Browsing by Keyword "combinatorial optimization"
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Item PIACERE Project: Description and Prototype for Optimizing Infrastructure as Code Deployment Configurations(Association for Computing Machinery, Inc, 2022-07-09) Osaba, Eneko; Diaz-De-Arcaya, Josu; Orue-Echevarria, Leire; Alonso, Juncal; Lobo, Jesus L.; Benguria, Gorka; Etxaniz, Iñaki; Quantum; HPA; Tecnalia Research & Innovation; IAPIACERE is an European project supported by the Union's Horizon 2020 research and innovation programme, whose objective is to enhance the productivity of DevOps teams in the operation of Infrastructure as Code (IaC) by offering an integrated DevSec-Ops framework. Thus, DevOps practitioners can develop IaC as if they were programming a common software application. In order to achieve this challenging task, one of the core technologies considered within PIACERE will be the design and development of optimization metaheuristics, in a module coined as IaC Optimizer Platform (IOP). The main objective of the IOP is to provide DevSecOps teams with the most appropriate deployment configurations that best fit a set of defined constraints. The goal of this technical paper is to describe the preliminary approach followed in PIACERE for carrying out this optimization, and how the IOP fits into the whole PIACERE ecosystem. Additionally, results obtained in a preliminary experimentation are detailed in this study.Item Traveling salesman problem: a perspective review of recent research and new results with bio-inspired metaheuristics(Elsevier, 2020-01-01) Osaba, Eneko; Yang, Xin She; Del Ser, Javier; Quantum; IAThe traveling salesman problem (TSP) is one of the most studied problems in computational intelligence and operations research. Since its first formulation, a myriad of works has been published proposing different alternatives for its solution. Additionally, a plethora of advanced formulations have also been proposed by the related practitioners, trying to enhance the applicability of the basic TSP. This chapter is firstly devoted to providing an informed overview on the TSP. For this reason, we first review the recent history of this research area, placing emphasis on milestone studies contributed in recent years. Next, we aim at making a step forward in the field proposing an experimentation hybridizing three different reputed bio-inspired computational metaheuristics (namely, particle swarm optimization, the firefly algorithm, and the bat algorithm) and the novelty search mechanism. For assessing the quality of the implemented methods, 15 different datasets taken from the well-known TSPLIB have been used. We end this chapter by sharing our envisioned status of the field, for which we identify opportunities and challenges which should stimulate research efforts in years to come.