Quantum Behavior-Based Enhanced Fruit Fly Optimization Algorithm with Application to UAV Path Planning

Abstract

As a newly developed simple and effective optimization technology, the fruit fly optimization algorithm (FOA) has been successfully applied in many fields. To accelerate the algorithm convergence and avoid the local optimum, the enhanced FOA based on quantum theory called QFOA is proposed in this paper. When establishing the quantum Delta potential well around the location of fruit fly swarm, QFOA introduces the quantum behavior-based searching mechanism into the original osphresis-based search procedure of FOA. In the process that fruit flies find and move toward the food source, fruit flies follow the wave function property of the Delta potential well rather than the Newtonian mechanics. Taking advantage of the probability and uncertainty of quantum theory, the proposed QFOA can effectively overcome the weakness in premature convergence and easy trapping into local optimum. Since there are two popular models of the basic FOA, this paper also develops two corresponding QFOAs. Experimental results on various benchmark functions show that both the two QFOA models has overall better performance compared with the basic FOA as well as other FOA variants and other well-known optimization algorithms. In addition, the proposed QFOAs are also applied to unmanned aerial vehicle (UAV) path planning problem in the three-dimensional environment, and comparative results about the obtained optimal flight path and population convergence process show the effectiveness of QFOAs.

Meta-heuristic Algorithms in UAV Path Planning Optimization: A Systematic Review (2018–2022)

“…Meta-Heuristic-Type [62] Collision free 4D path planning for multiple UAVs based on spatial refined voting mechanism and PSO approach SRVM Swarm-based algorithm [63] UAV path planning for data ferrying with communication constraints GA Evolutionary algorithm [64] Adaptive meta-heuristic-based methods for autonomous robot path planning: sustainable agricultural applications I-GWO Swarm-based algorithm [11] UAVs path planning architecture for effective medical emergency response in future networks CVRP Hybrid [65] Quantum behavior-based enhanced fruit fly optimization algorithm with application to UAV path planning QFOA Hybrid [66] A cooperative target search method based on intelligent water drops algorithm LMIWD Hybrid [67] Autonomous unmanned aerial vehicles in search and rescue missions using real-time cooperative model predictive control PSO Swarm-based algorithm [12] A grey wolf optimizer using gaussian estimation of distribution and its application in the multi-UAV multi-target urban tracking problem GEDGWO Hybrid [68] UAV trajectory planning based on PH curve improved by particle swarm optimization and quasi-newton method PSO and Quasi-Newton Method Hybrid [69] On-board real-time trajectory planning for fixed wing unmanned aerial vehicles in extreme environments RTTP base GA Evolutionary algorithm [70] Grey wolf optimization based sense and avoid algorithm in a bayesian framework for multiple UAV path planning in an uncertain environment GWO Swarm-based algorithm [71] Evolutionary collaborative human-UAV search for escaped criminals HEA Hybrid [72] A practical methodology for generating high-resolution 3D models of Open-Pit slopes using UAVs: flight path planning and optimization…”

Hybrid Quantum Genetic Algorithm with Fuzzy Adaptive Rotation Angle for Efficient Placement of Unmanned Aerial Vehicles in Natural Disaster Areas

“…The objective of the path planning task is to find the optimal route from an initial point to a final point, avoiding obstacles [23,24]. In [25], a quantumbehavior-based enhanced fruit fly optimization algorithm (QFOA) for UAV path planning is shown. The QFOA aims to accelerate algorithm convergence and avoid local optimality by incorporating quantum theory into the fruit fly optimization algorithm (FOA).…”

Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles

“…Duan et al [ 11 ] presented a novel swarm intelligence optimizer based on the collective behavior of pigeons, after which authors applied this pigeon-inspired optimization for solving air robot path planning problems. Zhang et al [ 12 ] proposed an enhanced fruit fly optimization algorithm based on quantum theory and the collective behavior of fruit flies. In addition, the proposed algorithm was also adopted to unmanned aerial vehicle path planning problems in the three-dimensional environment.…”