Multi-Objective Puma Optimizer (MOPO) for Solving Leds Placement in Indoor VLC Systems

Optimizing the placement of LEDs in Visible Light Communication (VLC) systems presents a multi-objective challenge, requiring a balance between two objective functions, Coverage and Throughput. To address this challenge, we propose a Multi-Objective version of the Puma optimizer algorithm (MOPO), inspired by NSGA-II, which integrates non-dominated sorting, and crowding distance to ensure a diverse set of optimized solutions. The proposed MOPO is evaluated through multiple iterations, considering key performance metrics such as throughput and user coverage. Simulation results validate the efficiency and accuracy of MOPO, demonstrating its superiority in identifying an optimal Pareto front, against existing algorithms, including Non-dominated sorting genetic algorithm (NSGA-II) and Multi-Objective Whale Optimization Algorithm (MOWOA).