A Genetic Algorithm and Fuzzy Approach (GAFA) for Constrained Nonlinear Optimization in Design
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Constrained nonlinear optimization (CNLO) problems often involve imprecise objectives and constraints. The application of conventional nonlinear optimization techniques does not seem the best approach for CNLO. A multi-objective multi-constraint algorithm is proposed in this paper to find an approximation of the so-called pareto-optimal solution, which is searched by a genetic algorithm that uses constraints and objective functions defined via fuzzy sets. The proposed algorithm is illustrated by two classic examples in the field of engineering design. Results indicate that the proposed algorithm is effective in terms of finding a solution that is within acceptable proximity of the so-called 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 the modified algorithm; the population evolves towards the Pareto-optimal set.
author list (cited authors)
Bhuvaneshwaran, V., Langari, R., & Temponi, C.