Solving LEDs Placement Problem in Indoor VLC System Using an Efficient Coronavirus Herd Immunity Optimizer

Visible Light Communication (VLC) is a new emerging technology. It uses Light Emitting Diodes (LEDs) as transmitters and Photo-Detectors (PDs) as receivers to provide both communication and illumination for users. In indoor VLC systems, the deployment of numerous LEDs to serve numerous users is a complex problem. Because this is an NP-Hard problem, approximation methods, particularly meta-heuristics, are appropriate for solving it. In this paper, we introduce a novel algorithm called ECHIO (Enhanced Chaos-based Herd Immunity Optimizer) that integrates elements from the Improved Herd Immunity Optimizer (CHIO), incorporating the concept of chaotic maps and the Opposition-Based Learning (OBL) mechanism. The chaotic map is adopted to increase the chaotic stochastic behavior of the CHIO. Moreover, the Opposition-Based Learning (OBL) mechanism is applied to enhance the convergence speed of CHIO and explore the search space effectively. Finally, The effectiveness of the proposed ECHIO is tested on several scenarios under different settings, taking into account the throughput and the user coverage metrics. Simulation results demonstrate the accuracy and superiority of our proposed ECHIO algorithm in finding optimal LEDs positions when compared with the standard CHIO, Firefly algorithm (FA), Particle Swarm Optimization (PSO), Marine Predators algorithm (MPA), Manta Ray Foraging Optimization (MRFO), Whale Optimization Algorithm (WOA), Bat algorithm (BAT), Grey Wolf Optimizer (GWO), and Genetic algorithm (GA).