Multi-Objective Routing Optimization in Electric and Flying Vehicles: A Genetic Algorithm Perspective

Abstract

The advent of electric and flying vehicles (EnFVs) has brought significant advancements to the transportation industry, offering improved sustainability, reduced congestion, and enhanced mobility. However, the efficient routing of messages in EnFVs presents unique challenges that demand specialized algorithms to address their specific constraints and objectives. This study analyzes several case studies that investigate the effectiveness of genetic algorithms (GAs) in optimizing routing for EnFVs. The major contributions of this research lie in demonstrating the capability of GAs to handle complex optimization problems with multiple objectives, enabling the simultaneous consideration of factors like energy efficiency, travel time, and vehicle utilization. Moreover, GAs offer a flexible and adaptive approach to finding near-optimal solutions in dynamic transportation systems, making them suitable for real-world EnFV networks. While GAs show promise, there are also limitations, such as computational complexity, difficulty in capturing real-world constraints, and potential sub-optimal solutions. Addressing these challenges, the study highlights several future research directions, including the integration of real-time data and dynamic routing updates, hybrid approaches with other optimization techniques, consideration of uncertainty and risk management, scalability for large-scale routing problems, and enhancing energy efficiency and sustainability in routing. By exploring these avenues, researchers can further improve the efficiency and effectiveness of routing algorithms for EnFVs, paving the way for their seamless integration into modern transportation systems.

Risks of Drone Use in Light of Literature Studies

“…While [ 152 ] describes the technological or regulatory challenges for UAVs, in [ 204 , 222 ], attention is drawn to the legal-political, socio-legal approach and the implementation of regulations, highlighting the measures that should be taken before making a given technology available to the public. Some of the important areas are planning paths, avoiding obstacles, and navigation [ 143 , 154 , 156 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 ]. The aspects mentioned above have a significant impact on the operational safety of the drone.…”

Optimizing Disaster Response through Efficient Path Planning of Mobile Aerial Base Station with Genetic Algorithm

“…The authors of [18] propose a novel hybrid method, VDGA (Voronoi Diagram and Genetic Algorithm), to optimize data collection networks in the Internet of Things (IoT). The VDGA method leverages the strengths of both techniques: the Voronoi Diagram (VD) for initial positioning and the Genetic Algorithm (GA) for fine-tuning placement to achieve optimal network coverage.…”

“…In [18], GA is proposed as a solution for the multi-UAV path planning problem, incorporating timing and path constraints. This methodology uses Voronoi diagrams to avoid physical threats like power lines, hostile drones activity, and other obstacles, aiming to maximize the exploration of desired areas while avoiding no-fly zones.…”

“…Five fitness functions were used in this study to select a particular solution that is the most suitable and can adequately deal with the problem of choosing a specimen, which is the MABS path. The weight coefficient for score specimens is calculated using (18). It takes into account the SR, AR, DR, IR, and PSR as the result of the fitness functions of the specimens.…”