Self-Adaptive Bare-Bones Teaching-Learning-Based Optimization for Inversion of Multiple Self-Potential Anomaly Sources

Teaching-learning-based optimization (TLBO) is a meta-heuristic algorithm that simulates the process of teacher and student (or learner) interaction in transmitting knowledge. The algorithm is relatively simple to implement, with free-tuning parameters for balancing exploration and exploitation of the solution space. TLBO contains two phases, namely, teaching and learning. In this paper, self-adaptive Gaussian bare-bones TLBO (SABBTLBO) is proposed for improving TLBO and Gaussian bare-bones TLBO (BBTLBO) performance. In the SABBTLBO, Gaussian bare-bones and the original teaching phase in TLBO become more adaptive by a mechanism based on the learner's rank. For the new learning phase, an adaptive scaling factor based on the rank mechanism is used to modify the neighborhood search strategy. A restarted mutation approach is also added in the learning phase. The developed SABBTLBO is compared with six state-of-the-art TLBO variant algorithms for inversion of synthetic multiple self-potential (SP) anomaly sources. The proposed SABBTLBO algorithm is also tested and compared with several algorithms applied for field SP data from different locations in the world including India, Portugal, and Indonesia, using the assumption that SP data are sourced by idealized bodies (simple geometric model or thin sheet model). The inversion of multiple SP anomaly sources using SABBTLBO is used not only for determining the best model parameters, but also their uncertainties. The latter is estimated from the equivalence region of the set of possible solutions via cost function topography evaluation. Significant results were obtained and can be associated with the geology of studied area.