Volume 8, Issue 6, December 2015, Pages 1053 - 1075
An adaptive local search with prioritized tracking for Dynamic Environments
A.D. Masegosa, E. Onieva, P. Lopez-Garcia, E. Osaba, A. Perallos
Received 20 May 2015, Accepted 30 August 2015, Available Online 1 December 2015.
- https://doi.org/10.1080/18756891.2015.1113736How to use a DOI?
- Dynamic Environments, Dynamic Optimization Problems, Trajectory-based Methods, Prioritized Tracking, Local Search, Adaptive Metaheuristics
- Dynamic Optimization Problems (DOPs) have attracted a growing interest in recent years. This interest is mainly due to two reasons: their closeness to practical real conditions and their high complexity. The majority of the approaches proposed so far to solve DOPs are population-based methods, because it is usually believed that their higher diversity allows a better detection and tracking of changes. However, recent studies have shown that trajectory-based methods can also provide competitive results. This work is focused on this last type of algorithms. Concretely, it proposes a new adaptive local search for continuous DOPs that incorporates a memory archive. The main novelties of the proposal are two-fold: the prioritized tracking, a method to determine which solutions in the memory archive should be tracked first; and an adaptive mechanism to control the minimum step-length or precision of the search. The experimentation done over the Moving Peaks Problem (MPB) shows the benefits of the prioritized tracking and the adaptive precision mechanism. Furthermore, our proposal obtains competitive results with respect to state-of-the-art algorithms for the MPB, both in terms of performance and tracking ability.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - A.D. Masegosa AU - E. Onieva AU - P. Lopez-Garcia AU - E. Osaba AU - A. Perallos PY - 2015 DA - 2015/12 TI - An adaptive local search with prioritized tracking for Dynamic Environments JO - International Journal of Computational Intelligence Systems SP - 1053 EP - 1075 VL - 8 IS - 6 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2015.1113736 DO - https://doi.org/10.1080/18756891.2015.1113736 ID - Masegosa2015 ER -