Abstract
An improved version of the charged system search (CSS) algorithm is introduced which is called magnetic charged system search (MCSS). In the new algorithm, magnetic forces are considered in addition to electrical forces, using the Biot–Savart law. Each charged particle (CP), as a search agent, exerts magnetic forces on other CPs based on the variation of its objective function value during its last movement and its distance between other CPs. This additional force provides useful information for the optimization process and enhances the performance of the CSS algorithm. The efficiency of the MCSS is examined by application of this algorithm to well-known mathematical benchmarks and three well-studied engineering design problems. The results are compared to those of the CSS, and the improvements are highlighted.
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References
Holland J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Goldberg D.E.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Boston (1989)
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)
Dorigo M., Maniezzo V., Colorni A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B 26, 29–41 (1996)
Kirkpatrick S., Gelatt C., Vecchi M.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Geem Z.W., Kim J.H., Loganathan G.V.: A new heuristic optimization algorithm; harmony search. Simulation 76, 60–68 (2001)
Rashedi E., Nezamabadi-pour H., Saryazdi S.: GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)
Erol O.K., Eksin I.: New optimization method: Big Bang–Big Crunch. Adv. Eng. Softw. 37, 106–111 (2006)
Kaveh A., Talatahari S.: Size optimization of space trusses using Big Bang–Big Crunch algorithm. Comput. Struct. 87, 1129–1140 (2009)
Kaveh A., Laknejadi K., Alinejad B.: Performance based multi-objective optimization of large steel structures. Acta. Mech. 223, 355–369 (2012)
Kaveh A., Talatahari S.: Charged system search for optimal design of planar frame structures. Appl. Soft Comput. 12, 382–393 (2012)
Kaveh A., Talatahari S.: Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Comput. Struct. 87, 267–283 (2009)
Kaveh A., Talatahari S.: Optimization of large-scale truss structures using modified charged system search. Int. J. Optim. Civil Eng. 1, 15–28 (2011)
Kaveh A., Talatahari S.: A novel heuristic optimization method: charged system search. Acta Mech. 213, 267–286 (2010)
Halliday D., Resnick R., Walker J.: Fundamentals of Physics. Wiley, New York (2008)
Tsoulos I.G.: Modifications of real code genetic algorithm for global optimization. Appl. Math. Comput. 203, 598–607 (2008)
Hines W., Montgomery D.: Probability and Statistics in Engineering and Management Science. 3rd edn. Wiley, New York (1990)
Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.-P., Auger, A., Tiwari, S.: Problem Definitions and Evaluation Criteria for CEC 2005 Special Session on Real-Parameter Optimization, Technical Report, Nanyang Technological University, Singapore and KanGAL Report Number 2005005 (2005)
Garcia S., Molina D., Lozano M., Herrera F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms behavior: a case study on the CEC 2005 special session on real parameter optimization. J. Heuristics 15, 617–644 (2009)
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 (1982)
Arora J.S.: Introduction to Optimum Design. McGraw-Hill, New York (1989)
Coello C.A.C.: Use of a self-adaptive penalty approach for engineering optimization problems. Comput. Ind. 41, 113–127 (2000)
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)
He Q., Wang L.: An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng. Appl. Artif. Intell. 20, 89–99 (2007)
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, 443–473 (2008)
Kaveh A., Talatahari S.: An improved ant colony optimization for constrained engineering design problems. Eng. Comput. 27, 155–182 (2010)
Ragsdell K.M., Phillips D.T.: Optimal design of a class of welded structures using geometric programming. ASME J. Eng. Ind. Ser. B 98, 1021–1025 (1976)
Deb K.: Optimal design of a welded beam via genetic algorithms. AIAA J. 29, 2013–2015 (1991)
Sandgren, E.: Nonlinear integer and discrete programming in mechanical design. In: Proceedings of the ASME Design Technology Conference, pp. 95–105. Kissimine (1988)
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)
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)
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Kaveh, A., Motie Share, M.A. & Moslehi, M. Magnetic charged system search: a new meta-heuristic algorithm for optimization. Acta Mech 224, 85–107 (2013). https://doi.org/10.1007/s00707-012-0745-6
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DOI: https://doi.org/10.1007/s00707-012-0745-6