GA Based Engineering Design Optimization Using Fuzzy Constraints and Fitness Functions
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This paper is focused on constrained nonlinear optimization problems that are encountered in engineering design. These problems often involve imprecise objectives as well as constraints and as such do not lend themselves well to the application of conventional nonlinear optimization techniques. In this paper, fuzzy logic, in conjunction with genetic algorithms, is used to address this issue. In particular, fuzzy sets are used to define the constraints/objective functions while a genetic search algorithm is used to find an approximation of the so-called pareto-optimal solution. It is shown, via the application of this approach to two classic problems in the field, that the proposed methodology is effective in terms of finding a solution that is within acceptable proximity of the pareto-optimal front. Moreover, a comparative analysis of the performance of the proposed approach with those reported in the literature reveals the computational efficiency of this methodology.
author list (cited authors)
Bhuvaneshwaran, V., Temponi, C., & Langari, R.