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Function optimization using robust simulated annealing

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In today’s world, researchers spend more time in fine-tuning of algorithms rather than designing and implementing them. This is very true when developing heuristics and metaheuristics, where the correct choice of values for search parameters has a considerable effect on the performance of the procedure. Determination of optimal parameters is continuous engineering task whose goals are to reduce the production costs and to achieve the desired product quality. In this research, simulated annealing algorithm is applied to solve function optimization. This paper presents the application and use of statistical analysis method Taguchi design method for optimizing the parameters are tuned for the optimum output. The outcomes for various combinations of inputs are analyzed and the best combination is found among them. From all the factors considered during experimentation, the factors and its values which show the significant effect on output are discovered.




Pandey, H. M., & Gajendran, A. (2016). Function optimization using robust simulated annealing. In Advances in Intelligent Systems and Computing (Vol. 435, pp. 347–355). Springer Verlag.

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