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

Riccardo Pellegrini,Giampaolo Liuzzi,Matteo Diez,Francesco Rinaldi,Stefano Lucidi,Andrea Serani

doi:10.3390/math8040546

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.

Highlights

The research and development of new technologies, along with a reduction of design and production costs, are enabling factors to face the challenges imposed by the worldwide-market competition
The objective of the present work is to show how the use of a global/local hybrid algorithm based on multi-objective deterministic version of the PSO algorithm (MODPSO) and derivative-free multi-objective (DFMO) achieves better performance than stand-alone global and local algorithms in solving multi-objective simulation-based design (SBD) optimization (SBDO) for hull-form design
The MODPSO setup was defined as in [6]: number of particles Z equal to 8MN, initialized using a Hammersley sequence sampling [56] over variables domain and boundaries; coefficients χ = 0.721 and φ1 = φ2 = 1.655 [57]; box constraints handled by a semi-elastic wall-type method [15]

Summary

Introduction

The research and development of new technologies, along with a reduction of design and production costs, are enabling factors to face the challenges imposed by the worldwide-market competition. Computer simulations have played an increasingly important role in the design process of engineering products whose efficiency is greatly affected by shape parameters, such as air-, ground-, and water-borne vehicles. For these reasons, addressing real-world complex industrial applications involves high-fidelity physics-based solvers, where innovative products are pursued for which past experience is not available. In this context, the simulation-based design (SBD) paradigm has demonstrated the ability to support the design decision-making process. The computational costs associated with the solution of the SBDO problem with high-fidelity solvers remain a limiting factor for the implementation of the SBDO in industries and, in particular, small- and medium-sized enterprises

Objectives

Results

Conclusion