An adaptive local search with prioritized tracking for Dynamic Environments
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2015-12-01
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Springer Science and Business Media B.V.
Resumen
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.
Palabras clave
Adaptive metaheuristics
Dynamic Environments
Dynamic Optimization Problems
Local search
Prioritized tracking
Trajectory-based methods
Dynamic Environments
Dynamic Optimization Problems
Local search
Prioritized tracking
Trajectory-based methods
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Cita
Masegosa, Onieva, Lopez-Garcia, Osaba, & Perallos. (2015). An adaptive local search with prioritized tracking for Dynamic Environments. International Journal of Computational Intelligence Systems, 8(6), 1053-1075. https://doi.org/10.1080/18756891.2015.1113736