scholarly journals Collision Avoidance Controller for Unmanned Surface Vehicle Based on Improved Cuckoo Search Algorithm

2021 ◽  
Vol 11 (20) ◽  
pp. 9741
Author(s):  
Yunsheng Fan ◽  
Xiaojie Sun ◽  
Guofeng Wang ◽  
Dongdong Mu
Keyword(s):  
Collision Avoidance ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Variable Step Size ◽  
Size Factor ◽  
Step Size ◽  
Unmanned Surface Vehicle ◽  
Avoidance Strategy ◽  
Improved Cuckoo Search Algorithm

For the dynamic collision avoidance problem of an unmanned surface vehicle (USV), a dynamic collision avoidance control method based on an improved cuckoo search algorithm is proposed. The collision avoidance model for a USV and obstacles is established on the basis of the principle of the velocity obstacle method. Simultaneously, the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) is incorporated in the collision avoidance process. For the improvement of the cuckoo algorithm, the adaptive variable step-size factor is designed to realize the adaptive adjustment of flight step-size, and a mutation and crossover strategy is introduced to enhance the population diversity and improve the global optimization ability. The improved cuckoo search algorithm is applied to the collision avoidance model to obtain an optimal collision avoidance strategy. According to the collision avoidance strategy, the desired evasion trajectory is obtained, and the tracking controller based on PID is used for the Lanxin USV. The experimental results show the feasibility and effectiveness of the proposed collision avoidance method, which provides a solution for the autonomous dynamic collision avoidance of USVs.

scholarly journals An Improved Cuckoo Search Algorithm for Multithreshold Image Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wentan Jiao ◽  
Wenqing Chen ◽  
Jing Zhang
Keyword(s):  
Image Segmentation ◽  
Maximum Entropy ◽  
Search Algorithm ◽  
Fitness Function ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Threshold Function ◽  
Size Factor ◽  
Step Size ◽  
Multiple Thresholds

Image segmentation is an important part of image processing. For the disadvantages of image segmentation under multiple thresholds such as long time and poor quality, an improved cuckoo search (ICS) is proposed for multithreshold image segmentation strategy. Firstly, the image segmentation model based on the maximum entropy threshold is described, and secondly, the cuckoo algorithm is improved by using chaotic initialization population to improve the diversity of solutions, optimizing the step size factor to improve the possibility of obtaining the optimal solution, and using probability to reduce the complexity of the algorithm; finally, the maximum entropy threshold function in image segmentation is used as the individual fitness function of the cuckoo search algorithm for solving. The simulation experiments show that the algorithm has a good segmentation effect under four different thresholding conditions.

Coverage optimization of VLC in smart homes based on improved cuckoo search algorithm

2017 ◽  
Vol 116 ◽  
pp. 63-78 ◽  
Author(s):  
Geng Sun ◽  
Yanheng Liu ◽  
Ming Yang ◽  
Aimin Wang ◽  
Shuang Liang ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Smart Homes ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Coverage Optimization ◽  
Improved Cuckoo Search Algorithm

A solution method for treatment scheduling in rehabilitation hospitals with real-life requirements

2018 ◽  
Vol 30 (4) ◽  
pp. 367-386 ◽  
Author(s):  
Liyang Xiao ◽  
Mahjoub Dridi ◽  
Amir Hajjam El Hassani ◽  
Wanlong Lin ◽  
Hongying Fei
Keyword(s):  
Waiting Time ◽  
Programming Model ◽  
Search Algorithm ◽  
Real Life ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Mixed Integer ◽  
Solution Quality ◽  
Improved Cuckoo Search Algorithm ◽  
Rehabilitation Hospitals

