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 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 Optimization of Order-Picking Problems by Intelligent Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhong-huan Wu ◽  
Hong-jie Chen ◽  
Jia-jia Yang
Keyword(s):  
Optimization Algorithm ◽  
Optimal Solution ◽  
Fruit Fly ◽  
Order Picking ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Intelligent Optimization Algorithm ◽  
Random Weight ◽  
Weight Decrease ◽  
The Mathematical Model

To improve the efficiency of warehouse operations, reasonable optimization of picking operations has become an important task of the modern supply chain. For the purpose of optimization of order picking in warehouses, a new fruit fly optimization algorithm, particle swarm optimization, random weight, and weight decrease model are used to solve the mathematical model. Further optimization is achieved through the analysis of the warehouse shelves and screening of the optimal solution of the picking time. In addition, simulation experiments are conducted in the MATLAB environment through programming. The shortest picking time is found out and chosen as an optimized method by taking advantage of the effectiveness of these six algorithms in the picking optimization and comparing the data obtained under the simulation. The result shows that the optimization capacity of RWFOA is better and the picking efficiency is the best.

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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