Gender Based Sea Lion Optimization Algorithm for Maximum Power Point Tracking

Abstract

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.