Publikationsdatum:
2017
Beschreibung:
ABSTRACT
There is no meta‐heuristic approach best suited for solving all optimization problems making this field of study highly active. This results in enhancing current approaches and proposing new meta‐heuristic algorithms. Out of all meta‐heuristic algorithms swarm intelligence is preferred as it can preserve information about the search space over the course of iterations and usually has fewer tuning parameters. Grey Wolves (GW) considered as apex predators, motivated us to simulate GW in the optimization of geophysical data sets. The GW Optimizer (GWO) is a swarm‐based meta‐heuristic algorithm, inspired by mimicking the social leadership hierarchy and hunting behavior of GW. The leadership hierarchy is simulated by alpha, beta, delta and omega types of wolf. The three main phases of hunting i.e., searching, encircling, and attacking prey, is implemented to perform the optimization. To evaluate the efficacy of the GWO, we performed inversion on the total gradient of magnetic, gravity and self‐potential (SP) anomalies. The results have been compared with the Particle Swarm Optimization (PSO) technique. Global minimum for all the examples from GWO was obtained with 7 wolves in a pack and 2000 iterations. Inversion was initially performed on thin dykes for noise free and noise corrupted (up to 20% random noise) synthetic data sets. The inversion on a single thin dyke was performed with a different search space. The results demonstrate that compared to PSO, GWO is less sensitive to search space variations. Inversion of noise corrupted data shows that GWO has a better capability in handling noisy data as compared to PSO. Practical applicability of the GWO has been demonstrated by adopting four profiles (i.e., surface magnetic, airborne magnetic, gravity and SP) from the published literature. The GWO results show better data fit than the PSO results and match well with borehole data.
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Print ISSN:
0016-8025
Digitale ISSN:
1365-2478
Thema:
Geologie und Paläontologie
,
Physik
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