DEUSTO research: A Hybrid Method for Short-term Traffic Congestion Forecasting Using Genetic Algorithms and Cross-entropy

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Abstract - This paper presents a method of optimizing the elements of a hierarchy of Fuzzy Rule-Based Systems (FRBSs).

DEUSTO research: A Meta-heuristic based in the Hybridization of Genetic Algorithms and Cross Entropy methods for continuous optimization (GACE)


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Abstract - Metaheuristics have proven to get a good performance solving difficult optimization problems in practice. Despite its success, metaheuristics still suffers from several problems that remains open as the variability of their performance depending on the problem or instance being solved. One of the approaches to deal with these problems is the hybridization of techniques.

DEUSTO research: An Improved Discrete Bat Algorithm for Symmetric and Asymmetric Traveling Salesman Problems


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Abstract - Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric traveling salesman problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm.

DEUSTO research: An Evolutionary Discrete Firefly Algorithm with Novel Operators for Solving the Vehicle Routing Problem with Time Windows

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Abstract - An evolutionary discrete version of the Firefly Algorithm (EDFA) is presented in this paper for solving the well-known Vehicle Routing Problem with Time Windows (VRPTW). The contribution of this work is not only the adaptation of the EDFA to the VRPTW. Additionaly, some novel route optimization operators are presented in this study. These operators incorporate the process of minimizing the number of routes for a solution in the search process itself. To do this, node selective extractions and subsequent reinsertion are performed. The new operators analyze all routes of the current solution increasing the diversification capacity of the search process (against traditional node and arc exchange based operators).

DEUSTO research: Comparison between Golden Ball Meta-heuristic, Evolutionary Simulated Annealing and Tabu Search for the Traveling Salesman Problem


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Abstract - The Golden Ball is a multi-population meta-heuristic based on soccer concepts. It was first designed to solve combinatorial optimization problems. Until now, it has been tested with different kind of problems, but its efficiency has only been compared with some classical algorithms, such as different kind of Genetic Algorithms and Distributed Genetic Algorithms.

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“The TIMON project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 636220”

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