An adaptive penalty function-based maximin desirability index for close tolerance multiple-response optimization problems

2011 ◽  
Vol 61 (1-4) ◽  
pp. 379-390 ◽  
Author(s):  
Sasadhar Bera ◽  
Indrajit Mukherjee
Keyword(s):  
Penalty Function ◽  
Optimization Problems ◽  
Multiple Response ◽  
Multiple Response Optimization ◽  
Adaptive Penalty ◽  
Adaptive Penalty Function ◽  
Response Optimization ◽  
Desirability Index

An Adaptive Penalty Function Method for Constrained Optimization with Evolutionary Programming

2000 ◽  
Vol 4 (2) ◽  
pp. 164-170 ◽  
Author(s):  
Xinghuo Yu ◽  
◽  
Baolin Wu
Keyword(s):  
Constrained Optimization ◽  
Penalty Function ◽  
Function Method ◽  
Optimization Problems ◽  
Evolutionary Programming ◽  
Benchmark Problems ◽  
Local Optimum ◽  
Penalty Function Method ◽  
Adaptive Penalty ◽  
Adaptive Penalty Function

In this paper, we propose a novel adaptive penalty function method for constrained optimization problems using the evolutionary programming technique. This method incorporates an adaptive tuning algorithm that adjusts the penalty parameters according to the population landscape so that it allows fast escape from a local optimum and quick convergence toward a global optimum. The method is simple and computationally effective in the sense that only very few penalty parameters are needed for tuning. Simulation results of five well-known benchmark problems are presented to show the performance of the proposed method.

scholarly journals Higher Desirability in Solving Multiple Response Optimization Problems with Committee Machine

2014 ◽  
Vol 564 ◽  
pp. 608-613 ◽  
Author(s):  
Seyed Jafar Golestaneh ◽  
N. Ismail ◽  
M.K.A.M. Ariffin ◽  
S.H. Tang ◽  
Hassan Moslemi Naeini ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Fitness Function ◽  
Multiple Response ◽  
Final Solution ◽  
Committee Machine ◽  
Average Value ◽  
Multiple Response Optimization ◽  
Response Optimization ◽  
Current Article

Many industrial problems need to be optimized several responses simultaneously. These problems are named multiple response optimization (MRO) and they can have different objectives such as Target, Minimization or Maximization. Committee machine (CM) as a set of some experts such as some artificial neural networks (ANNs) in combination with genetic algorithm (GA) is applied for modeling and optimization of MRO problems. In addition, optimization usually is done on Global Desirability (GD) function. Current article is a development for recent authors' work to determine economic run number for application of CM and GA in MRO problem solving. This study includes a committee machine with four different ANNs. The CM weights are determined with GA which its fitness function is minimizing the RMSE. Then, another GA specifies the final solution with object maximizing the global desirability. This algorithm was implemented on five case studies and the results represent the algorithm can get higher global desirability by repeating the runs and economic run number (ERN) depends on the MRO problem objective. ERN is ten for objective “Target”. This number for objectives which are mixture of minimization and maximization ERN is five. The repetition are continued until these ERN values have considerable increased in maximum GD with respect to average value of GD. More repetition from these ERN to forty five numbers cause a slight raise in maximum GD.

scholarly journals Desirability Improvement of Committee Machine to Solve Multiple Response Optimization Problems

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Seyed Jafar Golestaneh ◽  
Napsiah Ismail ◽  
Mohd Khairol Anuar M. Ariffin ◽  
Say Hong Tang ◽  
Hassan Moslemi Naeini
Keyword(s):  
Optimization Problems ◽  
Fitness Function ◽  
Optimization Techniques ◽  
Multiple Response ◽  
Repeat Number ◽  
Committee Machine ◽  
Average Value ◽  
Multiple Response Optimization ◽  
Response Optimization ◽  
Final Response

Multiple response optimization (MRO) problems are usually solved in three phases that include experiment design, modeling, and optimization. Committee machine (CM) as a set of some experts such as some artificial neural networks (ANNs) is used for modeling phase. Also, the optimization phase is done with different optimization techniques such as genetic algorithm (GA). The current paper is a development of recent authors' work on application of CM in MRO problem solving. In the modeling phase, the CM weights are determined with GA in which its fitness function is minimizing the RMSE. Then, in the optimization phase, the GA specifies the final response with the object to maximize the global desirability. Due to the fact that GA has a stochastic nature, it usually finds the response points near to optimum. Therefore, the performance the algorithm for several times will yield different responses with different GD values. This study includes a committee machine with four different ANNs. The algorithm was implemented on five case studies and the results represent for selected cases, when number of performances is equal to five, increasing in maximum GD with respect to average value of GD will be eleven percent. Increasing repeat number from five to forty-five will raise the maximum GD by only about three percent more. Consequently, the economic run number of the algorithm is five.

scholarly journals A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Ali Salmasnia ◽  
Mahdi Bastan ◽  
Asghar Moeini
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problems ◽  
Multiple Response ◽  
Process Variables ◽  
Multiple Response Optimization ◽  
Response Optimization ◽  
Artificial Neural ◽  
The Relationship ◽  
Response Variables

An important problem encountered in product or process design is the setting of process variables to meet a required specification of quality characteristics (response variables), called a multiple response optimization (MRO) problem. Common optimization approaches often begin with estimating the relationship between the response variable with the process variables. Among these methods, response surface methodology (RSM), due to simplicity, has attracted most attention in recent years. However, in many manufacturing cases, on one hand, the relationship between the response variables with respect to the process variables is far too complex to be efficiently estimated; on the other hand, solving such an optimization problem with accurate techniques is associated with problem. Alternative approach presented in this paper is to use artificial neural network to estimate response functions and meet heuristic algorithms in process optimization. In addition, the proposed approach uses the Taguchi robust parameter design to overcome the common limitation of the existing multiple response approaches, which typically ignore the dispersion effect of the responses. The paper presents a case study to illustrate the effectiveness of the proposed intelligent framework for tackling multiple response optimization problems.

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