Abstract
In the framework of multi-response optimization techniques, the optimization methodology based on the desirability function is one of the most popular and most frequently used methodologies by researchers and practitioners in engineering, chemistry, technology and many other fields of science and technique. Numerous desirability functions have been introduced to improve the performance of this optimization methodology. Recently, a novel desirability function for multi-response optimization is proposed, which is smooth, nonlinear, and differentiable, and thus more suitable for applying some of the more efficient gradient-based optimization methods. This paper evaluates the performance of the proposed method through six real examples. After a comparative analysis of the results, it is shown that the proposed method in a certain measure outperforms the other competitive optimization methods.
Highlights
There are two categories of optimization techniques
Among the many optimization techniques presented for desirability functions that can be employed to solve multi-response optimization (MRO) problems, the direct search (DS) method and its modifications are still the “first resort methods” [9], and sometimes the only options for solving a large class of optimization problems
The weights can be specified by the decision maker (DM) next to the shape parameters in the desirability functions
Summary
There are two categories of optimization techniques. The first deals with single optimization problems, while the second solves multi-response optimization (MRO) problems. Among the many optimization techniques presented for desirability functions that can be employed to solve MRO problems, the direct search (DS) method and its modifications are still the “first resort methods” [9], and sometimes the only options for solving a large class of optimization problems. In some multi-response optimization problems, the graphical approach can be very useful when two design factors (input variables) are considered and the number of responses (output variables) is not too large. In such cases, the contour plot methodology allows to find visually the optimal conditions that simultaneously satisfy all the involved responses. Brief concluding remarks are given in the sixth Section
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