Catálogo de publicaciones - libros

Manifold possibilities of hybridizing individual metaheuristics with each other and/or with algorithms from other fields exist. A large number of publications documents the benefits and great success of such hybrids. This article overviews several popular hybridization approaches and classifies them based on various characteristics. In particular with respect to low-level hybrids of different metaheuristics, a unified view based on a common pool template is described. It helps in making similarities and different key components of existing metaheuristics explicit. We then consider these key components as a toolbox for building new, effective hybrid metaheuristics. This approach of thinking seems to be superior to sticking too strongly to the philosophies and historical backgrounds behind the different metaheuristic paradigms. Finally, particularly promising possibilities of combining metaheuristics with constraint programming and integer programming techniques are highlighted.

We consider the problems of packing rectangles, whose shapes are adjustable within given perimeter and area constraints. Using “sequence pairs” to specify relative positions of rectangles, we solve the resulting linear or convex programming problems to determine sizes and locations of all rectangles. To find good sequence pairs, we then resort to local search techniques. This is therefore a hybrid of local search and mathematical programming. The resulting algorithm can solve problem instances with up to 50 rectangles in reasonable amount of time.

The steelmaking process consists of two phases: primary steelmaking and finishing lines. The scheduling of the continuous galvanizing lines (CGL) is regarded as the most difficult process among the finishing lines due to its multi-objective and highly-constrained nature. In this paper, we present a multi-population parallel genetic algorithm (MPGA) with a new genetic representation called k ^ th nearest neighbor representation, and with a new communication operator for performing better communication between subpopulations in the scheduling of CGL. The developed MPGA consists of two phases. Phase one generates schedules from a primary work in process (WIP) inventory filtered according to the production campaign, campaign tonnage, priorities of planning department, and the due date information of each steel coil. If the final schedule includes the violations of some constraints, phase two repairs these violations by using a secondary WIP inventory of steel coils. The developed scheduling system is currently being used in a steel making company with encouraging preliminary results.

In island based genetic algorithms, the population is splitted into subpopulations which evolve independently and ocasionally communicate by sending some individuals. This way, several zones of the landscape are explored in parallel and solutions with different features can be discovered. The interchange of information is a key point for the performance of these algorithms, since the combination of those solutions usually produces better ones. In this work, it is proposed a method based in path relinking which makes the combination process more effective.

Hybridizing metaheuristic approaches becomes a common way to improve the efficiency of optimization methods. Many hybridizations deal with the combination of several optimization methods. In this paper we are interested in another type of hybridization, where datamining approaches are combined within an optimization process. Hence, we propose to study the interest of combining metaheuristics and datamining through a short survey that enumerates the different opportunities of such combinations based on literature examples.

This paper addresses a capacitated hub problem consisting of choosing the routes and the hubs to use in order to send a set of commodities from sources to destinations in a given capacitated network with minimum cost. The capacities and costs of the arcs and hubs are given, and the graph connecting the hubs is not assumed to be complete. For solving this problem we propose a heuristic approach that makes use of a linear programming relaxation in an Iterated Local Search scheme. The heuristic turns out to be very effective and the results of the computational experiments show that near-optimal solutions can be derived rapidly for instances of large size.

Variable neigborhood search is among the well studied local search based metaheuristics. It has provided good results for many combinatorial optimization problems throughout the last decade. Based on previous successful applications of this metaheuristic on various network design problems in telecommunications, we further enhance this approach by incorporating adaptive memory mechanisms from the scatter search and tabu search metaheuristics. The heuristics are compared among each other as well as against objective function values obtained from a mathematical programming formulation based on a commercial solver. The problem instances cover a large variety of networks and demand patterns. The analysis carried out in this paper corroborates that there are significant differences between the variable neighborhood search and the hybrid approach.

Deoxyribonucleic acid (DNA) sequencing is an important task in computational biology. In recent years the specific problem of DNA sequencing by hybridization has attracted quite a lot of interest in the optimization community. This led to the development of several metaheuristic approaches such as tabu search and evolutionary algorithms. In this work we propose an ant colony algorithm to resolve this problem. In addition, we apply our algorithm within a multi-level framework which helps in significantly reducing the computation time. The results show that our algorithm is currently among the state-of-the-art methods for this problem.

Different approaches in the hybridization of constraint programming and local search techniques have been recently proposed in the literature. In this paper we investigate two of them, namely the employment of local search to improve a solution found by constraint programming and the exploitation of a constraint model to perform the exploration of the local neighborhood. We apply the two approaches to a real-world personnel rostering problem arising at the department of neurology of the Udine University hospital and we report on computational studies on both real-world and randomly generated structured instances. The results highlight the benefits of the hybridization approach w.r.t. their component algorithms.

This paper presents a hybrid metaheuristic to address the vehicle routing problem with time windows (VRPTW). The VRPTW can be described as the problem of designing least cost routes from a depot to geographically dispersed customers. The routes must be designed such that each customer is visited only once by exactly one vehicle without violating capacity and time window constraints. The proposed solution method is a multi-start local search approach which combines reactively the systematic diversification mechanisms of Greedy Randomized Adaptive Search Procedures with a novel Variable Neighborhood Tabu Search hybrid metaheuristic for intensification search. Experimental results on well known benchmark instances show that the suggested method is both efficient and robust in terms of the quality of the solutions produced.