Using the approximate algorithms, we are faced with the problem of determining the appropriate values of their input parameters, which is always a complex task and is considered an optimization problem. In this context, incorporating online control parameters is a very interesting issue. The aim is to vary the parameters during the run so that the studied algorithm can provide the best convergence rate and, thus, achieve the best performance. In this paper, we compare the performance of a self-adaptive approach for the biogeography-based optimization algorithm using the mutation rate parameter with respect to its original version and other heuristics. This work proposes altering some parameters of the metaheuristic according to its exhibited efficiency. To test this approach, we solve the set covering problem, which is a classical optimization benchmark with many industrial applications such as line balancing production, crew scheduling, service installation, databases, among several others. We illustrate encouraging experimental results, where the proposed approach is capable of reaching various global optimums for a well-known instance set taken from the Beasleys OR-Library, and sometimes, it improves the results obtained by the original version of the algorithm.
Accepté le :
DOI : 10.1051/ro/2019039
Mots-clés : Metaheuristics, biogeography-based optimization algorithm, set covering problem
@article{RO_2019__53_3_1033_0, author = {Crawford, Broderick and Soto, Ricardo and Olivares, Rodrigo and Riquelme, Luis and Astorga, Gino and Johnson, Franklin and Cort\'es, Enrique and Castro, Carlos and Paredes, Fernando}, title = {A self-adaptive biogeography-based algorithm to solve the set covering problem}, journal = {RAIRO - Operations Research - Recherche Op\'erationnelle}, pages = {1033--1059}, publisher = {EDP-Sciences}, volume = {53}, number = {3}, year = {2019}, doi = {10.1051/ro/2019039}, zbl = {07127062}, language = {en}, url = {http://archive.numdam.org/articles/10.1051/ro/2019039/} }
TY - JOUR AU - Crawford, Broderick AU - Soto, Ricardo AU - Olivares, Rodrigo AU - Riquelme, Luis AU - Astorga, Gino AU - Johnson, Franklin AU - Cortés, Enrique AU - Castro, Carlos AU - Paredes, Fernando TI - A self-adaptive biogeography-based algorithm to solve the set covering problem JO - RAIRO - Operations Research - Recherche Opérationnelle PY - 2019 SP - 1033 EP - 1059 VL - 53 IS - 3 PB - EDP-Sciences UR - http://archive.numdam.org/articles/10.1051/ro/2019039/ DO - 10.1051/ro/2019039 LA - en ID - RO_2019__53_3_1033_0 ER -
%0 Journal Article %A Crawford, Broderick %A Soto, Ricardo %A Olivares, Rodrigo %A Riquelme, Luis %A Astorga, Gino %A Johnson, Franklin %A Cortés, Enrique %A Castro, Carlos %A Paredes, Fernando %T A self-adaptive biogeography-based algorithm to solve the set covering problem %J RAIRO - Operations Research - Recherche Opérationnelle %D 2019 %P 1033-1059 %V 53 %N 3 %I EDP-Sciences %U http://archive.numdam.org/articles/10.1051/ro/2019039/ %R 10.1051/ro/2019039 %G en %F RO_2019__53_3_1033_0
Crawford, Broderick; Soto, Ricardo; Olivares, Rodrigo; Riquelme, Luis; Astorga, Gino; Johnson, Franklin; Cortés, Enrique; Castro, Carlos; Paredes, Fernando. A self-adaptive biogeography-based algorithm to solve the set covering problem. RAIRO - Operations Research - Recherche Opérationnelle, Tome 53 (2019) no. 3, pp. 1033-1059. doi : 10.1051/ro/2019039. http://archive.numdam.org/articles/10.1051/ro/2019039/
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