CO
Combinatorial Optimization
In the combinatorial optimization research line, we deal with the development of algorithms for the efficient solution of combinatorial optimization problems. While we mainly focus on the development and analysis of metaheuristics algorithms, we also do research in exact and mathehuristic approaches.
Combinatorial optimization problems are ubiquitous in the real-world. Routing, scheduling, location, or cutting/packing, are examples of common problems in this area. Most of these problems are characterized for having a complexity that makes it impossible to solve high-dimensional instances to optimality, and here is where metaheuristics algorithms come to play. Metaheuristic algorithms are a set of techniques and algorithms that, although in most of occasions do not guarantee to find the global optimal solutions, they provide high-quality solutions in affordable computation times. They account for high-level procedures to find, generate, or select simple heuristics, or elaborated methods where heuristics are hybridized with mathematical programming techniques.
In the heuristics optimization research line we develop new algorithms and techniques for the solution of combinatorial optimization problems. Particularly, in the last years we have carried out research in the areas of population-based algorithms and local-search strategies. In population-based algorithms we have concentrated in estimation of distribution algorithms and particularly in their use on the solution of permutation-based combinatorial optimization problems. In the case of local-search algorithms, several of our research results in the analysis of their behavior have contributed to the design of new local search methods.
In addition to the previous work we also pursue theoretical analysis of metaheuristic algorithms and its application on the solution of real-world problems. Specifically, we have lately considered spacecraft trajectory design, routing problems, production planning or logistics for emergency medical services.
Another piece of research that is also carried out in the line is the development of exact algorithms. Particularly we try to make contributions to i) branch-and-bound and branch-and-cut strategies, ii) research in stochastic optimization and risk management and iii) design and implementation of decomposition algorithms based on Branch and Fix Coordination and Lagrangean techniques.
OPLib
OPLib: Test instances for the Orienteering Problem
Authors: Gorka Kobeaga, Maria Merino, Jose A. Lozano
License: free and open source software
RB&C and EA4OP
In this repository, you will find the implementation of two algorithms to solve the Orienteering Problem (OP): RB&C (exact) https://doi.org/10.1016/j.ejor.2023.07.034 and EA4OP (heuristic) https://doi.org/10.1016/j.cor.2017.09.003.
Authors: Gorka Kobeaga, Maria Merino, Jose A. Lozano
License: free and open source software
A307429 sequence
OEIS sequence with the number of permutations of {1..n} at Kendall tau distance k of permutation sigma1 and k+1 Kendall tau distance of permutation sigma2, where sigma1 and sigma2 are at Kendall tau distance 1. Published in https://doi.org/10.1007/s12293-022-00371-y
Authors: Imanol Unanue, Maria Merino, Jose A. Lozano
License: free and open source software
SMIQLib
SMIQLib is a repository of multistage stochastic mixed 0–1 quadratic instances. See
https://doi.org/10.1007/s10479-018-3032-7
Authors: Unai Aldasoro, Maria Merino, Gloria Pérez
License: free and open source software
SMIQLib2
Dataset for Stochastic Mixed Integer Quadratic Optimization
Authors: Unai Aldasoro, Maria Merino, Gloria Pérez
License: free and open source software
OPTECOT - Optimal Evaluation Cost Tracking
This repository contains supplementary material for the paper Speeding-up Evolutionary Algorithms to solve Black-Box Optimization Problems. In this work, we have presented OPTECOT (Optimal Evaluation Cost Tracking): a technique to reduce the cost of solving a computationally expensive black-box optimization problem using population-based algorithms, avoiding loss of solution quality. OPTECOT requires a set of approximate objective functions of different costs and accuracies, obtained by modifying a strategic parameter in the definition of the original function. The proposal allows the selection of the lowest cost approximation with the trade-off between cost and accuracy in real time during the algorithm execution. To solve an optimization problem different from those addressed in the paper, the repository also contains a library to apply OPTECOT with the CMA-ES (Covariance Matrix Adaptation Evolution Strategy) optimization algorithm.
Authors: Judith Echevarrieta, Etor Arza, Aritz Pérez
License: free and open source software
TransfHH
A multi-domain methodology to analyze an optimization problem set
Authors: Etor Arza, Ekhiñe Irurozki, Josu Ceberio, Aritz Perez
License: free and open source software
GESP
Authors: Etor Arza, Leni Le Goff, Emma Hart
License: free and open source software