A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

Abstract

The concept of smart cities is to enhance the life quality of residents and provide efficient services by integrating advanced information and communication technologies, autonomous robots, Internet of Things (IoT) devices, etc. Unmanned Aerial Vehicles (UAVs) are a class of autonomous flying mobile robots that bring a lot of benefits to smart cities due to their mobility, accessibility, autonomy, and many other advantages. Their integration allows for accomplishing hard and complex tasks that humans or other entities are not able to complete. In most applications, multiple connected UAVs are required to build a network under which missions are completed and tasks are shared. This network can be established by finding the optimal UAV placement that meets some requirements such as user coverage, UAV connectivity, energy, and load distribution. In this paper, we present a hybrid algorithm, called IMRFO-TS, based on the hybridization of Improved Manta Ray Foraging Optimization (IMRFO) with the Tabu Search (TS) algorithm for solving the UAV placement problem in a smart city. First, the tangential control strategy is incorporated into the original MRFO algorithm to enhance its convergence speed and explore the search space effectively. Second, The TS algorithm is hybridized with the IMRFO algorithm to increase the exploitation capability of IMRFO and improve the best solution (placement quality) obtained after each iteration. The performance of the proposed IMRFO-TS algorithm is validated using 52 benchmarks considering the fitness value, coverage, connectivity, energy consumption, and load distribution parameters. Compared to eight well-known optimization meta-heuristics such as the original MRFO, TS algorithm, Bat Algorithm (BA), Firefly Algorithm (FA), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Reptile Search Algorithm (RSA), the results of the experiments revealed the significant superiority of the proposed IMRFO-TS algorithm by obtaining promising solutions (optimal positions of UAVs) in the majority of the cases.

A Survey on Manta Ray Foraging Optimization Algorithm of Variants and Applications

“…Researchers had effectively processed a large amount of data using RBFNN and simulated the chain-like, vortex-like, and rolling foraging behavior of manta rays through MRFO, achieving effective exploration in a large search space. Saadi et al (2023) introduced a hybrid algorithm, IMRFO-TS, which combined improved MRFO with the TS algorithm for optimal unmanned aerial vehicles (UAVs) placement in smart cities. The algorithm effectively balanced user coverage, UAV interconnectivity, energy expenditure, and load distribution, demonstrating superior performance over other metaheuristic algorithms in finding optimal UAV positions.…”

A Novel Network Optimization Scheme Based on Anti-Flocking and Improved Nash Equilibrium Algorithm

“…Chowdhury and De [14] proposed an improved Reverse Glowworm Swarm Optimization (RGSO) algorithm to avoid possible collisions in the motion path of UAVs in their study of the 3D path planning problem of UAVs. A hybrid algorithm based on Improved Manta Ray Foraging Optimization and Tabu Search (IMRFO-TS) algorithm was proposed by AIT SAADI et al [15] to obtain the optimal deployment location of UAVs in the network to improve its convergence speed and explore the UAV search area efficiently when studying the problem of building multi-UAV networks in the context of smart cities.…”