Galapagos giant tortoise mating algorithm: revolutionizing wireless charging trajectories and secure data transmission in sustainable power plants

This research makes several important contributions. • We introduce a unique bio-inspired framework based on tortoise mating behaviors, expanding the range of bio-inspired metaheuristics. • The GGTMOA is specifically designed to address complex, real-world problems in power plants, optimizing WMC trajectories while considering energy constraints, spatial distribution, and security concerns. • Our proposed approach achieves a balanced exploration and exploitation strategy that enhances both charging efficiency and data security in dynamic environments. • Through empirical evaluations and comparative analyses, we demonstrate that GGTMOA outperforms other metaheuristics in terms of solution quality, energy efficiency, and security. This paper presents the Galapagos Giant Tortoise Mating Optimization Algorithm (GGTMOA), a novel nature-inspired metaheuristic developed to optimize the trajectory paths of Wireless Mobile Chargers (WMCs) and ensure secure data transmission in power plants. The algorithm addresses critical challenges such as energy-efficient charging, the spatial distribution of wireless sensor nodes, limited operational energy resources, dynamic trajectory planning, and data encryption for secure communication. Inspired by the unique mating behaviors of galapagos giant tortoises, GGTMOA achieves a robust balance between exploration and exploitation through innovative initialization techniques, movement strategies, mating mechanisms, and selection processes. In this study, the proposed algorithm is first employed to optimize the trajectory paths of WMCs, addressing key challenges in energy-efficient charging and dynamic path planning. Following this, the algorithm integrates advanced encryption methods to ensure the secure transmission of data between sensor nodes and base stations, safeguarding sensitive information and enhancing the overall security of the system. This two-fold approach not only optimizes charging efficiency and reduces energy consumption but also fortifies data communication, making the system more robust and reliable in industrial environments. Simulation results demonstrate that GGTMOA outperforms existing metaheuristics by generating optimal trajectories that enhance charging efficiency, reduce energy consumption, ensure secure data communication, and satisfy plant-specific energy constraints. These findings establish GGTMOA as a powerful tool for sustainable energy management, wireless charging optimization, and secure data handling in industrial environments.