E
Enrique Alba
Researcher at University of Málaga
Publications - 540
Citations - 16018
Enrique Alba is an academic researcher from University of Málaga. The author has contributed to research in topics: Metaheuristic & Evolutionary algorithm. The author has an hindex of 57, co-authored 530 publications receiving 14535 citations. Previous affiliations of Enrique Alba include ETSI & University of Waterloo.
Papers
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Book ChapterDOI
Automatic parameter tuning with metaheuristics of the AODV routing protocol for vehicular ad-hoc networks
José García-Nieto,Enrique Alba +1 more
TL;DR: This work addresses the optimal automatic parameter tuning of a well-known routing protocol: Ad Hoc On Demand Distance Vector (AODV), and finds that PSO outperforms all the compared algorithms in efficiency and accuracy.
Proceedings ArticleDOI
Multi-objective Optimal Test Suite Computation for Software Product Line Pairwise Testing
TL;DR: This exploratory paper proposes a zero-one mathematical linear program for solving the multi-objective problem and presents an algorithm to compute the true Pareto front, hence an optimal solution, from the feature model of a SPL.
Journal ArticleDOI
Automatic tuning of communication protocols for vehicular ad hoc networks using metaheuristics
TL;DR: Particle Swarm Optimization outperforms all the compared algorithms for both studied VANET instances and faces the FTC with five representative state-of-the-art optimization techniques and compares their performance.
Journal ArticleDOI
Reducing vehicle emissions and fuel consumption in the city by using particle swarm optimization
TL;DR: A Swarm Intelligence approach is proposed for the optimal scheduling of traffic lights timing programs in metropolitan areas so that the traffic flow of vehicles can be improved with the final goal global target of reducing their fuel consumption and gas emissions.
Journal ArticleDOI
Improving Diversity in Evolutionary Algorithms: New Best Solutions for Frequency Assignment
TL;DR: This paper applies and extends some of the most recent advances in evolutionary algorithms to two common variants of the FAP, and shows how, in traditional techniques, two common issues affect their performance: premature convergence and the way in which neutral networks are handled.