An Environmentally Adaptive CRO-SL Algorithm Based on Dynamic Agents for the Channel Assignment Problem in Wireless Networks

In recent decades, metaheuristic algorithms have emerged as indispensable tools for addressing complex optimization challenges, particularly in several engineering fields, where NP-hard problems are prevalent. A common NP-hard problem in communication engineering is the Channel Assignment Problem (CAP) for wireless access points (APs), with a determined number of stations (STAs) connected to them. The performance of the complete network depends on the interference and noise among the different clusters of devices and the obstacles or elements placed in the physical transmission space. To address the CAP, a new environmentally adaptive approach is proposed for the Coral Reefs Optimization with Substrate Layers (CRO-SL) algorithm, introducing new environmental agents: algae (representing tabu positions) and ocean water acidification (lowering fitness thresholds). The Environmentally Adaptive CRO-SL (EnvAdapt-CRO-SL) implementation aims to improve solution exploration, enhancing computational efficacy in generating new candidate solutions within the coral reef population. An exhaustive comparative analysis of four configurations of the proposed EnvAdapt-CRO-SL variant assesses the impact of each environmental agent on the algorithm’s performance. Additionally, external benchmarks against four different metaheuristics, along with an analysis of the influence of pseudorandom number generators on initialization and search operators, and a robust optimization case study, provide deeper insights. The results show that incorporating the new environmental agents into the EnvAdapt-CRO-SL workflow significantly boosts throughput while reducing the computational time required to obtain optimal solutions.