A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selection of appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer ReviewedPostprint (published version

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

Why simheuristics? : Benefits, limitations, and best practices when combining metaheuristics with simulation

Many decision-making processes in our society involve NP-hard optimization problems. The largescale, dynamism, and uncertainty of these problems constrain the potential use of stand-alone optimization methods. The same applies for isolated simulation models, which do not have the potential to find optimal solutions in a combinatorial environment. This paper discusses the utilization of modelling and solving approaches based on the integration of simulation with metaheuristics. These 'simheuristic' algorithms, which constitute a natural extension of both metaheuristics and simulation techniques, should be used as a 'first-resort' method when addressing large-scale and NP-hard optimization problems under uncertainty -which is a frequent case in real-life applications. We outline the benefits and limitations of simheuristic algorithms, provide numerical experiments that validate our arguments, review some recent publications, and outline the best practices to consider during their design and implementation stages

Internet of Things in urban waste collection

Nowadays, the waste collection management has an important role in urban areas. This paper faces this issue and proposes the application of a metaheuristic for the optimization of a weekly schedule and routing of the waste collection activities in an urban area. Differently to several contributions in literature, fixed periodic routes are not imposed. The results significantly improve the performance of the company involved, both in terms of resources used and costs saving

Towards ‘Metaheuristics in the Large’

There is a pressing need for a higher-level architectural per- spective in metaheuristics research. This article proposes a purely functional collection of component signatures as a basis for the scalable and automatic construction of meta- heuristics. We claim that this is an important step for sci- entific progress because: i). It is increasingly accepted that newly-proposed meta- heuristics should be grounded in terms of well-defined frameworks and components. Standardized descrip- tions help to distinguish novelty from minor variation. ii). Greater reproducibility is needed, particularly to facil- itate comparison with the state-of-the-art. iii). Interoperable descriptions are a pre-requisite for a data model supporting large-scale knowledge discovery across frameworks and problems. A key obstacle is that metaheuristic components suffer from an intrinsic lack of modularity, so we present some design op- tions for dealing with this and use this to provide a roadmap for addressing the above issues.PreprintPeer reviewe