel computing: Design and analysis of algorithms. Redwood City, CA: Benjamin/Cummings Publishing Company.
21. Kursawe, F., 1990. A variant of evolution strategies for vector optimization. Proceedings of the parallel problem solving from nature I, 1-30 October, Dortmund, Germany. Lecture Notes in Computer Science,Vol. 496. Berlin/Heidelberg: Springer-Verlag, 193-197.
22. Li, X., 2003.A non-dominated sorting particle swarm optimizer for multiobjective optimization. Proceedings of the 2003 genetic and evolutionary computation conference, 12-16 July, Chicago, IL. Lecture Notes in Computer Science,Vol. 2723. Berlin/Heidelberg: Springer-Verlag, 37-48.
23. McKay, M.D., Beckman R.J. and Conover W.J., 1979. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21 (2), 239-245. (Pubitemid 9452235)
24. Mostaghim, S. and Teich, J., 2004. Covering Pareto-optimal fronts by subswarms in multi-objective particle swarm optimization. Proceedings of the 2004 congress on evolutionary computation, 20-23 June, Portland, OR. Piscataway, NJ: IEEE Service Center, Vol. 2, 1404-1411.
25. Neef, M., Thierens, D. and Arciszewski, H., 1999.A case study of a multiobjective recombinative genetic algorithm with co-evolutionary sharing. Proceedings of the 1999 congress on evolutionary computation, 6-9 July,Washington, DC. Piscataway, NJ: IEEE Service Center, Vol. 1, 796-803.
26. Osyczka, A. and Kundu, S., 1995.A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm. Structural Optimization, 10, 94-99.
27. Parmee, I.C. and Watson., A. H., 1999. Preliminary airframe design using co-evolutionary multiobjective genetic algorithms. Proceedings of the genetic and evolutionary computation conference, 13-17 July, Orlando, FL. San Francisco, CA: Morgan Kaufmann, Vol. 2, 1657-1665.
28. Raquel, C.R. and Naval, P.C., Jr., 2005.Aneffective use of crowding distance in multiobjective particle swarm optimization. Proceedings of the 7th annual genetic and evolutionary computation conference, 25-29 July,Washington, DC. New York: ACM Press, 257-264. (Pubitemid 43226295)
29. Ray, T. and Liew, K.M., 2002. A swarm metaphor for multiobjective design optimization. Engineering Optimization, 34 (2), 141-153. (Pubitemid 35263146)
30. Reyes Sierra, M. and Coello Coello, C.A., 2006. On-line adaptation in multi-objective particle swarm optimization. Proceedings of the IEEE swarm intelligence symposium 2006, 12-14 May, Indianapolis, IN. Piscataway, NJ: IEEE Press, 61-68.
31. Santana-Quintero, L.V., Ramírez-Santiago, N. and Coello Coello C.A., 2008. Towards a More Efficient Multi-Objective Particle Swarm Optimizer. In: L.T. Bui and S. Alam, eds. Multi-objective optimization in computational intelligence: Theory and Practice. Hershey, PA: Information Science Reference, 76-105.
32. Shi, Y. and Eberhart, R.C., 1998. Parameter selection in particle swarm optimization. In V.W. Porto, N. Saravanan, D. Waagen and A.E. Eiben, eds. Proceedings of the 7th annual conference on evolutionary programming, 25-27 March, San Diego, CA. Berlin: Springer-Verlag, 591-600. (Pubitemid 128118969)
33. Tan, K.C., Yang, Y.J. and Goh, C.K., 2006. A distributed cooperative co-evolutionary algorithm for multiobjective optimization. IEEE Transactions on Evolutionary Computation, 10 (2), 527-549. (Pubitemid 44560741)
34. Tocano Pulido, G. and Coello Coello, C.A., 2004. Using clustering techniques to improve the performance of a particle swarm optimizer. Proceedings of the 2004 genetic and evolutionary computation conference, 26-30 June, Seattle, WA. Lecture Notes in Computer Science, Vol. 3102. Berlin/Heidelberg: Springer-Verlag, 225-237. (Pubitemid 39747212)
35. Toscano Pulido, G., Coello Coello C.A. and Santana-Quintero, L.V., 2007. EMOPSO: A multi-objective particle swarm optimizer with emphasis on efficiency. In: S. Obayashi, K. Deb, C. Poloni, T. Hiroyasu and T. Murata, eds. Evolutionary multi-criterion optimization: 4th international conference, 5-8 March, Matshushima, Japan. Berlin/Heidelberg: Springer-Verlag, 272-285. (Pubitemid 350284280)
36. Van Veldhuizen, D.A., 1999. Multiobjective evolutionary algorithms: Classifications, analyses, and new innovations. Thesis (PhD). Air Force Institute of Technology, Dayton, OH.
37. Van Veldhuizen, D.A., Zydallis, J.B. and Lamont, G.B. 2003. Considerations in engineering parallel multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 7 (2), 144-173.
38. Zhang, Q., Zhou, A., Zhao, S., Suganthan, P. N., Liu,W. and Tiwari, S., 2008. Multiobjective optimization test instances for the CEC 2009 special session and competition. Technical report, CES-487, School of Computer Science and Electrical Engineering, University of Essex.
39. Zitzler, E., Deb, K. and Thiele, L., 2000. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation Journal, 8 (2), 125-148.