A novel multi-objective optimization method with local search scheme using shuffled complex evolution applied to mechanical engineering problems. Issue 8 (18th August 2022)

• Record Type: Journal Article
• Authors: Lobato, Fran Sérgio; Libotte, Gustavo Barbosa; Platt, Gustavo Mendes
• Is Part Of: Engineering computations. Volume 39:Issue 8(2022)
• Page Start: 2958
• Page End: 2989
• Publication Date: 2022-08-18
• Subjects: Multi-objective optimization -- Shuffled complex evolution -- Local search -- Mechanical engineering problems -- Latin hypercube; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-07-2021-0381
• Languages: English
• ISSNs: 0264-4401

Abstract

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.