E-Waste Management Using Artificial Intelligence Based Optimization Algorithms

Because of rapid development of technology and escalating consumer demand for electronic devices, e-waste (Electronic Waste) has turned out to be a severe environmental concern. An efficient management of e-waste is crucial for minimizing environmental hazards and optimizing resource recovery. The present research is applying GA (Genetic Algorithm) and ACO (Ant Colony Optimization) methods, in MATLAB, for optimizing the e-waste collection points allocation to the recycling centers. The factors like e-waste amounts, transportation cost, and center capacities have been considered for determining an optimal distribution strategy. The performance of GA and ACO in minimizing the overall cost has been evaluated. The results revealed that both techniques are optimizing the allocation process effectively. However, ACO exhibits faster convergence, and GA provides more diverse solutions. This research contributes to development of sustainable and efficient e-waste management approaches by integrating AI-driven optimization techniques.