scholarly journals Multiple Swarm Fruit Fly Optimization Algorithm Based Path Planning Method for Multi-UAVs

2020 ◽  
Vol 10 (8) ◽  
pp. 2822 ◽  
Author(s):  
Kunming Shi ◽  
Xiangyin Zhang ◽  
Shuang Xia
Keyword(s):  
Path Planning ◽  
Nonlinear Optimization ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Fruit Fly ◽  
Local Optimum ◽  
Planning Problem ◽  
Fruit Fly Optimization Algorithm ◽  
Nonlinear Optimization Problem ◽  
Fruit Fly Optimization

The path planning of unmanned aerial vehicles (UAVs) in the threat and countermeasure region is a constrained nonlinear optimization problem with many static and dynamic constraints. The fruit fly optimization algorithm (FOA) is widely used to handle this kind of nonlinear optimization problem. In this paper, the multiple swarm fruit fly optimization algorithm (MSFOA) is proposed to overcome the drawback of the original FOA in terms of slow global convergence speed and local optimum, and then is applied to solve the coordinated path planning problem for multi-UAVs. In the proposed MSFOA, the whole fruit fly swarm is divided into several sub-swarms with multi-tasks in order to expand the searching space to improve the searching ability, while the offspring competition strategy is introduced to improve the utilization degree of each calculation result and realize the exchange of information among various fruit fly sub-swarms. To avoid the collision among multi-UAVs, the collision detection method is also proposed. Simulation results show that the proposed MSFOA is superior to the original FOA in terms of convergence and accuracy.

scholarly journals Solving the Multi-Functional Heterogeneous UAV Cooperative Mission Planning Problem Using Multi-Swarm Fruit Fly Optimization Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5026
Author(s):  
Rubin Luo ◽  
Hongxing Zheng ◽  
Jifeng Guo
Keyword(s):  
Optimization Algorithm ◽  
Rapid Development ◽  
Fruit Fly ◽  
Search Strategies ◽  
Mission Planning ◽  
Selection Strategy ◽  
Planning Problem ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Dual Strategy

The complexity of unmanned aerial vehicle (UAV) missions is increasing with the rapid development of UAV technology. Multiple UAVs usually cooperate in the form of teams to improve the efficiency of mission execution. The UAVs are equipped with multiple sensors with complementary functions to adapt to the complex mission constraints. Reasonable task assignment, task scheduling, and UAV trajectory planning are the prerequisites for efficient cooperation of multi-functional heterogeneous UAVs. In this paper, a multi-swarm fruit fly optimization algorithm (MFOA) with dual strategy switching is proposed to solve the multi-functional heterogeneous UAV cooperative mission planning problem with the criterion of simultaneously minimizing the makespan and the total mission time. First, the multi-swarm mechanism is introduced to enhance the global search capability of the fruit fly optimization algorithm. Second, in the smell-based search phase, the local search strategies and large-scale search strategies are designed to drive multiple fruit fly swarms, and the dual strategy switching method is presented. Third, in the vision-based search stage, the greedy selection strategy is adopted. Finally, numerical simulation experiments are designed. The simulation results show that the MFOA algorithm is more effective and stable for solving the multi-functional heterogeneous UAV cooperative mission planning problem compared with other algorithms.

Study on Path Planning Method for Mobile Robot Based on Fruit Fly Optimization Algorithm

2014 ◽  
Vol 536-537 ◽  
pp. 970-973 ◽  
Author(s):  
Tao Jiang ◽  
Jian Zhong Wang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Fruit Fly ◽  
Planning Method ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Labview Platform ◽  
The Mathematical Model

A path planning method based on fruit fly optimization algorithm was proposed. An optimization algorithm by the foraging process of fruit fly was presented, and the mathematical model of fitness function was established. The algorithm steps employing the LabVIEW platform were achieved. The experiments of path planning were carried out. The experimental results show that the optimization algorithm can achieve the path planning and avoidance of mobile robot, and thus to verify the feasibility.

scholarly journals An Improved Fruit Fly Optimization Algorithm Inspired from Cell Communication Mechanism

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Chuncai Xiao ◽  
Kuangrong Hao ◽  
Yongsheng Ding
Keyword(s):  
Carbon Fibers ◽  
Optimization Algorithm ◽  
Cell Communication ◽  
Fruit Fly ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Communication Mechanism ◽  
Evolution Stage

Fruit fly optimization algorithm (FOA) invented recently is a new swarm intelligence method based on fruit fly’s foraging behaviors and has been shown to be competitive with existing evolutionary algorithms, such as particle swarm optimization (PSO) algorithm. However, there are still some disadvantages in the FOA, such as low convergence precision, easily trapped in a local optimum value at the later evolution stage. This paper presents an improved FOA based on the cell communication mechanism (CFOA), by considering the information of the global worst, mean, and best solutions into the search strategy to improve the exploitation. The results from a set of numerical benchmark functions show that the CFOA outperforms the FOA and the PSO in most of the experiments. Further, the CFOA is applied to optimize the controller for preoxidation furnaces in carbon fibers production. Simulation results demonstrate the effectiveness of the CFOA.

Variables Screening Method Based on the Algorithm of Combining Fruit Fly Optimization Algorithm and RBF Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 3225-3230
Author(s):  
Fu Qiang Xu ◽  
You Tian Tao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Optimization Algorithm ◽  
Rbf Neural Network ◽  
Screening Method ◽  
Fruit Fly ◽  
Local Optimum ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
The Mean

The form of fruit fly optimization algorithm (FOA) is easy to learn and has the characteristics of quick convergence and not readily dropping into local optimum. This paper presents the optimization of RBF neural network by means of FOA and establishment of network model, adopting it with the combination of the evaluation of the mean impact value (MIV) to select variables. The validity of this model is tested by two actual examples, furthermore, it is simpler to learn, more stable and practical.

scholarly journals Cooperative 3D Path Planning of Multi-UAV Via Improved Fruit Fly Optimization

2021 ◽  
Author(s):  
Yongjie Mao ◽  
Deqing Huang ◽  
Na Qin ◽  
Lei Zhu ◽  
Jiaxi Zhao
Keyword(s):  
Path Planning ◽  
Optimization Algorithm ◽  
Three Dimensional ◽  
Solution Space ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Local Optima ◽  
Fruit Fly Optimization ◽  
Multiple Uavs ◽  
Multi Uav

Abstract Path planning of multiple unmanned aerial vehicles (UAVs) is a crucial step in cooperative operation of multiple UAVs, whose main difficulties lie in the severe coupling of time and three-Dimensional (3D) space as well as the complexity of multi-objective optimization. For this purpose, the time stamp segmentation (TSS) model is first adopted to resolve the timespace coupling among multiple UAVs. Meanwhile, the solution space is reduced by transforming the multiobjective problem to a multi-constraint problem. In consequence, based on the elite retention strategy, a novel improved fruit fly optimization algorithm (NIFOA) is proposed for multi-UAV cooperative path planning, which overcomes the shortcomings of basic fruit fly optimization algorithm in slow convergence speed and the potentials to fall into local optima. In particular, the multi-subpopulations evolution mechanism is further designed to optimize the elite subpopulation. At last, the effectiveness of the proposed NIFOA has been verified by numerical experiments.

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