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

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.