A novel multi-objective optimization method with local search scheme using shuffled complex evolution applied to mechanical engineering problems. Issue 8 (18th August 2022) - Lobato, Fran Sérgio; Libotte, Gustavo Barbosa; Platt, Gustavo Mendes
Abstract : Purpose: 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. Design/methodology/approach: The multi-objective optimization shuffled complex evolution is used to accelerate the convergence process and to reduce the number of objective function evaluations. Findings: 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). Originality/value: The development of a new numerical method to solve multi-objective optimization problems is the major contribution.