New global optimization methods for ship design problems

Campana, Emilio F.; Liuzzi, Giampaolo; Lucidi, Stefano; Peri, Daniële; Piccialli, Veronica; Pinto, Gustavo Adolfo Saavedra

doi:10.1007/s11081-009-9085-3

Cited by 67 publications

(34 citation statements)

References 23 publications

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“…PSO belongs to the class of heuristic algorithms for single-objective evolutionary derivativefree global optimization. In order to make PSO more efficient for use within SBD, a deterministic version of the algorithm (DPSO) was formulated in [5] as follows…”

Section: The Dpso Algorithmmentioning

confidence: 99%

“…Such an approach can be too expensive (often almost unaffordable) in SBD optimization for industrial applications, when CPU-time expensive computer simulations are used directly as analysis tools. For this reason, deterministic approaches have been successfully developed and applied to SBD optimization, including hydrodynamic problems [5,11].…”

Section: Introductionmentioning

confidence: 99%

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Ship hydrodynamic optimization by local hybridization of deterministic derivative-free global algorithms

Serani

Fasano

Liuzzi³

et al. 2016

Applied Ocean Research

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a b s t r a c tSimulation-based design optimization methods integrate computer simulations, design modification tools, and optimization algorithms. In hydrodynamic applications, often objective functions are computationally expensive and noisy, their derivatives are not directly provided, and the existence of local minima cannot be excluded a priori, which motivates the use of deterministic derivative-free global optimization algorithms. The enhancement of two algorithms of this type, DIRECT (DIviding RECTangles) and DPSO (Deterministic Particle Swarm Optimization), is presented based on global/local hybridization with derivative-free line search methods. The hull-form optimization of the DTMB 5415 model is solved for the reduction of the calm-water resistance at Fr = 0.25, using potential flow and RANS solvers. Six and eleven design variables are used respectively, modifying both the hull and the sonar dome. Hybrid algorithms show a faster convergence towards the global minimum than the original global methods and are a viable option for ship hydrodynamic optimization. A significant resistance reduction is achieved both by potential flow and RANS-based optimizations, showing the effectiveness of the optimization procedure.

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Section: The Dpso Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ship hydrodynamic optimization by local hybridization of deterministic derivative-free global algorithms

Serani

Fasano

Liuzzi³

et al. 2016

Applied Ocean Research

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“…In particular, we refer to the generator of starting points DIRGEN [3], which is a DIRECT-type strategy [9]. Differently from the simple uniform random sampling, this choice allows to densely cover the search domain (see, e.g.…”

Section: A Convergence Property Of Goalmentioning

confidence: 99%

A filling function method for unconstrained global optimization

Lampariello

Liuzzi

2015

Comput Optim Appl

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The problem is considered of finding a global minimum point of a given continuously differentiable function. The strategy is adopted of a sequential nonmonotone improvement of local optima. In particular, to escape the basin of attraction of a local minimum, a suitable gaussianbased filling function is constructed using the quadratic model of the objective function, and added to the objective to fill the basin. Then, a procedure is defined where some new minima are determined, and that of them with the lowest function value is selected as the subsequent restarting point, even if its basin is higher than the starting one. The algorithm is applied to a set of test functions from the literature and the numerical results are reported.Key words: Global optimization, unconstrained minimization, gradient methods.A filling function method for unconstrained global optimizationAbstract. The problem is considered of finding a global minimum point of a given continuously differentiable function. The strategy is adopted of a sequential nonmonotone improvement of local optima. In particular, to escape the basin of attraction of a local minimum, a suitable gaussian-based filling function is constructed using the quadratic model of the objective function, and added to the objective to fill the basin. Then, a procedure is defined where some new minima are determined, and that of them with the lowest function value is selected as the subsequent restarting point, even if its basin is higher than the starting one. The algorithm is applied to a set of test functions from the literature and the numerical results are reported.

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“…The probabilistic operating scenario is that shown in Table 1. For MRDO, a particle swarm optimization (PSO) algorithm (Campana et al 2009) is imposed over the UQ, whereas for deterministic MDO, the same algorithm is imposed over the MDA. For both approaches, a maximum number of 3,000 objective function evaluations is assumed.…”

Section: Multidisciplinary Robust Design Optimization (Mrdo)mentioning

confidence: 99%

“…For solving the resulting minimization problem, a Particle Swarm Optimization (PSO) algorithm is used. The method, first introduced by Kennedy and Eberhart (1995) is here applied in the form proposed by Campana et al (2009).…”

Section: Introductionmentioning

confidence: 99%

Hydroelastic optimization of a keel fin of a sailing boat: a multidisciplinary robust formulation for ship design

Diez

Peri

Fasano

et al. 2012

Struct Multidisc Optim

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The paper presents a formulation for multidisciplinary design optimization of vessels, subject to uncertain operating conditions. The formulation couples the multidisciplinary design analysis with the Bayesian approach to decision problems affected by uncertainty. In the present context, the design specifications are no longer given in terms of a single operating design point, but in terms of probability density function of the operating scenario. The optimal configuration is that which maximizes the performance expectation over the uncertain parameters variation. In this sense, the optimal solution is "robust" within the stochastic scenario assumed. Theoretical and numerical issues are addressed and numerical results in the hydroelastic optimization of a keel fin of a sailing yacht are presented.

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New global optimization methods for ship design problems

Cited by 67 publications

References 23 publications

Ship hydrodynamic optimization by local hybridization of deterministic derivative-free global algorithms

Ship hydrodynamic optimization by local hybridization of deterministic derivative-free global algorithms

A filling function method for unconstrained global optimization

Hydroelastic optimization of a keel fin of a sailing boat: a multidisciplinary robust formulation for ship design

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