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, considered as apex predators, motivated us to simulate Grey Wolves in the optimization of geophysical data sets. The grey wolf optimizer is a swarm‐based meta‐heuristic algorithm, inspired by mimicking the social leadership hierarchy and hunting behaviour of Grey Wolves. The leadership hierarchy is simulated by alpha, beta, delta and omega types of wolves. The three main phases of hunting, that is searching, encircling and attacking prey, is implemented to perform the optimization. To evaluate the efficacy of the grey wolf optimizer, we performed inversion on the total gradient of magnetic, gravity and self‐potential anomalies. The results have been compared with the particle swarm optimization technique. Global minimum for all the examples from grey wolf optimizer was obtained with seven 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 with particle swarm optimization, the grey wolf optimizer is less sensitive to search space variations. Inversion of noise‐corrupted data shows that grey wolf optimizer has a better capability in handling noisy data as compared to particle swarm optimization. Practical applicability of the grey wolf optimizer has been demonstrated by adopting four profiles (i.e. surface magnetic, airborne magnetic, gravity and self‐potential) from the published literature. The grey wolf optimizer results show better data fit than the particle swarm optimizer results and match well with borehole data.
This paper compares the particle swarm optimization (PSO) and very fast simulated annealing (VFSA), which are both non-linear optimization techniques, results for selfpotential (SP) anomalies due to electrochemical nature. PSO is a simple global optimization strategy that simulates the social behavior observed in a flock (swarm) of birds searching for food. VFSA is based on the application of a non-linear optimization algorithm. These two methods have been tested over several theoretical models and field data. The paper uses only one general equation for the inversion of SP anomalies due to vertical cylinder, horizontal cylinder and sphere. The comparison of the results over horizontal cylindrical and spherical targets and three field data from Turkey, India and Germany are presented in this paper. The results show that both PSO and VFSA inversion schemes give similar results. However, tuning a VFSA algorithm was difficult as compared to PSO. Further, PSO contains merit of both local and global search algorithms.
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