Unmanned aerial vehicle for waste management system using genetic algorithms

Waste management system is an important economic sector aiding in supporting health and improving the quality of life in smart cities. This paper proposes a multiobjective genetic algorithm (GA) aiming at improving the waste collection process through identifying the best appropriate tours that can minimize the cost. The proposed system aims to satisfy multi-objective approach addressing constraints with a single Unmanned Aerial Vehicle (UAV). It aims at improving performance by reducing flight distance and power consumption while maximizing delivered waste capacity. It incorporates several modifications to the Genetic Algorithm (GA) such as adding distance minimization, energy conservation and capacity maximization mechanisms in the random initial generation, customizing the fitness function and using a hybrid space search in the mutation. The GA is well-designed to find feasible solutions while considering examining three quantities of waste in collection locations produced from synthetic data. Experimental results show that the proposed approach outperforms other previous research approaches addressing the same constraints and with a similar dataset. It is noteworthy that the proposed approach achieved an average reduction in errors in 100 instances compared to previous research by more than 81%. Whereas for 500 instances, the reduction in error reached more than 59%, while in1000 instances, the error was reduced by more than 44%. Those promising results proved that GA utilization can improve the waste collection process through identifying the best appropriate tours that can minimize the cost via distance minimization, energy conservation and capacity maximization mechanisms.