Hybridization of Multi-Objective Deterministic Particle Swarm with Derivative-Free Local Searches

Riccardo Pellegrini, Andrea Serani, Giampaolo Liuzzi, Francesco Rinaldi, Stefano Lucidi and Matteo Diez
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Riccardo Pellegrini: CNR-INM, National Research Council—Institute of Marine Engineering, 00139 Rome, Italy
Andrea Serani: CNR-INM, National Research Council—Institute of Marine Engineering, 00139 Rome, Italy
Giampaolo Liuzzi: CNR-IASI, National Research Council—Institute for Systems Analysis and Computer Science, 00185 Rome, Italy
Francesco Rinaldi: Department of Mathematics, University of Padua, 35121 Padua, Italy
Stefano Lucidi: Department of Computer, Control, and Management Engineering “A. Ruberti”, Sapienza University, 00185 Rome, Italy
Matteo Diez: CNR-INM, National Research Council—Institute of Marine Engineering, 00139 Rome, Italy

Mathematics, 2020, vol. 8, issue 4, 1-20

Abstract: The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts.

Keywords: hybrid algorithms; memetic algorithms; particle swarm; multi-objective deterministic optimization, derivative-free; global/local optimization; simulation-based design optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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