Juan M. Molina

Analysis of distributed genetic algorithms for solving cutting problems

In this paper, a solution to the three-stage two-dimensional cutting problem is presented by using sequential and parallel genetic algorithms (GAs). More specifically, an analysis of including distributed population ideas and parallelism in the basic GA are carried out to solve the problem more accurately and efficiently than with ordinary sequential techniques. Publicly available test problems have been used to illustrate the computational performance of the resulting metaheuristics. Experimental evidence in this work will show that the proposed algorithms outperform their sequential counterparts in time (high speedup with multiprocessors) and numerically (lower number of visited points during the search to find the solutions).

A New Heuristic for Solving the Parking Assignment Problem

It is often frustrating for drivers to find parking spaces, and parking itself is costly in almost every major city in the world. The search for a parking place is a task which can waste a lot of time and affect the efficiency of economic activities, social interactions, and the health of the environment. The planners of transport and city traffic must pay close attention to this issue in order to achieve an efficient management of mobility in smart cities. This work is intended to serve as an aid in the search for parking seeking the general interest of a group of drivers. The authors present an intensive description of the parking slots assignment problem for groups and apply it to a real case study. Also, they propose a hybrid genetic algorithm for solving this case and they compare it with three other algorithms in order to evaluate its performance.

Hybrid Ant Colony System to Solve a 2-Dimensional Strip Packing Problem

In this paper we present a study of an Ant Colony System (ACS) for the two-dimensional strip packing problem. In our computational study, we emphasize the influence of incorporating a simple optimization method at each cycle of the ACS. In this hybrid approach, local optimization is applied to a subset of the newly generated solutions to move them to a local optimum. We show that our ACS algorithm, when combined with a fine-tuned local search procedure, can compete with an existing genetic algorithm, reaching solutions of good quality and also exhibiting low execution times.

The influence of data implementation in the performance of evolutionary algorithms

In this paper we study the differences in performance among different implementations, in Java, of the data structures used in an evolutionary algorithm (EA). Typical studies on EAs performance deal only with different abstract representations of the data and pay no attention to the fact that each data representation has to be implemented in a concrete programming language which, in general, offers several possibilities, with differences in time consumed, that may be worthy of consideration.

Improvement heuristic for solving the one-dimensional bin-packing problem

We develop in the present paper a genetic algorithm for the one-dimensional bin packing problem. This algorithm performs a series of perturbations in an attempt to improve the current solution, applying some problem dependant genetic operators. Our procedure is efficient and easy to implement. We apply it to several benchmark instances taken from some problem sets and compare our results to those found in the literature. We find that our algorithm is able to generates competitive results compared to the best methods known so far and computes, for the first time, one optimal solution for one open benchmark instance.

Evolutionary and Ant Colony Optimization Based Approaches for a Two-Dimensional Strip Packing Problem

In the last few years, metaheuristic approaches have shown an important development in many application areas. This situation has turned them into one of the more appropriate candidates when dealing with difficult real-world problems for which timely, good-quality solutions are necessary. Furthermore, the class of metaheuristic approaches includes a large number of variants and designs which mainly depend on the concepts from which they are inspired. This chapter aims at giving an overview of Evolutionary Algorithms and Ant Colony Optimization when applied to the two-dimensional strip packing problem. The respective performance of these two metaheuristics are analyzed and compared from different perspectives by implementing a Genetic Algorithm and an Ant Colony System.

Analysis of Distributed Genetic Algorithms for Solving a Strip Packing Problem

This paper presents a solution of a constrained two dimensional strip packing problem using genetic algorithms. The constraint consists of considering three-stage guillotine patterns. This is quite a real constraint motivated by technological considerations in some industries. An analysis of including distributed population ideas and parallelism into the basic genetic algorithm is carried out to solve the problem accurately and efficiently. Experimental evidence in this work shows that the proposed parallel versions of the distributed algorithms outperform their sequential counterparts in time, although there are no significant differences either in the mean best values obtained or in the effort.