Grasp

Energy is a vital input for social and economic development. As a result of the generalization of agricultural, industrial and domestic activities the demand for energy has increased remarkably, especially in emergent countries. This has meant rapid grower in the level of greenhouse gas emissions and the increase in fuel prices, which are the main driving forces behind efforts to utilize renewable energy sources more effectively, i.e. energy which comes from natural resources and is also naturally replenished. Despite the obvious advantages of renewable energy, it presents important drawbacks, such as the discontinuity of generation, as most renewable energy resources depend on the climate, which is why their use requires complex design, planning and control optimization methods. Fortunately, the continuous advances in computer hardware and software are allowing researchers to deal with these optimization problems using computational resources, as can be seen in the large number of optimization methods that have been applied to the renewable and sustainable energy field. This paper presents a review of the current state of the art in computational optimization methods applied to renewable and sustainable energy, offering a clear vision of the lastest research advances in this field.

This paper is concerned with the application of a GRASP approach to a nursescheduling problem in which the objective is to optimise a set of preferences subject to a set of binding constraints. The balance between feasibility and optimality is a key issue. This is addressed by using a knapsack model to ensure that the solutions produced by the construction heuristic are easy to repair. Several construction heuristics and neighbourhoods are compared empirically. The best combination is further enhanced by a diversification strategy and a dynamic evaluation criterion. Tests show that it outperforms previously published approaches and finds optimal solutions quickly and consistently.

Microgrids are expected to become part of the next electric power system evolution, not only in rural and remote areas but also in urban communities. Since microgrids are expected to coexist with traditional power grids (such as district heating does with traditional heating systems), their planning process must be addressed to economic feasibility, as a long-term stability guarantee. Planning a microgrid is a complex process due to existing alternatives, goals, constraints and uncertainties. Usually planning goals conflict each other and, as a consequence, different optimization problems appear along the planning process. In this context, technical literature about optimization techniques applied to microgrid planning have been reviewed and the guidelines for innovative planning methodologies focused on economic feasibility can be defined. Finally, some trending techniques and new microgrid planning approaches are pointed out.

Esta tesis aborda el Problema Árbol de Expansión con incertidumbre intervalar en los costos,utilizando el criterio de Min-Max Regret. Varios algoritmos son implementados, tanto exactos como heurísticos. Con respecto a los algoritmos exactos, se implementan Descomposición de Benders y Branch and Cut , ambos incluyen variantes. Branch and Cut logra superar al resto de los algoritmos, incluso obteniendo gaps menores a un 10%, para 100 nodos, en un conjunto de instancias. En relación a las heurísticas, se desarrolla una heurística constructiva, la que utiliza la información de los intervalos,a diferencia de todas las aproximaciones de la literatura. Se proponen metaheurísticas basadas enBúsqueda Local (Mejora iterativa, Simulated Annealing
y GRASP), se obtienen gaps de calidad,incluso para las instancias más complejas.Se realiza una comparación desde un punto de vista experimental, mostrando un buen desempeño, pues se igualan o mejoran los resultados existentes.

Diversity selection is a common task in multiple real-world problems. The maximum diversity problem traditionally consists in selecting a subset of items from a given set, such that a measure of the diversity present in the subset of selected elements is maximized. In literature are reported several variants of this problem, according to the diversity function considered, in this investigation it solves a new problem, called the Max-mean model, which maximizes the average diversity between elements in the subset selected, and in on which the number of elements selected is also a decision variable, we show that finding an optimal solution to this problem is strongly NP-hard. The characteristics of the inter-element distances considered in the new model has applications in the selection of groups of people representative of a population, or in the selection of a group of institutions, etc. In the first part of this research, this new model is proposed, we study some of its properties, and is designed a highly efficient algorithm to solve it, based on a hybridization of metaheuristics with good performance in other similar combinatorial problems, to estimate the efficiency of the proposed method are adapted the best algorithms to solve similar problems, as reported in literature. In the second part of this research extends the optimization problem considered, an additional constraint is added: want also select the best items, the best according to some criterion. In this context, with this additional objective, the optimization problem becomes a classical multiobjective optimization problem. With simple processing techniques this problem can be transformed into the problem uniobjective above, for which we developed a very efficient method. This will solve the general problem of selecting the best and most diverse.

