Skip to main content
SpringerLink
Log in

An Artificial Fish Swarm Filter-Based Method for Constrained Global Optimization

  • Conference paper
Computational Science and Its Applications – ICCSA 2012 (ICCSA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7335))

Included in the following conference series:

Abstract

An artificial fish swarm algorithm based on a filter methodology for trial solutions acceptance is analyzed for general constrained global optimization problems. The new method uses the filter set concept to accept, at each iteration, a population of trial solutions whenever they improve constraint violation or objective function, relative to the current solutions. The preliminary numerical experiments with a well-known benchmark set of engineering design problems show the effectiveness of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aguirre, A.H., Rionda, S.B., Coello Coello, C.A., Lizárraga, G.L., Montes, E.M.: Handling constraints using multiobjective optimization concepts. International Journal for Numerical Methods in Engineering 59, 1989–2017 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  2. Akhtar, S., Tai, K., Tay, T.: A socio-behavioural simulation model for engineering design optimization. Engineering Optimization 34, 341–354 (2002)

    Article  Google Scholar 

  3. Ali, M.M., Golalikhani, M.: An electromagnetism-like method for nonlinearly constrained global optimization. Computers and Mathematics with Applications 60, 2279–2285 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  4. Audet, C., Dennis Jr., J.E.: A pattern search filter method for nonlinear programming without derivatives. SIAM Journal on Optimization 14(4), 980–1010 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  5. Azad, M.A.K., Fernandes, E.M.G.P., Rocha, A.M.A.C.: Nonlinear continuous global optimization by modified differential evolution. In: Simos, T.E., et al. (eds.) International Conference of Numerical Analysis and Applied Mathematics 2010, vol. 1281, pp. 955–958 (2010)

    Google Scholar 

  6. Azad, M. A.K., Fernandes, E.M.G.P.: Modified Differential Evolution Based on Global Competitive Ranking for Engineering Design Optimization Problems. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011, Part III. LNCS, vol. 6784, pp. 245–260. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  7. Barbosa, H.J.C., Lemonge, A.C.C.: An adaptive penalty method for genetic algorithms in constrained optimization problems. In: Iba, H. (ed.) Frontiers in Evolutionary Robotics, pp. 9–34. I-Tech Education Publ., Austria (2008)

    Google Scholar 

  8. Birgin, E.G., Floudas, C.A., Martinez, J.M.: Global minimization using an augmented Lagrangian method with variable lower-level constraints. Mathematical Programming 125, 139–162 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  9. Chootinan, P., Chen, A.: Constrained handling in genetic algorithms using a gradient-based repair method. Computers and Operations Research 33, 2263–2281 (2006)

    Article  MATH  Google Scholar 

  10. Coello Coello, C.A.: Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry 41, 113–127 (2000)

    Article  Google Scholar 

  11. Costa, M.F.P., Fernandes, E.M.G.P.: Assessing the potential of interior point barrier filter line search methods: nonmonotone versus monotone approach. Optimization 60(10-11), 1251–1268 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  12. Costa, M.F.P., Fernandes, E.M.G.P.: On Minimizing Objective and KKT Error in a Filter Line Search Strategy for an Interior Point Method. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011, Part III. LNCS, vol. 6784, pp. 231–244. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  13. Deb, K.: An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering 186, 311–338 (2000)

    Article  MATH  Google Scholar 

  14. Fernandes, E.M.G.P., Martins, T.F.M.C., Rocha, A.M.A.C.: Fish swarm intelligent algorithm for bound constrained global optimization. In: Aguiar, J.V. (ed.) CMMSE 2009, pp. 461–472 (2009)

    Google Scholar 

  15. Fletcher, R., Leyffer, S.: Nonlinear programming without a penalty function. Mathematical Programming 91, 239–269 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  16. Hedar, A.-R., Fukushima, M.: Heuristic pattern search and its hybridization with simulated annealing for nonlinear global optimization. Optimization Methods and Software 19, 291–308 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  17. Hedar, A.-R., Fukushima, M.: Derivative-free filter simulated annealing method for constrained continuous global optimization. Journal of Global Optimization 35, 521–549 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  18. Gao, X.Z., Wu, Y., Zenger, K., Huang, X.: A knowledge-based artificial fish-swarm algorithm. In: 13th IEEE International Conference on Computational Science and Engineering, pp. 327–332 (2010)

    Google Scholar 

  19. Jiang, M., Mastorakis, N., Yuan, D., Lagunas, M.A.: Image segmentation with improved artificial fish swarm algorithm. In: Mastorakis, N., et al. (eds.) ECC 2008. LNEE, vol. 28, pp. 133–138 (2009)

    Google Scholar 

  20. Jiang, M., Wang, Y., Pfletschinger, S., Lagunas, M.A., Yuan, D.: Optimal Multiuser Detection with Artificial Fish Swarm Algorithm. In: Huang, D.-S., et al. (eds.) ICIC 2007. CCIS, vol. 2, pp. 1084–1093. Springer, Heidelberg (2007)

    Google Scholar 

  21. Kaelo, P., Ali, M.M.: A numerical study of some modified differencial evolution algorithms. European Journal of Operational Research 169, 1176–1184 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  22. Karaboga, D., Basturk, B.: Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. LNCS (LNAI), vol. 4529, pp. 789–798. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  23. Karimi, A., Nobahari, H., Siarry, P.: Continuous ant colony system and tabu search algorithms hybridized for global minimization of continuous multi-minima functions. Computational Optimization and Applications 45, 639–661 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  24. Liu, J.-L., Lin, J.-H.: Evolutionary computation of unconstrained and constrained problems using a novel momentum-type particle swarm optimization. Engineering Optimization 39, 287–305 (2007)

