Open AccessMetaheuristic algorithms for building Covering Arrays: A review
Author(s) -
Jimena Adriana Timaná-Peña,
Carlos Cobos,
José Torres-Jiménez
Publication year - 2016
Publication title -
revista facultad de ingeniería/revista facultad de ingeniería
Language(s) - English
Resource type - Journals
eISSN - 2357-5328
pISSN - 0121-1129
DOI - 10.19053/01211129.v25.n43.2016.5295
Subject(s) - metaheuristic , harmony search , simulated annealing , parallel metaheuristic , tabu search , computer science , algorithm , particle swarm optimization , mathematical optimization , ant colony optimization algorithms , fitness function , genetic algorithm , mathematics , artificial intelligence , machine learning , meta optimization
Covering Arrays (CA) are mathematical objects used in the functional testing of software components. They enable the testing of all interactions of a given size of input parameters in a procedure, function, or logical unit in general, using the minimum number of test cases. Building CA is a complex task (NP-complete problem) that involves lengthy execution times and high computational loads. The most effective methods for building CAs are algebraic, Greedy, and metaheuristic-based. The latter have reported the best results to date. This paper presents a description of the major contributions made by a selection of different metaheuristics, including simulated annealing, tabu search, genetic algorithms, ant colony algorithms, particle swarm algorithms, and harmony search algorithms. It is worth noting that simulated annealing-based algorithms have evolved as the most competitive, and currently form the state of the art.