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One of the most common systems in non-automated warehouses, is drive-in pallet racking with a shared storage policy(which is usually based on the duration-of-stay). Such scheme targets towards an efficient use of storage space, since itsoperation costs are directly related to the size and layout of the warehouse. In this paper, two mathematical programmingmodels and two greedy-randomised based heuristics for finding (nearly) optimal storage and retrieval operation sequencesfor this type of storage system are proposed. The computational effectiveness of the proposed approaches is measured byconsidering two sets of synthetic instances. The obtained results show that the proposed heuristics are not only able tocompute high-quality solutions (as observed when being compared with the optimal solutions attained by the mathematicalprogramming models), but it is also capable of providing solutions in very short running times even for large instances forwhich the mathematical programming model failed to find feasible solutions. At the light of these results, the best heuristicis also tested using a rolling-horizon planning strategy in a real-world case study, obtained from a Chilean company. It turnsout that the attained results are more effective than the company’s current storage policy

Energy is a vital input for social and economic development. As a result of the generalization of agricultural, industrial and domestic activities the demand for energy has increased remarkably, especially in emergent countries. This has meant rapid grower in the level of greenhouse gas emissions and the increase in fuel prices, which are the main driving forces behind efforts to utilize renewable energy sources more effectively, i.e. energy which comes from natural resources and is also naturally replenished. Despite the obvious advantages of renewable energy, it presents important drawbacks, such as the discontinuity of generation, as most renewable energy resources depend on the climate, which is why their use requires complex design, planning and control optimization methods. Fortunately, the continuous advances in computer hardware and software are allowing researchers to deal with these optimization problems using computational resources, as can be seen in the large number of optimization methods that have been applied to the renewable and sustainable energy field. This paper presents a review of the current state of the art in computational optimization methods applied to renewable and sustainable energy, offering a clear vision of the lastest research advances in this field.

Energy is a vital input for social and economic development. As a result of the generalization of agricultural, industrial and domestic activities the demand for energy has increased remarkably, especially in emergent countries. This has meant rapid grower in the level of greenhouse gas emissions and the increase in fuel prices, which are the main driving forces behind efforts to utilize renewable energy sources more effectively, i.e. energy which comes from natural resources and is also naturally replenished. Despite the obvious advantages of renewable energy, it presents important drawbacks, such as the discontinuity of generation, as most renewable energy resources depend on the climate, which is why their use requires complex design, planning and control optimization methods. Fortunately, the continuous advances in computer hardware and software are allowing researchers to deal with these optimization problems using computational resources, as can be seen in the large number of optimization methods that have been applied to the renewable and sustainable energy field. This paper presents a review of the current state of the art in computational optimization methods applied to renewable and sustainable energy, offering a clear vision of the latest research advances in this field.

Tile temporal coupling between the transport and grasp components of prehension movements was investigated through two experiments. In Experiment !, six normal subjects were required to reach and grasp each of three spheres located at three different distances (Blocked trials). In Experiment 2, a visual perturbation paradigm was used in which the location of the object to be reached and grasped could change at the beginning ofarm movement (Perturbed trials). The same subjects participated in both experiments. Kinematics of wrist trajectory (transport component) and ofdistance between thumb and index finger (grasp component) were analyzed. The results of Experiment 1 showed that the two components could be temporally coupled during their time course. In Experiment 2, the visual perturbation affected both the components, but different times were required by each component to reorganize the movement towards the new target. These different times caused the decoupling of those events that appeared synchronized in Experiment 1. Finally, evidence was found to suggest that planning of grip formation takes into account not only the perceived characteristics of the object, but also the time planned by the transport component to reach the object.

