A nonmonotone GRASP

A greedy randomized adaptive search procedure (GRASP) is an itera- tive multistart metaheuristic for difficult combinatorial optimization problems. Each GRASP iteration consists of two phases: a construction phase, in which a feasible solution is produced, and a local search phase, in which a local optimum in the neighborhood of the constructed solution is sought. Repeated applications of the con- struction procedure yields different starting solutions for the local search and the best overall solution is kept as the result. The GRASP local search applies iterative improvement until a locally optimal solution is found. During this phase, starting from the current solution an improving neighbor solution is accepted and considered as the new current solution. In this paper, we propose a variant of the GRASP framework that uses a new “nonmonotone” strategy to explore the neighborhood of the current solu- tion. We formally state the convergence of the nonmonotone local search to a locally optimal solution and illustrate the effectiveness of the resulting Nonmonotone GRASP on three classical hard combinatorial optimization problems: the maximum cut prob- lem (MAX-CUT), the weighted maximum satisfiability problem (MAX-SAT), and the quadratic assignment problem (QAP)

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 derivative-free approach to constrained multiobjective nonsmooth optimization

open3noopenLiuzzi, G.; Lucidi, S.; Rinaldi, F.Liuzzi, G.; Lucidi, S.; Rinaldi, Francesc

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

Continuous global optimization for protein structure analysis

Optimization methods are a powerful tool in protein structure analysis. In this paper we show that they can be profitably used to solve relevant problems in drug design such as the comparison and recognition of protein binding sites and the protein-peptide docking. Binding sites recognition is generally based on geometry often combined with physico-chemical properties of the site whereas the search for correct protein-peptide docking is often based on the minimization of an interaction energy model. We show that continuous global optimization methods can be used to solve the above problems and show some computational results

Assessing chronological age of unaccompanied minors in Southern Italy

The increasing volume of, and subsequent complexities resulting from, migratory flows in the broader context of globalization has led to a range of problems, not only the protection of human rights and disease control but also the identification of those with the right to apply for refugee status and the age assessment of unaccompanied minors. Italy is a magnet for immigration from other countries bordering the Mediterranean Sea because the Italian coasts are within easy reach. In Italy, as in other western countries, unaccompanied asylum seekers deemed to be younger than 18 years face a very different path through the immigration system from that experienced by adults. Generally, adults are subject to immediate deportation or detention in jail. Minors are processed through the juvenile system, where detection is not mandatory; they will often have access to educational programs and may be granted a residency permit. The Section of Legal Medicine of the University of Bari was approached by immigration police authorities and judges to explore the possibility of examining unaccompanied asylum seekers, who claim do be younger than 18 years, to assess their age. A group of forensic pathologists and odontologists performed this evaluation relying on the skeletal maturation as seen on radiographs of the wrist and the pelvis for iliac crests and on an orthopantomograph, together with background information and clinical examination of each individual. Case studies are presented. This article does not attempt to give a definitive account of the different scientific methods for the assessment of age. It is important to understand some of the methods that may be used in an attempt to assess developmental maturity and from which it may be possible to ''read off'' an approximate chronological age. © 2011 by Lippincott Williams & Wilkins

Improving P300 Speller performance by means of optimization and machine learning

Brain-Computer Interfaces (BCIs) are systems allowing people to interact with the environment bypassing the natural neuromuscular and hormonal outputs of the peripheral nervous system (PNS). These interfaces record a user’s brain activity and translate it into control commands for external devices, thus providing the PNS with additional artificial outputs. In this framework, the BCIs based on the P300 Event-Related Potentials (ERP), which represent the electrical responses recorded from the brain after specific events or stimuli, have proven to be particularly successful and robust. The presence or the absence of a P300 evoked potential within the EEG features is determined through a classification algorithm. Linear classifiers such as stepwise linear discriminant analysis and support vector machine (SVM) are the most used discriminant algorithms for ERPs’ classification. Due to the low signal-to-noise ratio of the EEG signals, multiple stimulation sequences (a.k.a. iterations) are carried out and then averaged before the signals being classified. However, while augmenting the number of iterations improves the Signal-to-Noise Ratio, it also slows down the process. In the early studies, the number of iterations was fixed (no stopping environment), but recently several early stopping strategies have been proposed in the literature to dynamically interrupt the stimulation sequence when a certain criterion is met in order to enhance the communication rate. In this work, we explore how to improve the classification performances in P300 based BCIs by combining optimization and machine learning. First, we propose a new decision function that aims at improving classification performances in terms of accuracy and Information Transfer Rate both in a no stopping and early stopping environment. Then, we propose a new SVM training problem that aims to facilitate the target-detection process. Our approach proves to be effective on several publicly available datasets