Robust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithm

2021 ◽  
Author(s):  
Betül Sultan Yildiz ◽  
Nantiwat Pholdee ◽  
Sujin Bureerat ◽  
Ali Riza Yildiz ◽  
Sadiq M. Sait
Keyword(s):  
Robust Design ◽  
Optimization Algorithm ◽  
Robot Gripper ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

An Image Segmentation Method Based on Two-Dimensional Entropy and Chaotic Lightning Attachment Procedure Optimization Algorithm

2020 ◽  
Vol 34 (11) ◽  
pp. 2054030 ◽  
Author(s):  
Wei Liu ◽  
Shuai Yang ◽  
Zhiwei Ye ◽  
Qian Huang ◽  
Yongkun Huang
Keyword(s):  
Image Segmentation ◽  
Maximum Entropy ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Computational Time ◽  
Two Dimensional ◽  
Threshold Segmentation ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Competitive Algorithms

Threshold segmentation has been widely used in recent years due to its simplicity and efficiency. The method of segmenting images by the two-dimensional maximum entropy is a species of the useful technique of threshold segmentation. However, the efficiency and stability of this technique are still not ideal and the traditional search algorithm cannot meet the needs of engineering problems. To mitigate the above problem, swarm intelligent optimization algorithms have been employed in this field for searching the optimal threshold vector. An effective technique of lightning attachment procedure optimization (LAPO) algorithm based on a two-dimensional maximum entropy criterion is offered in this paper, and besides, a chaotic strategy is embedded into LAPO to develop a new algorithm named CLAPO. In order to confirm the benefits of the method proposed in this paper, the other seven kinds of competitive algorithms, such as Ant–lion Optimizer (ALO) and Grasshopper Optimization Algorithm (GOA), are compared. Experiments are conducted on four different kinds of images and the simulation results are presented in several indexes (such as computational time, maximum fitness, average fitness, variance of fitness and other indexes) at different threshold levels for each test image. By scrutinizing the results of the experiment, the superiority of the introduced method is demonstrated, which can meet the needs of image segmentation excellently.

scholarly journals Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm

2018 ◽  
Vol 10 (3) ◽  
pp. 478-495 ◽  
Author(s):  
Ibrahim Aljarah ◽  
Ala’ M. Al-Zoubi ◽  
Hossam Faris ◽  
Mohammad A. Hassonah ◽  
Seyedali Mirjalili ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Optimization Algorithm ◽  
Support Vector ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Nelder Mead with Grasshopper Optimization Algorithm for Node Localization in Wireless Sensor Networks

2020 ◽  
Vol 17 (12) ◽  
pp. 5409-5421
Author(s):  
M. Santhosh ◽  
P. Sudhakar
Keyword(s):  
Optimization Algorithm ◽  
Wireless Sensor ◽  
Target Area ◽  
Node Localization ◽  
Localization Accuracy ◽  
Simplex Search ◽  
Grasshopper Optimization Algorithm ◽  
Anchor Nodes ◽  
Series Of Experiments ◽  
Grasshopper Optimization

Node localization in wireless sensor network (WSN) becomes essential to calculate the coordinate points of the unknown nodes with the use of known or anchor nodes. The efficiency of the WSN has significant impact on localization accuracy. Node localization can be considered as an optimization problem and bioinspired algorithms finds useful to solve it. This paper introduces a novel Nelder Mead with Grasshopper Optimization Algorithm (NMGOA) for node localization in WSN. The Nelder-Mead simplex search method is employed to improve the effectiveness of GOA because of its capability of faster convergence. At the beginning, the nodes in WSN are arbitrarily placed in the target area and then nodes are initialized. Afterwards, the node executes the NMGOA technique for estimating the location of the unknown nodes and become localized nodes. In the subsequent round, the localized nodes will be included to the collection of anchor nodes to perform the localization process. The effectiveness of the NMGOA model is validated using a series of experiments and results indicated that the NMGOA model has achieved superior results over the compared methods.

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