Publication

Metaheuristics and Local Search

Dominique de Werra
2023

Abstract

Combinatorial optimization, which includes sports scheduling problems as one of its areas of application, consists in finding optimal solutions to problems defined over a discrete set of feasible solutions. This chapter overviews approximate optimization methods, from greedy and semi-greedy construction algorithms to local search and from local search to metaheuristics. Some fundamental principles of metaheuristics are reviewed, and their templates are provided. Several approaches to sports scheduling problems make use of local search in the development of heuristics. We show how neighborhoods and other principles of local search can be adapted to the specific case of round-robin tournament scheduling problems. We conclude with the presentation of a collection of applications of metaheuristics to problems in sports scheduling.

Official source

https://infoscience.epfl.ch/entities/publication/5def8522-0e94-4da4-b9ea-fac4049a9164

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Dominique de Werra
2023

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/5def8522-0e94-4da4-b9ea-fac4049a9164

About this result

Related concepts (27)

Optimization problem

In mathematics, computer science and economics, an optimization problem is the problem of finding the best solution from all feasible solutions. Optimization problems can be divided into two categories, depending on whether the variables are continuous or discrete: An optimization problem with discrete variables is known as a discrete optimization, in which an object such as an integer, permutation or graph must be found from a countable set.

Feasible region

In mathematical optimization, a feasible region, feasible set, search space, or solution space is the set of all possible points (sets of values of the choice variables) of an optimization problem that satisfy the problem's constraints, potentially including inequalities, equalities, and integer constraints. This is the initial set of candidate solutions to the problem, before the set of candidates has been narrowed down.

Mathematical optimization

Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

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