CYLINDRICAL CONSTRAINT EVOLUTIONARY ALGORITHM FOR MULTIOBJECTIVE OPTIMIZATION

Tohid Erfani, Sergei V. Utyuzhnikov

2011

Abstract

This paper introduces a new iterative evolutionary algorithm, which is able to provide an evenly distributed set of solutions in multiobjective context. The method is different from the other evolutionary algorithms in two perspectives. First, instead of density information incorporated to find a diverse set of solutions, a hypercylinder is introduced as a new constraint to the problem. Searching for the solution within this hypercylinder guarantees the evenly generated solutions at the end of the optimization process. Second, a fitness function is constructed to handle the problem constraints and meanwhile minimize the distance of the solution to the true optimum frontier. In addition, the method is developed in such a way that it can be easily implemented in searching the preferable region of the search space. The algorithm behaviour is tested on different test cases and the results are compared in both convergence and diversity to those of other well known approaches to demonstrate the efficacy of the proposed method.

References

  1. Boyd, S. and Vandenberghe, L. (2004). Convex optimization. Cambridge Univ Pr.
  2. Das, I. and Dennis, J. (1998). Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM Journal on Optimization, 8:631.
  3. Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Wiley.
  4. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2):182-197.
  5. Deb, K., Thiele, L., Laumanns, M., and Zitzler, E. (2005). Scalable test problems for evolutionary multiobjective optimization. Evolutionary Multiobjective Optimization, pages 105-145.
  6. Erfani, T. and Utyuzhnikov, S. (2010). Directed search domain: a method for even generation of the Pareto frontier in multiobjective optimization. Engineering Optimization, page DOI:10.1080/0305215X.2010.497185.
  7. Haupt, R., Haupt, S., and Wiley, J. (1998). Practical genetic algorithms. Wiley Online Library.
  8. Jahn, J. (2004). Vector optimization: theory, applications, and extensions. Springer Verlag.
  9. Messac, A., Ismail-Yahaya, A., and Mattson, C. (2003). The normalized normal constraint method for generating the Pareto frontier. Structural and Multidisciplinary Optimization, 25(2):86-98.
  10. Zitzler, E., Laumanns, M., Thiele, L., et al. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. In Eurogen, volume 3242. Citeseer.
  11. Zitzler, E. and Thiele, L. (2002). Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. evolutionary computation, IEEE transactions on, 3(4):257-271.
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Paper Citation


in Harvard Style

Erfani T. and V. Utyuzhnikov S. (2011). CYLINDRICAL CONSTRAINT EVOLUTIONARY ALGORITHM FOR MULTIOBJECTIVE OPTIMIZATION . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011) ISBN 978-989-8425-83-6, pages 184-189. DOI: 10.5220/0003670101840189


in Bibtex Style

@conference{ecta11,
author={Tohid Erfani and Sergei V. Utyuzhnikov},
title={CYLINDRICAL CONSTRAINT EVOLUTIONARY ALGORITHM FOR MULTIOBJECTIVE OPTIMIZATION},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011)},
year={2011},
pages={184-189},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003670101840189},
isbn={978-989-8425-83-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011)
TI - CYLINDRICAL CONSTRAINT EVOLUTIONARY ALGORITHM FOR MULTIOBJECTIVE OPTIMIZATION
SN - 978-989-8425-83-6
AU - Erfani T.
AU - V. Utyuzhnikov S.
PY - 2011
SP - 184
EP - 189
DO - 10.5220/0003670101840189