Clásicamente, el problema de la diversidad máxima consiste en seleccionar un número fijo de elementos, desde un conjunto, de tal manera que una medida de diversidad sea maximizada. En esta investigación analizamos un nuevo modelo, denominado Max-Mean, en el cuál se maximiza la diversidad promedio, con el número de elementos a seleccionar también variable de decisión. El nuevo modelo es muy útil para casos en los cuáles las distancias representan afinidades y por tanto no se restringe a que sólo tomen valores no negativos. Se desarrolla un algoritmo híbrido muy eficiente basado en GRASP, al que incorporamos las estrategias de búsqueda en vecindades variables y rencadenamiento de trayectorias. Y Se prueba que este algoritmo es muy eficiente en comparación con otras heurísticas propuestas para otros problemas de diversidad máxima.

Kinesthetic awareness is important to successfully navigate the environment. When we interact with our daily surroundings, some aspects of movement are deliberately planned, while others spontaneously occur below conscious awareness. The deliberate component of this dichotomy has been studied extensively in several contexts, while the spontaneous component remains largely under-explored. Moreover, how perceptual processes modulate these movement classes is still unclear. In particular, a currently debated issue is whether the visuomotor system is governed by the spatial percept produced by a visual illusion or whether it is not affected by the illusion and is governed instead by the veridical percept. Bistable percepts such as 3D depth inversion illusions (DIIs) provide an excellent context to study such interactions and balance, particularly when used in combination with reach-to-grasp movements. In this study, a methodology is developed that uses a DII to clarify the role of top-down processes on motor action, particularly exploring how reaches toward a target on a DII are affected in both deliberate and spontaneous movement domains.

Energy is a vital input for social and economic development. As a result of the generalization of agricultural, industrial and domestic activities the demand for energy has increased remarkably, especially in emergent countries. This has meant rapid grower in the level of greenhouse gas emissions and the increase in fuel prices, which are the main driving forces behind efforts to utilize renewable energy sources more effectively, i.e. energy which comes from natural resources and is also naturally replenished. Despite the obvious advantages of renewable energy, it presents important drawbacks, such as the discontinuity of generation, as most renewable energy resources depend on the climate, which is why their use requires complex design, planning and control optimization methods. Fortunately, the continuous advances in computer hardware and software are allowing researchers to deal with these optimization problems using computational resources, as can be seen in the large number of optimization methods that have been applied to the renewable and sustainable energy field. This paper presents a review of the current state of the art in computational optimization methods applied to renewable and sustainable energy, offering a clear vision of the lastest research advances in this field.

Detection of community structure in graphs remains up to this date a computationally challenging problem despite the efforts of many researchers from various scientific fields in the past few years. The modularity value of a set of vertex clusters in a graph is a widely used quality measure for community structure, and the relating problem of finding a partition of the vertices into clusters such that the corresponding modularity is maximized is an NP-Hard problem. A Greedy Randomized Adaptive Search Procedure (GRASP) with path relinking is presented in this paper, for modularity maximization in undirected graphs. A new class of {0, 1} matrices is introduced which characterizes the family of clusterings in a graph, and a distance function is given which enables us to define an l-neighborhood local search which generalizes most of the related local search methods that have appeared in the literature. Computational experiments comparing the proposed algorithm with other heuristics from the literature in a set of some well known benchmark instances, indicate that our implementation of GRASP with path relinking consistently produces better quality solutions.

As mobile and tangible devices are getting smaller and smaller it is desirable to extend the interaction area to their whole surface area. The HandSense prototype employs capacitive sensors for detecting when it is touched or held against a body part. HandSense is also able to detect in which hand the device is held, and how. The general properties of our approach were confirmed by a user study. HandSense was able to correctly classify over 80 percent of all touches, discriminating six different ways of touching the device (hold left/right, pick up left/right, pick up at top/bottom). This information can be used to implement or enhance implicit and explicit interaction with mobile phones and other tangible user interfaces. For example, graphical user interfaces can be adjusted to the user's handedness.

