HANDLING DYNAMIC MULTIOBJECTIVE PROBLEMS WITH PARTICLE SWARM OPTIMIZATION

Alan Díaz Manríquez, Gregorio Toscano Pulido, José Gabriel Ramírez-Torres

Abstract

In this paper the hyperplane distribution and Pareto dominance were incorporated into a particle swarm optimization algorithm in order to allow it to handle dynamic multiobjective problems. When a change in a dynamic multiobjectve function is detected, the proposed algorithm reinitializes (in different ways) the PSO's velocity parameter and the archive where the non-dominated solutions are beeing stored such that the algorithm can follow the dynamic Pareto front. The proposed approach is validated using two dynamic multiobjective test functions and an standard metric taken from the specialized literature. Results indicate that the proposed approach is highly competitive which can be considered as a viable alternative in order to solve dynamic multiobjective optimization problems.

References

  1. Bingul, Z. (2007). Adaptive Genetic Algorithms Applied to Dynamic Multi-ObjectiveProblems. Appl. Soft Comput., 7(3):791-799.
  2. Blinded (2005). Blinded. PhD thesis, Blinded, Blinded.
  3. Deb, K., Agrawal, S., Pratab, A., and Meyarivan, T. (2000). A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. KanGAL report 200001, Indian Institute of Technology, Kanpur, India.
  4. Deb, K., N., U. B. R., and Karthik, S. (2006). Dynamic multi-objective optimization and decision-making using modified NSGA-II: A case study on hydro-thermal power scheduling. In EMO, pages 803-817.
  5. Farina, M., Deb, K., and Amato, P. (2004). Dynamic Multiobjective Optimization Problems: Test Cases, Approximations, and Applications. IEEE Transactions on Evolutionary Computation, 8(5):425-442.
  6. Hatzakis, I. and Wallace, D. (2006). Dynamic MultiObjective Optimization with Evolutionary Algorithms: A Forward-Looking Approach. In et al., M. K., editor, 2006 Genetic and Evolutionary Computation Conference (GECCO'2006), volume 2, pages 1201-1208, Seattle, Washington, USA. ACM Press. ISBN 1-59593-186-4.
  7. Kennedy, J. and Eberhart, R. C. (2001). Swarm intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
  8. Ray, T., Isaacs, A., and Smith, W. (2009). A memetic algorithm for dynamic multiobjective optimization. In Multi-Objective Memetic Algorithms, volume 171 of Studies in Computational Intelligence, pages 353- 367. Springer Berlin / Heidelberg.
  9. Talukder, A. K. A. and Kirley, M. (2008). A pareto following variation operator for evolutionary dynamic multiobjective optimization. In Proceedings of the IEEE Congress on Evolutionary Computation 2008 (CEC 2008), Hong Kong, China. IEEE Press, Piscataway, NJ.
  10. Veldhuizen, D. A. V. and Lamont, G. B. (1998). Multiobjective Evolutionary Algorithm Research: A History and Analysis. Technical Report TR-98-03, Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB, Ohio.
  11. Veldhuizen, D. A. V. and Lamont, G. B. (2000). On Measuring Multiobjective Evolutionary Algorithm Performance. In 2000 Congress on Evolutionary Computation, volume 1, pages 204-211, Piscataway, New Jersey. IEEE Service Center.
  12. Yuping Wang, C. D. (2008). An evolutionary algorithm for dynamic multi-objective optimization. Applied Mathematics and ComputationIn Press.
  13. Zeng, S., Chen, G., Zheng, L., Shi, H., de Garis, H., Ding, L., and Kang, L. (2006). A Dynamic Multi-Objective Evolutionary Algorithm Based on an Orthogonal Design. In 2006 IEEE Congress on Evolutionary Computation (CEC'2006), pages 2588-2595, Vancouver, BC, Canada. IEEE.
  14. Zhou, A., Jin, Y., Zhang, Q., Sendhoff, B., and Tsang, E. (2006). Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization. In Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., and Murata, T., editors, The 4th Int. Conf. on Evolutionary Multi-Criterion Optimization, volume 4403, pages 832-846. Springer.
Download


Paper Citation


in Harvard Style

Díaz Manríquez A., Toscano Pulido G. and Gabriel Ramírez-Torres J. (2010). HANDLING DYNAMIC MULTIOBJECTIVE PROBLEMS WITH PARTICLE SWARM OPTIMIZATION . In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-674-021-4, pages 337-342. DOI: 10.5220/0002734403370342


in Bibtex Style

@conference{icaart10,
author={Alan Díaz Manríquez and Gregorio Toscano Pulido and José Gabriel Ramírez-Torres},
title={HANDLING DYNAMIC MULTIOBJECTIVE PROBLEMS WITH PARTICLE SWARM OPTIMIZATION},
booktitle={Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2010},
pages={337-342},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002734403370342},
isbn={978-989-674-021-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - HANDLING DYNAMIC MULTIOBJECTIVE PROBLEMS WITH PARTICLE SWARM OPTIMIZATION
SN - 978-989-674-021-4
AU - Díaz Manríquez A.
AU - Toscano Pulido G.
AU - Gabriel Ramírez-Torres J.
PY - 2010
SP - 337
EP - 342
DO - 10.5220/0002734403370342