José García-Nieto

SMPSO: A new PSO-based metaheuristic for multi-objective optimization

In this work, we present a new multi-objective particle swarm optimization algorithm (PSO) characterized by the use of a strategy to limit the velocity of the particles. The proposed approach, called Speed-constrained Multi-objective PSO (SMPSO) allows to produce new effective particle positions in those cases in which the velocity becomes too high. Other features of SMPSO include the use of polynomial mutation as a turbulence factor and an external archive to store the non-dominated solutions found during the search. Our proposed approach is compared with respect to five multi-objective metaheuristics representative of the state-of-the-art in the area. For the comparison, two different criteria are adopted: the quality of the resulting approximation sets and the convergence speed to the Pareto front. The experiments carried out indicate that SMPSO obtains remarkable results in terms of both, accuracy and speed.

Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms

In this work we compare the use of a particle swarm optimization (PSO) and a genetic algorithm (GA) (both augmented with support vector machines SVM) for the classification of high dimensional microarray data. Both algorithms are used for finding small samples of informative genes amongst thousands of them. A SVM classifier with 10- fold cross-validation is applied in order to validate and evaluate the provided solutions. A first contribution is to prove that PSOsvm is able to find interesting genes and to provide classification competitive performance. Specifically, a new version of PSO, called Geometric PSO, is empirically evaluated for the first time in this work using a binary representation in Hamming space. In this sense, a comparison of this approach with a new GAsvm and also with other existing methods of literature is provided. A second important contribution consists in the actual discovery of new and challenging results on six public datasets identifying significant in the development of a variety of cancers (leukemia, breast, colon, ovarian, prostate, and lung).

Multi-Objective Particle Swarm Optimizers: An Experimental Comparison

Particle Swarm Optimization (PSO) has received increasing attention in the optimization research community since its first appearance in the mid-1990s. Regarding multi-objective optimization, a considerable number of algorithms based on Multi-Objective Particle Swarm Optimizers (MOPSOs) can be found in the specialized literature. Unfortunately, no experimental comparisons have been made in order to clarify which MOPSO version shows the best performance. In this paper, we use a benchmark composed of three well-known problem families (ZDT, DTLZ, and WFG) with the aim of analyzing the search capabilities of six representative state-of-the-art MOPSOs, namely, NSPSO, SigmaMOPSO, OMOPSO, AMOPSO, MOPSOpd, and CLMOPSO. We additionally propose a new MOPSO algorithm, called SMPSO, characterized by including a velocity constraint mechanism, obtaining promising results where the rest perform inadequately.

Intelligent OLSR Routing Protocol Optimization for VANETs

Recent advances in wireless technologies have given rise to the emergence of vehicular ad hoc networks (VANETs). In such networks, the limited coverage of WiFi and the high mobility of the nodes generate frequent topology changes and network fragmentations. For these reasons, and taking into account that there is no central manager entity, routing packets through the network is a challenging task. Therefore, offering an efficient routing strategy is crucial to the deployment of VANETs. This paper deals with the optimal parameter setting of the optimized link state routing (OLSR), which is a well-known mobile ad hoc network routing protocol, by defining an optimization problem. This way, a series of representative metaheuristic algorithms (particle swarm optimization, differential evolution, genetic algorithm, and simulated annealing) are studied in this paper to find automatically optimal configurations of this routing protocol. In addition, a set of realistic VANET scenarios (based in the city of Málaga) have been defined to accurately evaluate the performance of the network under our automatic OLSR. In the experiments, our tuned OLSR configurations result in better quality of service (QoS) than the standard request for comments (RFC 3626), as well as several human experts, making it amenable for utilization in VANET configurations.

A Study of Multiobjective Metaheuristics When Solving Parameter Scalable Problems

To evaluate the search capabilities of a multiobjective algorithm, the usual approach is to choose a benchmark of known problems, to perform a fixed number of function evaluations, and to apply a set of quality indicators. However, while real problems could have hundreds or even thousands of decision variables, current benchmarks are normally adopted with relatively few decision variables (normally from 10 to 30). Furthermore, performing a constant number of evaluations does not provide information about the effort required by an algorithm to get a satisfactory set of solutions; this information would also be of interest in real scenarios, where evaluating the functions defining the problem can be computationally expensive. In this paper, we study the effect of parameter scalability in a number of state-of-the-art multiobjective metaheuristics. We adopt a benchmark of parameter-wise scalable problems (the Zitzler-Deb-Thiele test suite) and analyze the behavior of eight multiobjective metaheuristics on these test problems when using a number of decision variables that range from 8 up to 2048. By using the hypervolume indicator as a stopping condition, we also analyze the computational effort required by each algorithm in order to reach the Pareto front. We conclude that the two analyzed algorithms based on particle swarm optimization and differential evolution yield the best overall results.