One of the most common systems in non-automated warehouses, is drive-in pallet racking with a shared storage policy (which is usually based on the duration-of-stay). Such scheme targets towards an efficient use of storage space, since its operation costs are directly related to the size and layout of the warehouse. In this paper, two mathematical programming models and two greedy-randomised based heuristics for finding (nearly) optimal storage and retrieval operation sequences for this type of storage system are proposed. The computational effectiveness of the proposed approaches is measured by considering two sets of synthetic instances. The obtained results show that the proposed heuristics are not only able to compute high-quality solutions (as observed when being compared with the optimal solutions attained by the mathematical programming models), but it is also capable of providing solutions in very short running times even for large instances for which the mathematical programming model failed to find feasible solutions. At the light of these results, the best heuristic is also tested using a rolling-horizon planning strategy in a real-world case study, obtained from a Chilean company. It turns out that the attained results are more effective than the company's current storage policy.

W e propose and describe a hybrid GRASP with weight perturbations and adaptive path-relinking heuristic (HGP + PR) for the Steiner problem in graphs. In this multistart approach, the greedy randomized construction phase of a GRASP is replaced by the use of several construction heuristics with a weight perturbation strategy that combines intensification and diversification elements, as in a strategic oscillation approach. The improvement phase circularly explores two different local search strategies. The first uses a node-based neighborhood for local search, while the second uses a key-path-based neighborhood. An adaptive path-relinking technique is applied to a set of elite solutions as a post-optimization strategy. Computational results on a broad set of benchmark problems illustrate the effectiveness and the robustness of our heuristic, which is very competitive when compared to other approximate algorithms.

We develop a self-energy screening approximation suitable for multi-electron atoms. c This approximation is tested in a number of few-and many-electron systems. c We obtain superior agreement with experiment compared with existing approximations. c An implementation of this approximation is provided for use with GRASP2K.

Energy is a vital input for social and economic development. As a result of the generalization of agricultural, industrial and domestic activities the demand for energy has increased remarkably, especially in emergent countries. This has meant rapid grower in the level of greenhouse gas emissions and the increase in fuel prices, which are the main driving forces behind efforts to utilize renewable energy sources more effectively, i.e. energy which comes from natural resources and is also naturally replenished. Despite the obvious advantages of renewable energy, it presents important drawbacks, such as the discontinuity of generation, as most renewable energy resources depend on the climate, which is why their use requires complex design, planning and control optimization methods. Fortunately, the continuous advances in computer hardware and software are allowing researchers to deal with these optimization problems using computational resources, as can be seen in the large number of optimization methods that have been applied to the renewable and sustainable energy field. This paper presents a review of the current state of the art in computational optimization methods applied to renewable and sustainable energy, offering a clear vision of the lastest research advances in this field.

Hand anthropometry data are largely based on measurements of the hand in an outstretched hand posture and are, therefore, difficult to apply to tool gripping hand postures. The purpose of this project was to develop a representative, scalable hand model to be used with 3-D software drawing packages to aid in the ergonomic design of hand tools. Landmarks (66) on the palmar surface of the right hand of 100 subjects were digitised in four functional hand postures and, from these, 3-D surface models of a mean, 25th and 75th% hand were developed. The root mean square differences in hand length between the hand model and the digitised data for the 25th, 50th and 75th percentile hand were 11.4, 3.2 and 8.9 mm, respectively. The corresponding values for hand breadth were 2.0, 0.4 and 1.4 mm. There was good agreement between distances on the digitised hand and the hand model. The application of this research includes improved ergonomic hand tool design through the use of hand anthropometry reference values developed from the general population using grasping hand postures.

