A Peer-to-Peer Dynamic Multi-objective Particle Swarm Optimizer

  • Hrishikesh Dewan
  • Raksha B. Nayak
  • V. Susheela Devi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)

Abstract

Multi-objective optimization problem is an important part in solving a wide number of engineering and scientific applications. To-date, most of the research has been conducted in solving static multi-objective problems where the decision variables and/or the objective functions do not change over a period of time. In a dynamic environment, the particles non dominated solution set during a specific iteration may no longer be valid due to change in the underlying system. As a result, traditional techniques for solving static multi-objective functions cannot be applied for solving dynamic multi-objective functions. Further, with the increase in the number of variables/objective functions, a single system based optimizer will take a long time to compute the non-dominated solution set. In this paper, we present a peer-to-peer distributed particle swarm optimization algorithm that tracks the change in the underlying system and is able to produce a diversified and dense non- dominated set using a network of peer-to-peer system. Our algorithms are tested using a set of known benchmark problems and results are reported. To our knowledge, this algorithm is the first of its kind in the areas of peer-to-peer particle swarm optimization.

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References

  1. 1.
    Helbig, M.: Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation. PhD thesis, University of Pretoria (2012)Google Scholar
  2. 2.
    Wang, Y., Li, B.: Investigation of memory-based multi-objective optimization evolutionary algorithm in dynamic environment. In: IEEE Congress on Evolutionary Computation, CEC 2009, pp. 630–637. IEEE (2009)Google Scholar
  3. 3.
    Tang, M., Huang, Z., Chen, G.: The construction of dynamic multi-objective optimization test functions. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 72–79. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93–100. L. Erlbaum Associates Inc. (1985)Google Scholar
  5. 5.
    Wang, Y., Dang, C.: An evolutionary algorithm for dynamic multi-objective optimization. Applied Mathematics and Computation 205(1), 6–18 (2008)CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    Shang, R., Jiao, L., Gong, M., Lu, B.: Clonal selection algorithm for dynamic multiobjective optimization. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y.-m., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 846–851. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Zeng, S.Y., Chen, G., Zheng, L., Shi, H., de Garis, H., Ding, L., Kang, L.: A dynamic multi-objective evolutionary algorithm based on an orthogonal design. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 573–580. IEEE (2006)Google Scholar
  8. 8.
    Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report 826 (1989)Google Scholar
  9. 9.
    Zhang, Z., Qian, S.: Artificial immune system in dynamic environments solving time-varying non-linear constrained multi-objective problems. Soft Computing 15(7), 1333–1349 (2011)CrossRefGoogle Scholar
  10. 10.
    Cámara, M., Ortega, J., Toro, F.J.: Parallel processing for multi-objective optimization in dynamic environments. In: IEEE International Parallel and Distributed Processing Symposium, IPDPS 2007, pp. 1–8. IEEE (2007)Google Scholar
  11. 11.
    Cámara, M., Ortega, J., de Toro, F.: Approaching dynamic multi-objective optimization problems by using parallel evolutionary algorithms. In: Coello Coello, C.A., Dhaenens, C., Jourdan, L. (eds.) Advances in Multi-Objective Nature Inspired Computing. SCI, vol. 272, pp. 63–86. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Ruiz, I.R.: Sinta-cc: Adaptive intelligent systems for modelling, prediction and dynamic optimization in clusters of computers tin2004-01419Google Scholar
  13. 13.
    Stoica, I., Morris, R., Karger, D., Kaashoek, M.F., Balakrishnan, H.: Chord: A scalable peer-to-peer lookup service for internet applications. ACM SIGCOMM Computer Communication Review 31, 149–160 (2001)CrossRefGoogle Scholar
  14. 14.
    Rowstron, A., Druschel, P.: Pastry: Scalable, decentralized object location, and routing for large-scale peer-to-peer systems. In: Guerraoui, R. (ed.) Middleware 2001. LNCS, vol. 2218, pp. 329–350. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  15. 15.
    Farina, M., Deb, K., Amato, P.: Dynamic multiobjective optimization problems: test cases, approximations, and applications. IEEE Transactions on Evolutionary Computation 8(5), 425–442 (2004)CrossRefGoogle Scholar
  16. 16.
    Koo, W.T., Goh, C.K., Tan, K.C.: A predictive gradient strategy for multiobjective evolutionary algorithms in a fast changing environment. Memetic Computing 2(2), 87–110 (2010)CrossRefGoogle Scholar
  17. 17.
    Goh, C.-K., Tan, K.C.: A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation 13(1), 103–127 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Hrishikesh Dewan
    • 1
    • 2
  • Raksha B. Nayak
    • 2
  • V. Susheela Devi
    • 1
  1. 1.Department of Computer Science & AutomationIndian Institute of ScienceBangaloreIndia
  2. 2.Knowledge & Innovation, Siemens Corporate Technology & Development CenterBangaloreIndia

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