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Optimization Algorithms
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Brief Description |
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| Metaheuristics, Evolutionary Bioinspired Algorithms, Parallel Algorithms, Hybridization, Dynamic, Self-Adaptive, Multiobjective, Swarm Intelligence |
Our goal is to design and develop new optimization algorithms. For this, we will resort to the dynamic, self-adaptive, parallel-distribution, multi-objective and hybrid creation of solvers to level up the performance of standard metaheuristics in order to meet the requirements of (first) academic-benchmarking problems, and then to transfer the new generated knowledge to face real world tasks in the industry. We are focused on multidisciplinary advances from a quantified and verifiable point of view and applications of interest for the Society and for Computer Science |
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Theoretical Fundations of Algorithms and Problems
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Brief Description |
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| Elementary Landscapes, Evolvability |
We plan to develop theoretical results to support the convergence, the computational complexity, and the landscape exploration of search and search/optimization techniques. This is a hard field in the research with metaheuristics, but an important one for the sake of abstract interests like creating a serious body of knowledge as well as for practical reasons, like constructing better operators, analyzing the difficulty of benchmarks, and designing self-guiding algorithms that minimize parameter tuning and adapt to the dynamics of the problem at hands. |
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Real World Optimization Problems
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Brief Description |
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| Smart Mobility and Intelligent Transport Systems, Software Engineering, Bioinformatics, Communication Networks |
The major challenge for optimization algorithms, metaheuristics in particular, comes from its actual application in real life. Many real problems have emerged in multidisciplinary areas like: Smart Mobility, Intelligent Transport Systems, Software Engineering, Bioinformatics and Communication Networks, for which current optimization algorithms have not been designed to deal with. Our mission is to create new algorithms to face such problems in these, as well as other transversal fields of the industry. |
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Academic Benchmarking Problems
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Brief Description |
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| Continuous Benchmarking, Competitions, Discrete Benchmarking |
Facing academic problems is an important task when designing new optimization techniques. In this way, it is possible to work with a-priori base-knowledgement for training and assessing such new algorithms in design time. This academic evaluation also comprises an actual challenge when trying to reach or bate the current state of the art. There exists continuous and discrete benchmarking sets of problems, as well as experimental procedures in competitions (CEC’05, BBOB, CEC’13, etc.), that lead us to follow the standard methodology with the aim of positioning our proposals in the top of best performing algorithms.
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Other Research Lines
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Brief Description |
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| Our Applications, Simulations, Android, Drons |
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