A gestão de transportes públicos carece de ferramen-tas que auxiliem no planejamento adequado dos recursos, uma vez que há uma grande diversidade de cenários a serem atendidos e um alto custo para prover o serviço em regiões de demanda baixa, variável e imprevisível. Dada a busca por alternativas que possibilitem o serviço sem a degradação da qualidade oferecida e não aumente os custos dos fornecedores, o problema de Roteamento de Veículos Dinâmico para Transporte Reativo a Demanda foi proposto. Neste trabalho, o problema é resolvido usando uma combinação de metaheuristicas, estratégias de busca local e de movimentos para melhorar a solução do problema. Os resultados obtidos, ao serem comparados à uma abordagem semelhante, mostram que a presente proposta apresenta melhores resultados uma vez que a solução obtida é melhor do que a solução inicialmente comparada. Index Terms-Roteamento de Veículos, Transporte Reativo a Demanda, GRASP e Busca Tabu I. INTRODUÇÃO O Problema de Roteamento de Veículos (PRV) pertence a uma classe de problemas de distribuição [1] cujo objetivo é determinar um conjunto de rotas de coleta e/ou entrega que minimize a distância total percorrida sujeito a restrições, que podem ser: tempo, tamanho da frota, demanda, entre outros. Segundo Giani et al. [2], quando os dados de entrada do PRV dependem explicitamente do tempo, ele é classificado como dinâmico; caso contrário, estático. Quandos todos os dados são conhecidos ao determinar a solução classifica-se em determinístico e em caso contrário, estocástico. Diante disso, citaremos cada combinação de problema resultante acompanhada de um exemplo: Estático e Determinístico (PRV capacitado); Estático e Estocástico (PRV capacitado com de-mandas estocásticas); Dinâmico e Determinístico (Problema do Caixeiro Viajante com tempos de viagem dependentes do horário); Dinâmico e Estocástico (PRV baseado em informa-ção de tráfego on line). O transporte reativo a demanda, do inglês Demand Responsive Transport (DRT), é um serviço de transporte sob demanda, perto das necessidades dos passageiros uma vez que há flexibilidade das rotas e horários dos veículos, passageiros determinam os locais e horários de embarque e desembarque [3], além de outros aspectos a serem considerados. Para Josselin et al. [4], um DRT Dinâmico seria um possível con-senso/compromisso entre Flexibilidade, Qualidade de Serviço e Eficiência. A Flexibilidade ocorre uma vez que desvios de rota inesperados poderão ocorrer a fim atender a solicitação recém-chegada de um passageiro ao sistema. Qualidade de Serviço assegura o atendimento de necessidades do passageiro durante a elaboração da rota: minimizar tempo a bordo, minimizar tempo de espera por atendimento, dentre outros. Por fim, Eficiência garante o atendimento das solicitações com um mínimo de veículos possíveis, respeitando as restrições de capacidade e nível de serviço aceitável para o passageiro. Segundo Gomes et al. [5], no problema de Roteamento de Veículos Dinâmico para Transportes Reativos a Demanda (PRVDTRD) as rotas e frequências podem variar de acordo com a demanda observada (solicitações de transporte recebidas em qualquer momento que possuem origem e destino, com as suas respectivas janelas de tempo, definidas pelos passageiros). Cada vértice de parada pertencente a uma rota, exceto o depó-sito, aceita a coleta e entrega simultânea de passageiros, isto é, pode ter múltiplas janelas de tempo com possível sobreposição e por isso, as novas solicitações recebidas em tempo real po-dem exigir a visita neste em diferentes períodos, diferenciando das variações PRV e Dial-a-Ride Problem (DARP). A frota de veículos possui igual capacidade (número de assentos), com início e término das rotas no único depósito existente. Como possui múltiplos objetivos que conflitam entre si, o problema assemelha-se a um DARP multi-objetivo [6], não sendo possível determinar a melhor solução, mas o conjunto de soluções eficientes que mais se aproxima do conjunto de soluções eficientes do problema e satisfaz as necessidades de-sejadas: Qualidade de Serviço (minimizar o tempo de espera e tempo de viagem do passageiro), Flexibilidade (rotas flexíveis) e Eficiência (minimizar o custo da rota total e maximizar as solicitações atendidas). O PRVDTRD tratado em [5] tem as seguintes característi-cas: múltiplos veículos com igual capacidade; único depósito onde os veículos iniciam e terminam as rotas; embarque e desembarque simultâneos; usuários especificam solicitações de transporte de qualquer lugar para qualquer lugar (many-to-many), em qualquer tempo (dynamic); usuários especificam

The Job-Shop Scheduling Problem (JSSP) is well known for its complexity as an NP-hard disjunctive scheduling problem. The problem addressed in this paper is JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints. An efficient GRASP Â ELS approach is introduced for solving this problem. The efficiency is evaluated using the widely known 40 Laurence's instances which encompass medium and large scale instances. The computational results prove that the proposed method competes with the best published methods in both quality of results and computational time. Recently, Web services have generated great interest in researchers. Such application architecture is based on the client-server model using existing Internet protocols and open standards. It provides new approaches to optimization methods. The proposed GRASP Â ELS is packaged into a Web Service (WS), i.e., it offers for the research community an open access to our optimization approach. Moreover, the proposed web service can be even included in research future works with a very small programming effort.

