Multi-objective design optimization of sandwich panel

In the scope of an R&D project a new floor system based on sandwich panel has been developed. The lightweight structural system shall be a competitive solution when compared to traditional rehabilitation technique of degraded timber floors in old buildings. The layout of the sandwich prototypes designed involved the use of steel face sheet and i) steel webs and polyurethane (PUR) foam core system, ii) glass fiber-reinforced polymer (GFRP) webs and PUR foam core system and iii) outer steel webs and balsa wood core. The design of the sandwich panels included an optimization procedure. A multi-objective genetic algorithm (GA) was developed for this purpose as it is a search method well suited for the solution of optimization problems. The multi-objective GA aims at the minimization of the three objective functions, i.e. cost, mass and environmental footprint of the sandwich panel. The definition of the main feature of the algorithm includes consideration about encoding procedure, fitness scaling, selection method and handling of constraints. The boundary conditions are imposed so that the retrieved solutions will represent a feasible solution to the problem. These boundary conditions are the analytical formulation of the serviceability, ultimate limit state and thermal transmittance verifications imposed by the building codes to sandwich panels. The present paper deals with the introduction of all the aspects of the optimization problems providing as an example the optimization of the panel with steel face sheets, webs and PUR foam.This work is part of the research project Lightslab - development of innovative slab solutions using sandwich panels (POCI-01-0247-FEDER-033865), co-funded by the European Regional Development Fund (FEDER), through the "Programa Operacional Competitividade e Internacionalizacao" (POCI) and the Portuguese National Innovation Agency (ANI). The first and second authors acknowledge the grants DFA/BD/8319/2020 and SFRH/BSAB/150266/2019, respectively, provided by FCT, financed by European Social Fund and by national funds through the FCT/MCTES

Revisiting Norm Optimization for Multi-Objective Black-Box Problems: A Finite-Time Analysis

The complexity of Pareto fronts imposes a great challenge on the convergence analysis of multi-objective optimization methods. While most theoretical convergence studies have addressed finite-set and/or discrete problems, others have provided probabilistic guarantees, assumed a total order on the solutions, or studied their asymptotic behaviour. In this paper, we revisit the Tchebycheff weighted method in a hierarchical bandits setting and provide a finite-time bound on the Pareto-compliant additive $\epsilon$-indicator. To the best of our knowledge, this paper is one of few that establish a link between weighted sum methods and quality indicators in finite time.Comment: submitted to Journal of Global Optimization. This article's notation and terminology is based on arXiv:1612.0841

Hybridization of multi-objective deterministic particle swarm with derivative-free local searches

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

A multi-objective DIRECT algorithm for ship hull optimization

The paper is concerned with black-box nonlinear constrained multi-objective optimization problems. Our interest is the definition of a multi-objective deterministic partition-based algorithm. The main target of the proposed algorithm is the solution of a real ship hull optimization problem. To this purpose and in pursuit of an efficient method, we develop an hybrid algorithm by coupling a multi-objective DIRECT-type algorithm with an efficient derivative-free local algorithm. The results obtained on a set of “hard” nonlinear constrained multi-objective test problems show viability of the proposed approach. Results on a hull-form optimization of a high-speed catamaran (sailing in head waves in the North Pacific Ocean) are also presented. In order to consider a real ocean environment, stochastic sea state and speed are taken into account. The problem is formulated as a multi-objective optimization aimed at (i) the reduction of the expected value of the mean total resistance in irregular head waves, at variable speed and (ii) the increase of the ship operability, with respect to a set of motion-related constraints. We show that the hybrid method performs well also on this industrial problem