Global Optimization of Self-Potential Anomalies Using Hunger Games Search Algorithm

In this paper, the effectiveness of recently proposed Hunger Games Search (HGS) optimization tool, which simulates behavioural choices of the predators in the animal kingdom, in estimating model parameters of idealized causative sources from Self-Potential (SP) anomalies is presented. Before the parameter estimations, the modal analyses of the model parameter pairs and parameter tuning processes were performed to characterize the optimization problem and to decide the optimal value of the control parameter of HGS algorithm, respectively. Subsequently, the proposed optimizer was applied on some synthetic SP data sets and four anomalies obtained from India, Turkiye, Canada, and Indonesia. Multiple and intercalating causative sources were considered in the synthetic and real data cases, and HGS algorithm produced satisfactory solutions. In the composite anomaly cases a second moving average procedure successfully removed the undesired background effects. Uncertainty appraisal analyses showed the validity of the model parameter estimations. The outputs of HGS optimizer were also compared with those of the well-established and popular standard particle swarm optimization regarding convergence rate, robustness, stability, and accuracy. Applications presented here clearly showed that SP anomalies can be inverted more effectively via HGS since the procedure has the ability to search the predefined model space extensively without being trapped into numerous local minima and approximate the global minimum more precisely. Thus, it is recommended to use the HGS algorithm in model parameter estimation studies using SP anomalies.