The work addressed in this paper is motivated from a real problem proposed to the authors by a bakery company in Northern Spain. The objective is to minimize the total distance travelled for the daily routes over the week. In order to reduce this total distance, some flexibility in the dates of delivery is introduced. A mixed-integer linear model for the problem is formulated. In addition, a two-phase method based in GRASP and path-relinking metaheuristic strategies is proposed. Computational experiments show that the method performs very well, obtaining high-quality solutions in short computational times. Moreover, when it is applied to real-data-based instances, the obtained solutions considerably reduce transportation costs over the planning horizon.

GRASP (Greedy Randomized Adaptive Search Procedures) is a multistart metaheuristic for producing good-quality solutions of combinatorial optimization problems. Each GRASP iteration is usually made up of a construction phase, where a feasible solution is constructed, and a local search phase which starts at the constructed solution and applies iterative improvement until a locally optimal solution is found. While, in general, the construction phase of GRASP is a randomized greedy algorithm, other types of construction procedures have been proposed. Repeated applications of a construction procedure yields diverse starting solutions for the local search. This chapter gives an overview of GRASP describing its basic components and enhancements to the basic procedure, including reactive GRASP and intensification strategies.

This paper will present a case study on the use of simulation to develop and implement an assembly line for the assembly and test of customer located telephony equipment. The simulation model was used as a tool to assist in development and integration of the assembly and test processes with a focus on capacity, material flow optimization, and equipment layout. The authors will discuss how the model affected the facilities layout, equipment specifications, and material flow.

This paper describes a graphical-user-interface and a heuristic based on a greedy randomised adaptive search procedure which have been developed to work in combination to tackle the basic vehicle routing problem (VRP). Customers of known demand are supplied from a single depot by vehicles which are subject to a weight limit and, in some cases, to a limit on the distance travelled. Only one vehicle is allowed to supply each customer. The system works in a Windows environment with a familiar look and feel for the majority of computer users.

AbstractÐThis paper introduces GRASP (Generic seaRch Algorithm for the Satisfiability Problem), a new search algorithm for Propositional Satisfiability (SAT). GRASP incorporates several search-pruning techniques that proved to be quite powerful on a wide variety of SAT problems. Some of these techniques are specific to SAT, whereas others are similar in spirit to approaches in other fields of Artificial Intelligence. GRASP is premised on the inevitability of conflicts during the search and its most distinguishing feature is the augmentation of basic backtracking search with a powerful conflict analysis procedure. Analyzing conflicts to determine their causes enables GRASP to backtrack nonchronologically to earlier levels in the search tree, potentially pruning large portions of the search space. In addition, by ªrecordingº the causes of conflicts, GRASP can recognize and preempt the occurrence of similar conflicts later on in the search. Finally, straightforward bookkeeping of the causality chains leading up to conflicts allows GRASP to identify assignments that are necessary for a solution to be found. Experimental results obtained from a large number of benchmarks indicate that application of the proposed conflict analysis techniques to SAT algorithms can be extremely effective for a large number of representative classes of SAT instances.

This Paper proposes a compound robotic hand that benefits the features of both underactuated and continuous systems. This hand consists of two underactuated tendon-driven fingers and a single section continuous finger. The continuous finger helps to increase the Manipulation capabilities of grasping. Each underactuated finger is composed of three phalanges that are actuated with one tendon with appropriate embedded tendon guide, and the continuous finger is driven by three cables. By pulling the wire using mechanically driven actuators, the flexion of the three-phalange fingers and the desired shape of the continuous finger are obtained. After the conceptual design, fabrication, and performance analysis of the hand, Kinematic equations of the continuous finger are discussed, and finally, the form-closure property of grasp is studied.

