Application of Adaptive Current Search to Solve Constraint Optimization Problems

Main Article Content

Supaporn Suwannarongsri


The adaptive current search (ACS) is one of the most efficient metaheuristic optimization techniques. Algorithms of the ACS is developed from the behavior of electric current flown through electrical networks. This paper proposes the application of the ACS to solve constraint optimization problems. Performance of the ACS is evaluated against three standard benchmarking constraint problems. Then, the ACS is applied to solve two real-world constraint engineering problems, i.e. spring design and pressure vessel design. Results obtained by the ACS will be compared with those obtained by the conventional method. As results, it was found that the ACS can provide superior solutions to the conventional method for all problems. 

Article Details

How to Cite
Suwannarongsri, S. (2016). Application of Adaptive Current Search to Solve Constraint Optimization Problems. Thai Industrial Engineering Network Journal, 2(1), 35–42. Retrieved from
Research and Review Article


[1] F. Gloverand G. A. Kochenberger, Handbook of Metaheuristics, Kluwer Academic Publishers, Dordrecht, 2003.
[2] E. G. Talbi, Metaheuristics from Design to Implementation, John Wiley & Sons, 2009.
[3] X. S. Yang, Engineering Optimization: An Introduction with Metaheuristic Applications, John Wiley & Sons, 2010.
[4] S. Suwannarongsri, T. Bunnag and W. Klinbun, “Energy resource management of assembly line balancing problem using modified current search method,” International Journal of Intelligent
Systems and Applications, vol.6, no.3, 2014, pp. 1 – 11.
[5] S. Suwannarongsri, T. Bunnag and W. Klinbun, “Optimization of energy resource management for assembly line balancing using adaptive current search,” American Journal of Operations Research, vol.4,
no.1, 2014, pp. 8 – 21.
[6] S. Suwannarongsri, T. Bunnag and W. Klinbun, “Traveling transportation problem optimization by adaptive current search method,”International Journal of Modern Education and Computer Science, vol.6,
no.5, 2014, pp. 33 – 45.
[7] D. M. Himmelblau, Applied Nonlinear Programming, McGraw-Hill, New York, 1972.
[8] J. Braken and G. P. McCormick, Selected Applications of Nonlinear Programming, John Wiley & Sons, New York, 1968.
[9] L. C. Cagnina, S. C. Esquivel and C. A. Coello, “Solving engineering optimization problems with the simple constrained particle swarm optimizer,” Informatica, vol. 32, 2008, pp. 319 – 326.
[10] C. A. Coello, “Use of a self-adaptive penalty approach for engineering optimization problems,” Computers in Industry, vol. 41, 2000, pp. 113 – 127