Título: A Novel Multi-objective Optimization Method with Local Search Scheme using Shuffled Complex Evolution Applied to Mechanical Engineering Problems
Autores: LOBATO, F. S.; LIBOTTE, G. B.; PLATT, G. M.
Revista: Engineering Computations, 1(1), 1-30, 2022.
Resumo: In this work, the multi-objective optimization shuffled complex evolution is proposed. The algorithm is based on the extension of shuffled complex evolution, by incorporating two classical operators into the original algorithm: the rank ordering and crowding distance. In order to accelerate the convergence process, a Local Search strategy based on the generation of potential candidates by using Latin Hypercube method is also proposed. The multi-objective optimization shuffled complex evolution is used to accelerate the convergence process and to reduce the number of objective function evaluations. In general, the proposed methodology was able to solve a classical mechanical engineering problem with different characteristics. From a statistical point of view, we demonstrated that differences may exist between the proposed methodology and other evolutionary strategies concerning two different metrics (convergence and diversity), for a class of benchmark functions (ZDT functions).
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