A5039681328- Metaheuristic Optimization Algorithms Research
- Vehicle Routing Optimization Methods
- Advanced Multi-Objective Optimization Algorithms
- Evolutionary Algorithms and Applications
- Quantum Computing Algorithms and Architecture
- Optimization and Packing Problems
- Transportation and Mobility Innovations
- Advanced Manufacturing and Logistics Optimization
- Transportation Planning and Optimization
- Traffic Prediction and Management Techniques
- Data Stream Mining Techniques
- Quantum Information and Cryptography
- Cloud Computing and Resource Management
- Optimization and Search Problems
- Machine Learning and Data Classification
- Neural Networks and Applications
- Urban and Freight Transport Logistics
- Neural Networks and Reservoir Computing
- Data Mining Algorithms and Applications
- Software System Performance and Reliability
- Distributed Control Multi-Agent Systems
- Scheduling and Optimization Algorithms
- Reinforcement Learning in Robotics
- Complex Network Analysis Techniques
- Data Management and Algorithms
Association of Electronic and Information Technologies
2020-2025
Digital Research Alliance of Canada
2025
Tecnalia
2017-2024
Euskadiko Parke Teknologikoa
2018-2024
Boeing (Spain)
2022
Universidad de Deusto
2011-2020
University of the Basque Country
2020
Middlesex University
2015
Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing successful proposal of new algorithm not easy task. Given the maturity this field, proposing technique innovative elements no longer enough. Apart from novelty, results reported by authors should be proven to achieve significant advance over previous outcomes state art....
Abstract The evolution of Cloud Computing into a service utility, along with the pervasive adoption IoT paradigm, has promoted significant growth in need computational and storage services. traditional use cloud services, focused on consumption one provider, is not valid anymore due to different shortcomings being risk vendor lock-in critical. We are assisting change from usage single provider combination multiple types, affecting way which applications designed, developed, deployed operated...
This paper presents a method of optimizing the elements hierarchy fuzzy-rule-based systems (FRBSs). It is hybridization genetic algorithm (GA) and cross-entropy (CE) method, which here called GACE. used to predict congestion in 9-km-long stretch I5 freeway California, with time horizons 5, 15, 30 min. A comparative study different levels GACE made. These range from pure GA CE, passing through weights for each combined techniques. The results prove that more accurate than or CE alone...
Quantum Computing is drawing a significant attention from the current scientific community. The potential advantages offered by this revolutionary paradigm has led to an upsurge of production in different fields such as economics, industry, or logistics. main purpose paper collect, organize and systematically examine literature published so far on application routing problems. To do this, we embrace well-established procedure named Systematic Literature Review. Specifically, provide unified,...
This paper capitalizes on the increasingly high relevance gained by data-intensive technologies in development of intelligent transportation system, which calls for progressive adoption adaptive, self-learning methods solving modeling, simulation, and optimization problems. In this regard, certain mechanisms processes observed nature, including animal brain, have proved themselves to excel not only terms efficiently capturing time-evolving stimuli, but also at undertaking complex tasks...
Evolutionary computation has largely exhibited its potential to complement conventional learning algorithms in a variety of machine tasks, especially those related unsupervised (clustering) and supervised learning. It not been until lately when the computational efficiency evolutionary solvers put prospective for training reinforcement models. However, most studies framed so far within this context have considered environments tasks conceived isolation, without any exchange knowledge among...
Efficient packing of items into bins is a common daily task. Known as Bin Packing Problem, it has been intensively studied in the field artificial intelligence, thanks to wide interest from industry and logistics. Since decades, many variants have proposed, with three-dimensional Problem closest one real-world use cases. We introduce hybrid quantum-classical framework for solving Problems (Q4RealBPP), considering different realistic characteristics, such as: i) package bin dimensions, ii)...
Satellite mission planning for Earth observation satellites is a combinatorial optimization problem that consists of selecting the optimal subset imaging requests, subject to constraints, be fulfilled during an orbit pass satellite. The ever-growing amount in underscores need operate them efficiently, which requires solving many instances short periods time. However, current classical algorithms often fail find global optimum or take too long execute. Here, we approach from quantum computing...
Quantum computing (QC) is expected to solve incredibly difficult problems, including finding optimal solutions combinatorial optimization problems. However, date, QC alone still far demonstrate this capability except on small-sized Hybrid approaches where and classical work together have shown the most potential for solving real-world scale This aims show that we can enhance a algorithm with so it overcome limitation. We present new hybrid quantum-classical tabu search (HQTS) capacitated...