Abstract In this study, we aim to minimize the total waiting time between successive treatments for inpatients in rehabilitation hospitals (departments) during a working day. Firstly, the daily treatment scheduling problem is formulated as a mixed-integer linear programming model, taking into consideration real-life requirements, and is solved by Gurobi, a commercial solver. Then, an improved cuckoo search algorithm is developed to obtain good quality solutions quickly for large-sized problems. Our methods are demonstrated with data collected from a medium-sized rehabilitation hospital in China. The numerical results indicate that the improved cuckoo search algorithm outperforms the real schedules applied in the targeted hospital with regard to the total waiting time of inpatients. Gurobi can construct schedules without waits for all the tested dataset though its efficiency is quite low. Three sets of numerical experiments are executed to compare the improved cuckoo search algorithm with Gurobi in terms of solution quality, effectiveness and capability to solve large instances.

Optimized Rod Size Design of Denim Fabric Grinding Robot Based on Improved Cuckoo Search Algorithm

2021 ◽  
Vol 14 ◽  
Author(s):  
Wenjie Wang ◽  
Congcong Chen ◽  
Yuting Cao ◽  
Jian Xu ◽  
Xiaohua Wang
Keyword(s):  
Optimal Design ◽  
Optimization Design ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Convergence Speed ◽  
Motion Performance ◽  
Improved Cuckoo Search Algorithm ◽  
Solution Accuracy ◽  
Cuckoo Algorithm

Background: Dexterity is an important index for evaluating the motion performance of a robot. The size of the robot connecting rods directly affects the performance of flexibility. Objective: The purpose of this study is to provide an overview of optimal design methods from many pieces of literature and patents, and propose a new optimal design method for ensuring the robot completes its tasks flexibly and efficiently under workspace constraints. Methods: The kinematics and working space of the robot are analyzed to determine the range of motion of each joint. Then, a dexterity index is established based on the mean value of the global spatial condition number. Finally, an improved cuckoo algorithm is proposed, which changes the step size control factor with the number of iterations. Taking the dexterity index as the objective optimization function and the working radius as the constraint condition, the improved cuckoo search algorithm is used to optimize the size of the robot rod. Results: The improved cuckoo algorithm and proposed rod size optimized method are fully evaluated by experiments and comparative studies. The optimization design process shows that the proposed method has better solution accuracy and faster convergence speed. The optimized design results show that the robot's dexterity index has increased by 26.1%. Conclusion: The proposed method has better solution accuracy and faster convergence speed. The method was suitable for optimizing the rod parameters of the robot, and it was very meaningful to improve the motion performance of the robot.

Performances of Adaptive Cuckoo Search Algorithm in Engineering Optimization

2016 ◽  
pp. 676-699 ◽  
Author(s):  
Pauline Ong ◽  
S. Kohshelan
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Breeding Strategy ◽  
Function Optimization ◽  
Engineering Optimization ◽  
Optimal Solutions ◽  
Step Size ◽  
Swarm Optimization ◽  
Searching Strategy

A new optimization algorithm, specifically, the cuckoo search algorithm (CSA), which inspired by the unique breeding strategy of cuckoos, has been developed recently. Preliminary studies demonstrated the comparative performances of the CSA as opposed to genetic algorithm and particle swarm optimization, however, with the competitive advantage of employing fewer control parameters. Given enough computation, the CSA is guaranteed to converge to the optimal solutions, albeit the search process associated to the random-walk behavior might be time-consuming. Moreover, the drawback from the fixed step size searching strategy in the inner computation of CSA still remain unsolved. The adaptive cuckoo search algorithm (ACSA), with the effort in the aspect of integrating an adaptive search strategy, was attached in this study. Its beneficial potential are analyzed in the benchmark test function optimization, as well as engineering optimization problem. Results showed that the proposed ACSA improved over the classical CSA.

scholarly journals Adaptive Cuckoo Search Algorithm for Unconstrained Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Pauline Ong
Keyword(s):  
Marked Improvement ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimal Solutions ◽  
Step Size ◽  
Adjustment Strategy ◽  
Global Optimal ◽  
Adaptive Step ◽  
Size Adjustment

Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.

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