Skip to main content

A novel heuristic optimization method: charged system search

Abstract

This paper presents a new optimization algorithm based on some principles from physics and mechanics, which will be called Charged System Search (CSS). We utilize the governing Coulomb law from electrostatics and the Newtonian laws of mechanics. CSS is a multi-agent approach in which each agent is a Charged Particle (CP). CPs can affect each other based on their fitness values and their separation distances. The quantity of the resultant force is determined by using the electrostatics laws and the quality of the movement is determined using Newtonian mechanics laws. CSS can be utilized in all optimization fields; especially it is suitable for non-smooth or non-convex domains. CSS needs neither the gradient information nor the continuity of the search space. The efficiency of the new approach is demonstrated using standard benchmark functions and some well-studied engineering design problems. A comparison of the results with those of other evolutionary algorithms shows that the proposed algorithm outperforms its rivals.

This is a preview of subscription content, access via your institution.

References

  1. Kaveh, A., Talatahari, S.: An improved ant colony optimization for constrained engineering design problems. Engineering Computations 27(1) (2010, in press)

  2. Fogel L.J., Owens A.J., Walsh M.J.: Artificial Intelligence Through Simulated Evolution. Wiley, Chichester (1966)

    MATH  Google Scholar 

  3. De Jong, K.: Analysis of the behavior of a class of genetic adaptive systems. Ph.D. Thesis, University of Michigan, Ann Arbor, MI (1975)

  4. Koza, J.R.: Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems. Report No. STAN-CS-90-1314, Stanford University, Stanford, CA (1990)

  5. Holland J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  6. Goldberg D.E.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Boston (1989)

    MATH  Google Scholar 

  7. Glover F.: Heuristic for integer programming using surrogate constraints. Decis. Sci. 8(1), 156–166 (1977)

    Article  Google Scholar 

  8. Dorigo M., Maniezzo V., Colorni A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B 26(1), 29–41 (1996)

    Article  Google Scholar 

  9. Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan (1995)

  10. Kirkpatrick S., Gelatt C., Vecchi M.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  Google Scholar 

  11. Erol O.K., Eksin I.: New optimization method: Big Bang–Big Crunch. Adv. Eng. Softw. 37, 106–111 (2006)

    Article  Google Scholar 

  12. Kaveh A., Talatahari S.: Size optimization of space trusses using Big Bang–Big Crunch algorithm. Comput. Struct. 87(17–18), 1129–1140 (2009)

    Article  Google Scholar 

  13. Rashedi E., Nezamabadi-pour H., Saryazdi S.: GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)

    MATH  Article  Google Scholar 

  14. Halliday D., Resnick R., Walker J.: Fundamentals of Physics, 8th edn. Wiley, New York (2008)

    Google Scholar 

  15. Kaveh A., Talatahari S.: Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Comput. Struct. 87(5–6), 267–283 (2009)

    Article  Google Scholar 

  16. Coello C.A.C.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput. Methods Appl. Mech. Eng. 191(11–12), 1245–1287 (2002)

    MATH  Article  Google Scholar 

  17. Kaveh A., Talatahari S.: A particle swarm ant colony optimization for truss structures with discrete variables. J. Constr. Steel Res. 65(8–9), 1558–1568 (2009)

    Article  Google Scholar 

  18. Tsoulos I.G.: Modifications of real code genetic algorithm for global optimization. Appl. Math. Comput. 203, 598–607 (2008)

    MATH  Article  MathSciNet  Google Scholar 

  19. Belegundu, A.D.: A study of mathematical programming methods for structural optimization. Ph.D. thesis, Department of Civil and Environmental Engineering, University of Iowa, Iowa, USA (1982)

  20. Arora J.S.: Introduction to Optimum Design. McGraw-Hill, New York (1989)

    Google Scholar 

  21. Coello C.A.C.: Use of a self-adaptive penalty approach for engineering optimization problems. Comput. Ind. 41, 113–127 (2000)

    Article  Google Scholar 

  22. Coello C.A.C., Montes E.M.: Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv. Eng. Inform. 16, 193–203 (2002)

    Article  Google Scholar 

  23. He Q., Wang L.: An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng. Appl. Artif. Intell. 20, 89–99 (2007)

    Article  Google Scholar 

  24. Montes E.M., Coello C.A.C.: An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int. J. Gen. Syst. 37(4), 443–473 (2008)

    Article  MathSciNet  Google Scholar 

  25. Ragsdell K.M., Phillips D.T.: Optimal design of a class of welded structures using geometric programming. ASME J. Eng. Ind. Ser. B 98(3), 1021–1025 (1976)

    Google Scholar 

  26. Deb K.: Optimal design of a welded beam via genetic algorithms. Am. Inst. Aeronaut. Astronaut. J. 29(11), 2013–2015 (1991)

    Google Scholar 

  27. Sandgren, E.: Nonlinear integer and discrete programming in mechanical design. In: Proceedings of the ASME design technology conference, Kissimine, FL, pp. 95–105 (1988)

  28. Kannan B.K., Kramer S.N.: An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. Trans. ASME J. Mech. Des. 116, 318–320 (1994)

    Article  Google Scholar 

  29. Deb K., Gene A.S.: A robust optimal design technique for mechanical component design. In: Dasgupta, D., Michalewicz, Z. (eds) Evolutionary Algorithms in Engineering Applications, pp. 497–514. Springer, Berlin (1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Kaveh.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Kaveh, A., Talatahari, S. A novel heuristic optimization method: charged system search. Acta Mech 213, 267–289 (2010). https://doi.org/10.1007/s00707-009-0270-4

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00707-009-0270-4

Keywords

  • Charge Particle
  • Resultant Force
  • Gravitational Search Algorithm
  • Mixed Integer Nonlinear Programming
  • Charge System Search