O problema do caixeiro viajante alugador : um estudo algorítmico

The Car Rental Salesman Problem (CaRS) is a variant of the classical Traveling Salesman Problem which was not described in the literature where a tour of visits can be decomposed into contiguous paths that may be performed in different rental cars. The aim is to determine the Hamiltonian cycle that results in a final minimum cost, considering the cost of the route added to the cost of an expected penalty paid for each exchange of vehicles on the route. This penalty is due to the return of the car dropped to the base. This paper introduces the general problem and illustrates some examples, also featuring some of its associated variants. An overview of the complexity of this combinatorial problem is also outlined, to justify their classification in the NPhard class. A database of instances for the problem is presented, describing the methodology of its constitution. The presented problem is also the subject of a study based on experimental algorithmic implementation of six metaheuristic solutions, representing adaptations of the best of state-of-the-art heuristic programming. New neighborhoods, construction procedures, search operators, evolutionary agents, cooperation by multi-pheromone are created for this problem. Furtermore, computational experiments and comparative performance tests are conducted on a sample of 60 instances of the created database, aiming to offer a algorithm with an efficient solution for this problem. These results will illustrate the best performance reached by the transgenetic algorithm in all instances of the dataset

Otimização em comitês de classificadores: uma abordagem baseada em filtro para seleção de subconjuntos de atributos

Traditional applications of feature selection in areas such as data mining, machine learning and pattern recognition aim to improve the accuracy and to reduce the computational cost of the model. It is done through the removal of redundant, irrelevant or noisy data, finding a representative subset of data that reduces its dimensionality without loss of performance. With the development of research in ensemble of classifiers and the verification that this type of model has better performance than the individual models, if the base classifiers are diverse, comes a new field of application to the research of feature selection. In this new field, it is desired to find diverse subsets of features for the construction of base classifiers for the ensemble systems. This work proposes an approach that maximizes the diversity of the ensembles by selecting subsets of features using a model independent of the learning algorithm and with low computational cost. This is done using bio-inspired metaheuristics with evaluation filter-based criteria

Aplicação do algoritmo de otimização por colônia de formigas sobre o problema do passeio do robô seletivo

This work seeks to propose and evaluate a change to the Ant Colony Optimization based on the results of experiments performed on the problem of Selective Ride Robot (PRS, a new problem, also proposed in this paper. Four metaheuristics are implemented, GRASP, VNS and two versions of Ant Colony Optimization, and their results are analyzed by running the algorithms over 32 instances created during this work. The metaheuristics also have their results compared to an exact approach. The results show that the algorithm implemented using the GRASP metaheuristic show good results. The version of the multicolony ant colony algorithm, proposed and evaluated in this work, shows the best results

Uma colônia de formigas para o caminho mais curto multiobjetivo

Multi-objective combinatorial optimization problems have peculiar characteristics that require optimization methods to adapt for this context. Since many of these problems are NP-Hard, the use of metaheuristics has grown over the last years. Particularly, many different approaches using Ant Colony Optimization (ACO) have been proposed. In this work, an ACO is proposed for the Multi-objective Shortest Path Problem, and is compared to two other optimizers found in the literature. A set of 18 instances from two distinct types of graphs are used, as well as a specific multiobjective performance assessment methodology. Initial experiments showed that the proposed algorithm is able to generate better approximation sets than the other optimizers for all instances. In the second part of this work, an experimental analysis is conducted, using several different multiobjective ACO proposals recently published and the same instances used in the first part. Results show each type of instance benefits a particular type of instance benefits a particular algorithmic approach. A new metaphor for the development of multiobjective ACOs is, then, proposed. Usually, ants share the same characteristics and only few works address multi-species approaches. This works proposes an approach where multi-species ants compete for food resources. Each specie has its own search strategy and different species do not access pheromone information of each other. As in nature, the successful ant populations are allowed to grow, whereas unsuccessful ones shrink. The approach introduced here shows to be able to inherit the behavior of strategies that are successful for different types of problems. Results of computational experiments are reported and show that the proposed approach is able to produce significantly better approximation sets than other methods

Reproductive Patterns and Birth Seasonality in a South-American Breeding Colony of Common Marmosets, Callithrix jacchus

SOUSA,M.B.C. et al. Reproductive Patterns and Birth Seasonality in a South-American Breeding Colony of Common Marmosets, Callithrix jacchus. Primates, v.40, n.2, p. 327-336, Apr. 1999.

O papel das rotas e da obtenção de informações sobre a eficiência no forrageio de dinoponera quadriceps em ambiente natural

In this study, we investigated the role of routes and information attainment for the queenless ant species Dinoponera quadriceps foraging efficiency. Two queenless ant colonies were observed in an area of Atlantic secondary Forest at the FLONA-ICMBio of Nisia Floresta, in the state of Rio Grande do Norte, northeastern Brazil, at least once a week. In the first stage of the study, we observed the workers, from leaving until returning to the colony. In the second stage, we introduced a acrylic plate (100 x 30 x 0,8 cm) on a selected entrance of the nest early in the morning before the ants left the nest. All behavioral recordings were done through focal time and all occurence samplings. The recording windows were of 15 minutes with 1 minute interval, and 5 minute intervals between each observation window. Foraging was the main activity when the workers were outside the nest. There was a positive correlation between time outside the nest and distance travelled by the ants. These variables influenced the proportion of resource that was taken to the nest, that is, the bigger its proportion, the longer the time outside and distance travelled during the search. That proportion also influenced the time the worker remained in the nest before a new trip, the bigger the proportion of the item, the shorter was the time in the nest. During all the study, workers showed fidelity to the route and to the sectors in the home range, even when the screen was in the ant´s way, once they deviated and kept the route. The features of foraging concerning time, distance, route and flexibility to go astray by the workers indicate that decisions are made by each individual and are optimal in terms of a cost-benefit relation. The strategy chosen by queenless ants fits the central place foraging and marginal value theorem theories and demonstrate its flexibility to new informations. This indicates that the workers can learn new environmental landmarks to guide their routes