Algorithm portfolio based scheme for dynamic optimization problems
- https://doi.org/10.1080/18756891.2015.1046327How to use a DOI?
- algorithm portfolio, dynamic optimization problems, learning, algorithm selection problem, combinatorial problems
Since their first appearance in 1997 in the prestigious journal Science, algorithm portfolios have become a popular approach to solve static problems. Nevertheless and despite that success, they have not received much attention in Dynamic Optimization Problems (DOPs). In this work, we aim at showing these methods as a powerful tool to solve combinatorial DOPs. To this end, we propose a new algorithm portfolio for this type of problems that incorporates a learning scheme to select, among the metaheuristics that compose it, the most appropriate solver or solvers for each problem, configuration and search stage. This method was tested over 5 binary-coded problems (dynamic variants of OneMax, Plateau, RoyalRoad, Deceptive and Knapsack) and compared versus two reference algorithms for these problems (Adaptive Hill Climbing Memetic Algorithm and Self Organized Random Immigrants Genetic Algorithm). The results showed the importance of a good design of the learning scheme, the superiority of the algorithm portfolio against the isolated version of the metaheuristics that integrate it, and the competitiveness of its performance versus the reference algorithms.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Jenny Fajardo Calderín AU - Antonio D. Masegosa AU - David A. Pelta PY - 2015 DA - 2015/08/01 TI - Algorithm portfolio based scheme for dynamic optimization problems JO - International Journal of Computational Intelligence Systems SP - 667 EP - 689 VL - 8 IS - 4 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2015.1046327 DO - https://doi.org/10.1080/18756891.2015.1046327 ID - Calderín2015 ER -