Vittorio Maniezzo

Ant Colony Optimization

Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The first algorithm which can be classified within this framework was presented in 1991 [21, 13] and, since then, many diverse variants of the basic principle have been reported in the literature. The essential trait of ACO algorithms is the combination of a priori information about the structure of a promising solution with a posteriori information about the structure of previously obtained good solutions.

A distributed geographic information system for the daily car pooling problem

following the difficulty of public transport to adequately cover all passenger transportation needs, different innovative mobility services are emerging. Among those are car pooling services, which are based on the idea that sets of car owners having the same travel destination share their vehicles. Until now these systems have had a limited use due to lack of an efficient information processing and communication support. In this study an integrated system for the organization of a car pooling service is presented, using several current Information and Communication Technologies (ICTs) technologies: web, GIS and SMS. The core of the system is an optimization module which solves heuristically the specific routing problem. The system has been tested in a real-life case study.

A variable neighborhood search for the capacitated arc routing problem with intermediate facilities

AbstractnThe capacitated arc routing problem (CARP) focuses on servicing edges of an undirected network graph. A wide spectrum of applications like mail delivery, waste collection or street maintenance outlines the relevance of this problem. A realistic variant of the CARP arises from the need of intermediate facilities (IFs) to load up or unload the service vehicle and from tour length restrictions. The proposed Variable Neighborhood Search (VNS) is a simple and robust solution technique which tackles the basic problem as well as its extensions. The VNS shows excellent results on four different benchmark sets. Particularly, for all 120 instances the best known solution could be found and in 71 cases a new best solution was achieved.n

Materialization of fragmented views in multidimensional databases

The most effective technique to enhance performances of multidimensional databases consists in materializing redundant aggregates called views. In the classical approach to materialization, each view includes all and only the measures of the cube it aggregates. In this paper we investigate the benefits of materializing views in vertical fragments, aimed at minimizing the workload response time. We formalize the fragmentation problem as a 0-1 integer linear programming problem, which is then solved by means of a standard integer programming solver to determine the optimal fragmentation for a given workload. Finally, we demonstrate the usefulness of fragmentation by presenting a large set of experimental results based on the TPC-H benchmark.

Scatter Search Methods for the Covering Tour Problem

The Covering Tour Problem (CTP) is a generalization of the Traveling Salesman Problem (TSP) which has several practical applications in the area of distribution network design. Given an undirected graph, the problem asks to identify a minimum cost cycle passing through a subset of vertices such that every vertex not in the cycle lies within a given distance from at least one node in the cycle. Being a generalization of the TSP, CTP is NP-hard. This paper presents three original Scatter Search heuristic algorithms for the CTP. Computational results are reported.

An Ant Approach to Membership Overlay Design

Designing an optimal overlay communication network for a set of processes on the Internet is a central problem of peer-to-peer (P2P) computing. Such a network defines membership and allows for members to disseminate information within the group. The network has to be robust and the available bandwidth has to be utilized in an optimal manner to allow for maximally efficient communication. This problem can be formulated as a dynamic optimization problem where classical combinatorial optimization techniques must face the further challenge of time-varying input data. ACO systems appear to be particularly fit for this class of problems, being able to construct an internal model of the instance to face and to exploit it for fast adaptation to modified contexts.

VERY STRONGLY CONSTRAINED PROBLEMS: AN ANT COLONY OPTIMIZATION APPROACH

Ant Colony Optimization (ACO) is a class of metaheuristic algorithms sharing the common approach of constructing a solution on the basis of information provided both by a standard constructive heuristic and by previously constructed solutions. This article is composed of three parts. The first one frames ACO in current trends of research on metaheuristics for combinatorial optimization. The second outlines some current research within the ACO community, reporting recent results obtained on different problems, while the third part focuses on a particular research line, named ANTS, providing some details on the algorithm and presenting results recently obtained on a prototypical strongly constrained problem: the set partitioning problem.

Cellular automata and roundabout traffic simulation

A new software package, named Archirota, for simulating traffic in roundabouts is introduced. Its simulation module is entirely based on cellular automata and is automatically configured for real-world geocoded data. Archirota can be used both as a support for designing new roundabout and for modeling and simulating existing ones. Tests on actual use cases testified the effectiveness of the implemented approach.

A PSYCHOGENETIC ALGORITHM FOR BEHAVIORAL SEQUENCE LEARNING

This work presents an original algorithmic model of some essential features of psychogenetic theory, as was proposed by J. Piaget. Specifically, we modeled some elements of cognitive structure learning in children from birth to four months of life. We are in fact convinced that the study of well-established cognitive models of human learning can suggest new, interesting approaches to problem so far not satisfactorily solved in the field of machine learning. Further, we discussed the possible parallels between our model and subsymbolic machine learning and neuroscience. The model was implemented and tested in some simple experimental settings, with reference to the task of learning sensorimotor sequences.

Soft variable fixing in path relinking: an application to aco codes

Soft variable fixing has emerged as one of the main techniques that the area of matheuristics can contribute to general metaheuristics. Recent years have in fact witnessed a fruitful interplay of methods that were originally proposed as general metaheuristcs with methods rooted in mathematic programming, which can be applied alone or as hybrids for solving combinatorial optimization problems. In this work, we show how one of the most effective matheuristics techniques, namely soft variable fixing, can be hybridized with Ant Colony Optimization. Specifically, we will combine a standard ACO code with a path relinking operator, implemented by means of soft variable fixing.