Brain-Computer Interfaces are promising technologies that can improve Human-Robot Interaction, especially for disabled and impaired individuals. Noninvasive BCI's, which are very desirable from a medical and therapeutic perspective, are only able to deliver noisy, low-bandwidth signals, making their use in complex tasks difficult. To this end, we present a shared control online grasp planning framework using an advanced EEG-based interface. Unlike commonly used paradigms, the EEG interface we incorporate allows online generation of a flexible number of options. This online planning framework allows the user to direct the planner towards grasps that reflect their intent for using the grasped object by successively selecting grasps that approach the desired approach direction of the hand. The planner divides the grasping task into phases, and generates images that reflect the choices that the planner can make at each phase. The EEG interface is used to recognize the user's preference among a set of options presented by the planner. The EEG signal classifier is fast and simple to train, and the system as a whole requires almost no learning on the part of the subject. Three subjects were able to successfully use the system to grasp and pick up a number of objects in a cluttered scene.

Several methods to select variables that are subsequently used in discriminant analysis are proposed and analysed. The aim is to find from among a set of m variables a smaller subset which enables an efficient classification of cases. Reducing dimensionality has some advantages such as reducing the costs of data acquisition, better understanding of the final classification model, and an increase in the efficiency and efficacy of the model itself. The specific problem consists in finding, for a small integer value of p, the size p subset of original variables that yields the greatest percentage of hits in the discriminant analysis. To solve this problem a series of techniques based on metaheuristic strategies is proposed. After performing some test it is found that they obtain significantly better results than the Stepwise, Backward or Forward methods used by classic statistical packages. The way these methods work is illustrated with several examples.

In this paper, a new modified version of Greedy Randomized Adaptive Search Procedure (GRASP), called Multiple Phase Neighborhood Search-GRASP (MPNS-GRASP), is proposed for the solution of the Traveling Salesman Problem. In this method, some procedures have been included to the classical GRASP algorithm in order to improve its performance and to cope with the major disadvantage of GRASP which is that it does not have a stopping criterion that will prevent the algorithm from spending time in iterations that give minor, if any, improvement in the solution. Thus, in MPNS-GRASP a stopping criterion based on Lagrangean Relaxation and Subgradient Optimization is proposed. Also, a different way for expanding the neighborhood search is used based on a new strategy, the Circle Restricted Local Search Moves strategy. A new variant of the Lin-Kernighan algorithm, called Random Backtracking Lin-Kernighan that helps the algorithm to diversify the search in non-promising regions of the search space is used in the Expanding Neighborhood Search phase of the algorithm. Finally, a Path Relinking Strategy is used in order to explore trajectories between elite solutions. The proposed algorithm is tested on numerous benchmark problems from TSPLIB with very satisfactory results.

Generalized Regression Analysis and Spatial Prediction (GRASP) was used to map the spatial distribution of swordfish (Xiphias gladius) in the South Atlantic. Generalized additive models (GAMs) were used to relate catch to environmental predictor variables. Catch information from 38,000 Brazilian pelagic longline sets from 1980 to 2000 and size frequency data from 5000 longline sets from 1982 to 2000 were obtained from International Commission for the Conservation of Atlantic Tuna (ICCAT). Results highlight the importance of environmental variables for the fishery and for the spatial distribution of different size classes of swordfish (small, intermediate and large). The distribution of swordfish was closely associated with convergence zones (inter-tropical and subtropical), especially in the months of greatest convergence intensity. Spatial distribution patterns differed for the three studied size classes. The smallest size classes were found mainly in coastal zones and in areas with a shallow mixed layer (<20 m). In contrast intermediate-sized swordfish were mostly associated with the inter-tropical convergence and mixed layers of more than 20 m depth and large swordfish were often found in the vicinity of the subtropical convergence zone and in areas with mixed layers of less than 25 m and greater than 60 m.

