Catálogo de publicaciones - libros

We develop a primal heuristic based on a genetic algorithm for the minimum graph bisection problem and incorporate it in a branch-and-cut framework. The problem concerns partitioning the nodes of a weighted graph into two subsets such that the total weight of each set is within some lower and upper bounds. The objective is to minimize the total cost of the edges between both subsets of the partition. We formulate the problem as an integer program. In the genetic algorithm the LP-relaxation of the IP-formulation is exploited. We present several ways of using LP information and demonstrate the computational success.

In this paper we investigate and compare multi-objective and weighted single objective approaches to a real world workforce scheduling problem. For this difficult problem we consider the trade off in solution quality versus population diversity, for different sets of fixed objective weights. Our real-world workforce scheduling problem consists of assigning resources with the appropriate skills to geographically dispersed task locations while satisfying time window constraints. The problem is NP-Hard and contains the Resource Constrained Project Scheduling Problem (RCPSP) as a sub problem. We investigate a genetic algorithm and serial schedule generation scheme together with various multi-objective approaches. We show that multi-objective genetic algorithms can create solutions whose fitness is within 2% of genetic algorithms using weighted sum objectives even though the multi-objective approaches know nothing of the weights. The result is highly significant for complex real-world problems where objective weights are seldom known in advance since it suggests that a multi-objective approach can generate a solution close to the user preferred one without having knowledge of user preferences.

A new model for evolving the structure of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The evolved PSO algorithm is compared to a human-designed PSO algorithm by using ten artificially constructed functions and one real-world problem. Numerical experiments show that the evolved PSO algorithm performs similarly and sometimes even better than standard approaches for the considered problems.

In this paper a tabu search algorithm is proposed for the optimization of constrained gas distribution networks. The problem consists in finding the least cost combination of diameters, from a discrete set of commercially available ones, for the pipes of a given gas network, satisfying the constraints related to minimum pressure requirements and upstream pipe conditions. Since this is a nonlinear mixed integer problem, metaheuristic approaches seem to be more suitable and to provide better results than classical optimization methods. In this work, a tabu search heuristics is applied to the problem and the results of the proposed algorithm are compared with the results of a genetic algorithm and two other versions of tabu search algorithms. The results are very promising, regarding both quality of solutions and computational time.

In this paper we address the joint problem of minimising both the transport and inventory costs of a retail chain that is supplied from a central warehouse. We propose a hybrid evolutionary algorithm where the delivery patterns are evolved for each shop, while the delivery routes are obtained employing the multistart sweep algorithm. The experiments performed show that this method can obtain acceptable results consistently and within a reasonable timescale. The results are also of a lower cost than those obtained by other strategies employed in previous research. Furthermore, they confirm the interest of addressing the optimisation problem jointly, rather than minimising separately inventory and transport.

Constructive greedy heuristics are algorithms that try to iteratively construct feasible solutions for combinatorial optimization problems from the scratch. For this they make use of a greedy scoring function, which evaluates the myopic impact of each possible element with respect to the solution under construction. Although fast, effective, and even exact for some problem classes, greedy heuristics might construct poor solution when applied to difficult (NP-hard) problems. To avoid such pitfalls we suggest the approach of parametrizing the scoring function by including several different myopic aspects at once, which are weighted against each other. This so-called pgreedy approach can be embedded into the metaheuristic concept of GRASP. The hybrid metaheuristic of GRASP with a parametrized scoring function is called parametrized GRASP heuristic (PGRASP). We present a PGRASP algorithm for the axial three index assignment problem (AP3) and computational results comparing PGRASP with the classical GRASP strategy.

Bucket elimination (BE) is an exact technique based on variable elimination, commonly used for solving constraint satisfaction problems. We consider the hybridization of BE with evolutionary algorithms endowed with tabu search. The resulting memetic algorithm (MA) uses BE as a mechanism for recombining solutions, providing the best possible child from the parental set. This MA is applied to the maximum density still life problem. Experimental tests indicate that the MA provides optimal or near-optimal results at an acceptable computational cost.

In this paper we investigate the properties of CEAs with populations structured as Watts–Strogatz small-world graphs and Albert–Barabási scale-free graphs as problem solvers, using several standard discrete optimization problems as a benchmark. The EA variants employed include self-adaptation of mutation rates. Results are compared with the corresponding classical panmictic EA showing that topology together with self-adaptation drastically influences the search.

This paper presents a competitive Particle Swarm Optimization algorithm for the Traveling Salesman Problem, where the velocity operator is based upon local search and path-relinking procedures. The paper proposes two versions of the algorithm, each of them utilizing a distinct local search method. The proposed heuristics are compared with other Particle Swarm Optimization algorithms presented previously for the same problem. The results are also compared with three effective algorithms for the TSP. A computational experiment with benchmark instances is reported. The results show that the method proposed in this paper finds high quality solutions and is comparable with the effective approaches presented for the TSP.

Cellular Genetic Algorithms (cGA) are spatially distributed Genetic Algorithms that, because of their high level of diversity, are superior to regular GAs on several optimization functions. Also, since these distributed algorithms only require communication between few closely arranged individuals, they are very suitable for a parallel implementation. We propose a new kind of cGA, called hierarchical cGA (H-cGA), where the population structure is augmented with a hierarchy according to the current fitness of the individuals. Better individuals are moved towards the center of the grid, so that high quality solutions are exploited quickly, while at the same time new solutions are provided by individuals at the outside that keep exploring the search space. This algorithmic variant is expected to increase the convergence speed of the cGA algorithm and maintain the diversity given by the distributed layout. We examine the effect of the introduced hierarchy by observing the variable takeover rates at different hierarchy levels and we compare the H-cGA to the cGA algorithm on a set of benchmark problems and show that the new approach performs promising.