Gender Based Sea Lion Optimization Algorithm for Maximum Power Point Tracking

Weng-Hooi Tan — 2025-10-29

This research paper proposes an improved variant of SLnO, named the Gender Based Sea Lion Optimization (GBSLnO) algorithm. GBSLnO separates the population into two gender groups (male and female), where the search agents of different genders possess distinctive operational characteristics during the mathematical execution. Male agents are less considerate in localization, but bolder in action, consistently focused on efficiency, and capable of multitasking (variable dimension). In contrast, female agents are more considerate in positioning and actions, but work in a single task without concerning on efficiency (invariant dimensionality). GBSLnO retains the searching behavior, encircling behavior, and circle-updating behavior in the original SLnO, but its functionality has been improved with enhanced coefficient adaptation. Overall, this algorithm mainly emphasizes the interactions between two gender groups which operate in same behavioural patterns but distinctive mathematical mechanism. The proposed algorithm was simulated on a total 20 maximum power point tracking (MPPT) challenges in photovoltaic application systems to compare with standard SLnO and several existing SLnO variants. Upon evaluation, GBSLnO outperformed other comparative algorithms in terms of reliability and robustness for all test cases. Meanwhile, it achieves the highest efficiency in the MPPT process of the photovoltaic system. In addition, its output power spectrum also shows that GBSLnO has the best convergence rate and the least oscillation. All these statements prove that GBSLnO is a successfully improved variant of SLnO, offering a more superior optimization process.

Transferability of an Artificial Neural Network Model for Landslide Susceptibility Prediction: Extending from Western Sarawak to Tuaran and Penampang, Sabah

Nur Hisyam Ramli — 2025-10-29

Landslides have been determined to be one of the most damaging forms of natural disasters as well as common geological hazards around the world that causes significant damage to the ecosystems, and settlements. Landslide susceptibility assessment is a crucial step in understanding the risk of landslides for an area to conduct a successful mitigation effort. Cotemporary research has deemed a machine learning based approach to be highly accurate in determining the susceptibility of an area towards landslide, and a suitable prediction function for the landslide susceptibility map development. Given the dependency of machine learning models on the availability of data used to develop its predictive capabilities, the deployment of the method may not be suitable in regions that are lacking or do not have the required data, especially historical landslide points. In this study, the Artificial Neural Network model was developed with data from Western Sarawak, located 1158 km away from the target region which were Tuaran and Penampang, Sabah, that was lacking in the amount of available historical landslide data. To enable the transfer, the type of variables in both regions were kept uniform, which were aspect, curvature, elevation, land use and land cover, rainfall intensity, slope angle, and topographic wetness index. In its own region, the model was determined to be highly accurate for predicting landslide susceptibility with a training success rate, and prediction success rate of 100%. Transferring the model to the target region reveals the process was a success, with a prediction success rate of 98%, and a precision of 100%.

Journal of Advanced Research Design

Gender Based Sea Lion Optimization Algorithm for Maximum Power Point Tracking

Transferability of an Artificial Neural Network Model for Landslide Susceptibility Prediction: Extending from Western Sarawak to Tuaran and Penampang, Sabah