Ant lion optimization based inertia weight optimized KGMO for mobility Management in Heterogeneous LTE cellular networks

This study proposes a hybrid optimization-based mobility management strategy employing Kinetic Gas Molecular Optimization (KGMO) and Ant Lion Optimization (ALO). Initially, KGMO calculates particle properties, such as gas molecule position and velocity, based on kinetic energy principles. While KGMO demonstrates expedited convergence in large-dimensional spaces, its limitations prompt the hybridization with ALO. ALO modifies the inertia weight of KGMO to mitigate these issues. The proposed KGMO-ALO approach is implemented and validated in a MATLAB environment, compared against other meta-heuristic techniques like Seagull and Monarch Butterfly Optimizations across various performance metrics including throughput, bit error rate, end-to-end delay, and HO. Existing techniques such as PSO, MBO, and SOA exhibit superior performance primarily in scenarios with high user counts. The underperformance of ALO is attributed to its reliance on the best antlion solution in each optimization round. By leveraging the hybrid KGMO-ALO approach, this study aims to overcome these limitations and advance mobility management strategies in heterogeneous LTE cellular networks.