scholarly journals A Novel Fuzzy logic based Improved Cuckoo Search Algorithm

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Set Theory ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Control Parameters ◽  
Objective Functions ◽  
Local Optima ◽  
Kapur’S Entropy ◽  
Multi Level ◽  
Improved Cuckoo Search Algorithm

Cuckoo Search (CS) algorithm is a nature-inspired optimization algorithm (NIOA) with less control parameters that is stable, versatile, and easy to implement. CS has good global search capabilities, but it is prone to local optima problems. As a result, it may be possible to improve the classic CS algorithm's optimization capability. Centered on fuzzy set theory, this paper introduces an improved CS version. The population of solutions has been divided into two fuzzy sets, and each solution is assigned to one of the sets based on its fitness. The fuzzy collection centroids, global best solution advice, and Lévy distribution dependent mutation are all used to boost the population's solutions. With well-accepted objective functions such as Otsu inter class variance and Kapur's entropy, the experimental analysis has been conducted on the CEC-2014 test suite and image multi-level thresholding domain. The proposed fuzzy cuckoo search (FCS) algorithm is compared to the classical CS, PSO, FA, SMA, and BA algorithm and provides satisfactory results.

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.

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