We address the so-called subset selection problem in multiple linear regression where the objective is to select a minimal subset of predictor variables without sacrificing any explanatory power. A new mathematical programming model is proposed for this purpose. A parametric solution of this model yields a number of efficient subsets. To obtain this solution, we repeatedly use an exact or one of two heuristic algorithms. The subsets generated in this way are compared with the ones generated by several standard procedures. The results suggest that, in most cases, our approach finds subsets that compare favorably against the standard procedures (in terms of generally accepted measures such as adjusted R 2 and

Over recent years, several nonlinear time series models have been proposed in the literature. One model that has found a large number of successful applications is the threshold autoregressive model (TAR). The TAR model is a piecewise linear process whose central idea is to change the parameters of a linear autoregressive model according to the value of an observable variable, called the threshold variable. If this variable is a lagged value of the time series, the model is called a self-exciting threshold autoregressive (SETAR) model. In this paper, we propose a heuristic to estimate a more general SETAR model, where the thresholds are multivariate. We formulate the task of finding multivariate thresholds as a combinatorial optimization problem. We develop an algorithm based on a Greedy Randomized Adaptive Search Procedure (GRASP) to solve the problem. GRASP is an iterative randomized sampling technique that has been shown to quickly produce good quality solutions for a wide variety of optimization problems. The proposed model performs well on both simulated and real data.

A greedy randomized adaptive search procedure (GRASP) is a randomized heuristic that has been shown to quickly produce good quality solutions for a wide variety of combinatorial optimization problems. In this paper, we describe a GRASP for the quadratic assignment problem. We review basic concepts of GRASP: construction and local search algorithms. The implementation of GRASP for the quadratic assignment problem is described in detail. Computational experience on a large set of standard test problems (QAPLIB) is presented.

We describe Fortran subroutines for finding approximate solutions of sparse instances of the Quadratic Assignment Problem (QAP) using a Greedy Randomized Adaptive Search Procedure (GRASP). The design and implementation of the code are described in detail. Computational results comparing the new subroutines with a dense version of the code (Algorithm 754, ACM TOMS) show that the speedup increases with the sparsity of the data.

The Maximum Diversity Problem (MDP) consists in identifying, in a population, a subset of elements, characterized by a set of attributes, that present the most diverse characteristics between themselves. The identification of such solution is an NP-hard problem. Some heuristics are available to obtain approximate solutions for this problem. In this paper we propose different GRASP heuristics for the MDP, using distinct construction procedures and path-relinking techniques. Performance comparison between related work and the proposed heuristics is provided. Experimental results show that the new GRASP heuristics are quite robust and are able to find high-quality solutions in a feasible computational time.

The set multicovering or set k-covering problem is an extension of the classical set covering problem, in which each object is required to be covered at least k times. The problem finds applications in the design of communication networks and in computational biology. We describe a GRASP with pathrelinking heuristic for the set k-covering problem, as well as the template of a family of Lagrangean heuristics. The hybrid GRASP Lagrangean heuristic employs the GRASP with path-relinking heuristic using modified costs to obtain approximate solutions for the original problem. Computational experiments carried out on 135 test instances show experimentally that the Lagrangean heuristics performed consistently better than GRASP. By properly tuning the parameters of the GRASP Lagrangean heuristic, it is possible to obtain a good trade-off between solution quality and running times. Furthermore, the GRASP Lagrangean heuristic makes better use of the dual information provided by subgradient optimization and is able to discover better solutions and to escape from locally optimal solutions even after the stabilization of the lower bounds, when other strategies fail to find new improving solutions.

The wake effect is the key factor affecting the low efficiency of wind power production. It is very important to predict the relationship between the cost and the produced power for various wind-turbine placements under various wind speeds and directions. This paper proposes a GRASP-VNS algorithm for the optimal placement of wind turbines. Four different wind-farm conditions were considered: (a) uniform wind with single direction, (b) uniform wind with variable directions, (c) non-uniform wind with variable directions, and (d) non-uniform and variable-direction wind with land constraint. The proposed GRASP-VNS algorithm combines two well-known metaheuristics, GRASP and VNS, to create additional advantages in yielding the search trajectory. Intensive experiments assuming the four windfarm conditions were performed. Statistical analyses show that the proposed GRASP-VNS algorithm significantly outperforms three existing GA-based methods.