MALLBA: a software library to design efficient optimisation algorithms

In this paper we discuss on the MALLBA framework, a software tool for the resolution of combinatorial optimisation problems using generic algorithmic skeletons implemented in C++. Every skeleton in the MALLBA library implements an optimisation method (exacts, metaheuristics and hybrids) and provides three different implementations for it: sequential, parallel for Local Area Networks and parallel for Wide Area Networks. This paper introduces some aspects about the software design of the MALLBA library, details of the most recent implemented skeletons and offers computational results for a scheduling problem to illustrate the utilisation of our library.

Swarm intelligence for traffic light scheduling: Application to real urban areas

Congestion, pollution, security, parking, noise, and many other problems derived from vehicular traffic are present every day in most cities around the world. The growing number of traffic lights that control the vehicular flow requires a complex scheduling, and hence, automatic systems are indispensable nowadays for optimally tackling this task. In this work, we propose a Swarm Intelligence approach to find successful cycle programs of traffic lights. Using a microscopic traffic simulator, the solutions obtained by our algorithm are evaluated in the context of two large and heterogeneous metropolitan areas located in the cities of Malaga and Sevilla (in Spain). In comparison with cycle programs predefined by experts (close to real ones), our proposal obtains significant profits in terms of two main indicators: the number of vehicles that reach their destinations on time and the global trip time.

Sensitivity and specificity based multiobjective approach for feature selection: Application to cancer diagnosis

The study of the sensitivity and the specificity of a classification test constitute a powerful kind of analysis since it provides specialists with very detailed information useful for cancer diagnosis. In this work, we propose the use of a multiobjective genetic algorithm for gene selection of Microarray datasets. This algorithm performs gene selection from the point of view of the sensitivity and the specificity, both used as quality indicators of the classification test applied to the previously selected genes. In this algorithm, the classification task is accomplished by Support Vector Machines; in addition a 10-Fold Cross-Validation is applied to the resulting subsets. The emerging behavior of all these techniques used together is noticeable, since this approach is able to offer, in an original and easy way, a wide range of accurate solutions to professionals in this area. The effectiveness of this approach is proved on public cancer datasets by working out new and promising results. A comparative analysis of our approach using two and three objectives, and with other existing algorithms, suggest that our proposal is highly appropriate for solving this problem.

Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization

Optimal staging of traffic lights, and in particular optimal light cycle programs, is a crucial task in present day cities with potential benefits in terms of energy consumption, traffic flow management, pedestrian safety, and environmental issues. Nevertheless, very few publications in the current literature tackle this problem by means of automatic intelligent systems, and, when they do, they focus on limited areas with elementary traffic light schedules. In this paper, we propose an optimization approach in which a particle swarm optimizer (PSO) is able to find successful traffic light cycle programs. The solutions obtained are simulated with simulator of urban mobility, a well-known microscopic traffic simulator. For this study, we have tested two large and heterogeneous metropolitan areas with hundreds of traffic lights located in the cities of Bahía Blanca in Argentina (American style) and Málaga in Spain (European style). Our algorithm is shown to obtain efficient traffic light cycle programs for both kinds of cities. In comparison with expertly predefined cycle programs (close to real ones), our PSO achieved quantitative improvements for the two main objectives: 1) the number of vehicles that reach their destination and 2) the overall journey time.

Restart particle swarm optimization with velocity modulation: a scalability test

Large scale continuous optimization problems are more relevant in current benchmarks since they are more representative of real-world problems (bioinformatics, data mining, etc.). Unfortunately, the performance of most of the available optimization algorithms deteriorates rapidly as the dimensionality of the search space increases. In particular, particle swarm optimization is a very simple and effective method for continuous optimization. Nevertheless, this algorithm usually suffers from unsuccessful performance on large dimension problems. In this work, we incorporate two new mechanisms into the particle swarm optimization with the aim of enhancing its scalability. First, a velocity modulation method is applied in the movement of particles in order to guide them within the region of interest. Second, a restarting mechanism avoids the early convergence and redirects the particles to promising areas in the search space. Experiments are carried out within the scope of this Special Issue to test scalability. The results obtained show that our proposal is scalable in all functions of the benchmark used, as well as numerically very competitive with regards to other compared optimizers.