Volume 24, Issue 2 (IJIEPR 2013)                   IJIEPR 2013, 24(2): 131-136 | Back to browse issues page

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Zadbood A, Noghondarian K, Zadbood Z. Multiresponse surface optimization via Harmony search algorithm (Quality Engineering Conference Paper). IJIEPR. 2013; 24 (2) :131-136
URL: http://ijiepr.iust.ac.ir/article-1-499-en.html
1- of Industrial Engineering ,Iran University of Science and Technology Master
2- Assistant Professor in Industrial Engineering, Iran University of Science and Technology , Noghondarian@iust.ac.ir
3- Bachelor of Computer Engineering ,University of science and culture
Abstract:   (5227 Views)
Response surface methodology is a common tool in optimizing processes. It mainly concerns situations when there is only one response of interest. However, many designed experiments often involve simultaneous optimization of several quality characteristics. This is called a Multiresponse Surface Optimization problem. A common approach in dealing with these problems is to apply desirability function approach combined with an optimization algorithm to determine the best settings of control variables. As the response surfaces are often nonlinear and complex a number of meta-heuristic search techniques have been widely for optimizing the objective function. Amongst these techniques genetic algorithm, simulated annealing, tabu search and hybridization of them have drawn a great deal of attention so far. This study presents the use of harmony search algorithm for Multiresponse surface optimization. It is one of the recently developed meta heuristic algorithms that has been successfully applied to several engineering problems. This music inspired heuristic is conceptualized from the musical process of searching for a perfect state of harmony. The performance of the algorithm is evaluated by an example from the literature. Results indicate the efficiency and outperformance of the method in comparison with some previously used methods.
Full-Text [PDF 488 kb]   (2049 Downloads)    
Type of Study: Research | Subject: Optimization Techniques
Received: 2013/01/28 | Accepted: 2013/06/17 | Published: 2013/06/17

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