    Article  MathSciNet  Google Scholar 

  25. Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation 188, 1567–1579 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  26. Mallipeddi, R., Suganthan, P.N.: Ensemble of constraint handling techniques. IEEE Transactions on Evolutionary Computation 14, 561–579 (2010)

    Article  Google Scholar 

  27. Petalas, Y.G., Parsopoulos, K.E., Vrahatis, M.N.: Memetic particle swarm optimization. Annals of Operations Research 156, 99–127 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  28. Pereira, A.I., Costa, M.F.P., Fernandes, E.M.G.P.: Interior point filter method for semi-infinite programming problems. Optimization 60(10-11), 1309–1338 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  29. Rocha, A.M.A.C., Fernandes, E.M.G.P.: Hybridizing the electromagnetism-like algorithm with descent search for solving engineering design problems. International Journal of Computer Mathematics 86, 1932–1946 (2009)

    Article  MATH  Google Scholar 

  30. Rocha, A.M.A.C., Fernandes, E.M.G.P., Martins, T.F.M.C.: Novel Fish Swarm Heuristics for Bound Constrained Global Optimization Problems. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011, Part III. LNCS, vol. 6784, pp. 185–199. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  31. Rocha, A.M.A.C., Fernandes, E.M.G.P.: Numerical study of augmented Lagrangian algorithms for constrained global optimization. Optimization 60(10-11), 1359–1378 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  32. Rocha, A.M.A.C., Martins, T.F.M.C., Fernandes, E.M.G.P.: An augmented Lagrangian fish swarm based method for global optimization. Journal of Computational and Applied Mathematics 235(16), 4611–4620 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  33. Runarsson, T.P., Yao, X.: Stochastic ranking for constrained evolutionary optimization. IEEE Transaction on Evolutionary Computation 4, 284–294 (2000)

    Article  Google Scholar 

  34. Silva, E.K., Barbosa, H.J.C., Lemonge, A.C.C.: An adaptive constraint handling technique for differential evolution with dynamic use of variants in engineering optimization. Optimization and Engineering 12, 31–54 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  35. Socha, K., Dorigo, M.: Ant colony optimization for continuous domains. European Journal of Operational Research 185, 1155–1173 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  36. Stanoyevitch, A.: Homogeneous genetic algorithms. International Journal of Computer Mathematics 87, 476–490 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  37. Ulbrich, M., Ulbrich, S., Vicente, L.N.: A globally convergent primal-dual interior-point filter method for nonlinear programming. Mathematical Programming 100, 379–410 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  38. Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106, 25–57 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  39. Wang, C.-R., Zhou, C.-L., Ma, J.-W.: An improved artificial fish-swarm algorithm and its application in feed-forward neural networks. In: Proceedings of the 4th ICMLC, pp. 2890–2894 (2005)

    Google Scholar 

  40. Wang, Y., Cai, Z., Zhou, Y., Fan, Z.: Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique. Structural and Multidisciplinary Optimization 37(4), 395–413 (2009)

    Article  Google Scholar 

  41. Wang, X., Gao, N., Cai, S., Huang, M.: An Artificial Fish Swarm Algorithm Based and ABC Supported QoS Unicast Routing Scheme in NGI. In: Min, G., Di Martino, B., Yang, L.T., Guo, M., Rünger, G. (eds.) ISPA Workshops 2006. LNCS, vol. 4331, pp. 205–214. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  42. Zahara, E., Hu, C.-H.: Solving constrained optimization problems with hybrid particle swarm optimization. Engineering Optimization 40(11), 1031–1049 (2008)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Production and Systems, School of Engineering, University of Minho, 4710-057, Braga, Portugal

    Ana Maria A. C. Rocha

  2. Department of Mathematics and Applications, School of Sciences, University of Minho, 4710-057, Braga, Portugal

    M. Fernanda P. Costa

  3. Mathematics R&D Centre, University of Minho, 4710-057, Braga, Portugal

    M. Fernanda P. Costa

  4. Algoritmi R&D Centre, University of Minho, 4710-057, Braga, Portugal

    Ana Maria A. C. Rocha & Edite M. G. P. Fernandes

Authors
  1. Ana Maria A. C. Rocha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Fernanda P. Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Edite M. G. P. Fernandes
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Laboratory of Urban and Territorial Systems, University of Basilicata, 10, Viale dell’Ateneo Lucano, 85100, Potenza, Italy

    Beniamino Murgante

  2. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli 1, 06123, Perugia, Italy

    Osvaldo Gervasi

  3. Department of Cyber Security Science, Federal University of Technology, Gidan Kwano Campus, Minna, Nigeria

    Sanjay Misra

  4. Faculty of Engineering, Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rua Sao Francisco Xavier, 524, 50. andar, sala 5145-F, Maracana, 20.550-013, Rio de Janeiro, RJ, Brazil

    Nadia Nedjah

  5. Department of Production and Systems, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal

    Ana Maria A. C. Rocha

  6. School of Business Systems, Monash University, 3800, Clayton, VIC, Australia

    David Taniar

  7. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, Higashi-ku, 813-8503, Fukuoka, Japan

    Bernady O. Apduhan

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rocha, A.M.A.C., Costa, M.F.P., Fernandes, E.M.G.P. (2012). An Artificial Fish Swarm Filter-Based Method for Constrained Global Optimization. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31137-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31137-6_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31136-9

  • Online ISBN: 978-3-642-31137-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation