scholarly journals Link breakage maintenance and power-aware secure routing in MANET based on the Fuzzy Krill Herd-based Grasshopper optimization

2019 ◽  
Vol 9 (2) ◽  
pp. 766-774
Keyword(s):  
Power Consumption ◽  
Ad Hoc ◽  
Life Time ◽  
Secure Routing ◽  
Fuzzy Function ◽  
Delay Detection ◽  
Krill Herd ◽  
Grasshopper Optimization Algorithm ◽  
Fitness Parameters ◽  
Grasshopper Optimization

As Mobile Ad hoc Networks (MANETs) are established using battery-powered nodes, the major concern to minimize the power consumption of the nodes is still a challenge while the lifetime of the network is taken into account. Thus, power-aware routing is the effective way to handle the situation that aims at minimizing the power consumption and network overhead in order to increase the network lifespan. Therefore, an effective power-aware secure routing protocol is designed using the proposed Fuzzy Krill Herd-based Grasshopper Optimization Algorithm (Fuzzy KH-GOA) such that the secure path is determined. Hence, it is evident that the routing path for transmitting the data packets is effectively decided using the proposed algorithm, which is based on the fitness parameters, such as fuzzy function, delay, distance, and power consumed by the path. The significance of this research relies on the link lifetime estimation for which the lifetime of the routes is detected in order to measure the link failure. The performance of the proposed Fuzzy KH-GOA reported the values of 23.858 J, 0.016 sec, 0.783, 0.840, and 10.908 sec, respectively in the absence of the attack and 22.624J, 22.624sec, 0.773, 0.812, and 9.862sec, respectively in the presence of attack for the measures, such as power, delay, Detection Rate (DR), throughput, and average Link Life Time.

Estudo de algoritmos de otimização bio-inspirados aplica à segmentação de imagens

2020 ◽  
Author(s):  
◽  
N. T. Saito
Keyword(s):  
Optimization Algorithm ◽  
Differential Evolution Algorithm ◽  
Grey Wolf Optimizer ◽  
Extensive System ◽  
Bee Colony ◽  
Krill Herd ◽  
Grasshopper Optimization Algorithm ◽  
Whale Optimization ◽  
Nonadditive Entropy ◽  
Grasshopper Optimization

Image segmentation is one of the first steps within the framework for processing scenes. Among the main existing techniques, we highlight the histogram-based binarization, which due to the simplicity of understanding and low computational complexity is one of the most used methods. However, for a multi-threshold process, this method becomes computationally costly. To minimize this problem, optimization algorithms are used to find the best thresholds. Recently, several algorithms inspired by nature have been proposed in a generic way in the area of combinatorial optimization and obtained excellent results, among which we highlight the more traditional ones such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution Algorithm (DE), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Krill Herd (KH). This work shows a comparison between some of these algorithms and more recent algorithms, from 2014, as Grey Wolf Optimizer (GWO), Elephant Herding Optimization (EHO), Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA) and Harris Hawks Optmization (HHO) . This work compared the thresholds obtained by 7 bio-inspired algorithms in a base composed of 100 images with 1 single object provided by the Weizmann Institute of Science (WIS). The comparison was made using consolidated metrics like Dice/Jaccard and PSNR, as well the recent Hxyz. In the experiments were used the extensive system as an objective function (Kapurs´ Method). Still in the proposal of this experiment, the extensive system was compared with a Tsallis nonadditive entropy, with the Super-extensive system being configured with q ? [0.1, 0.2, . . . 0.9] and the Sub-extensive system with q ? [1.1, 1.2, . . . 1.9]. The image Database contains 100 images with only 1 object on scene. The results show that the Krill Herd (KH) algorithm was the winning algorithm in 35% of executions according to the PSNR metric, 28% in the Dice/Jaccard metric and 35% on the Hxyz metric. The extensive system had the best overall performance and was responsible for the best threshold of 54 images according to the metric PSNR, 30 according to the metric Dice/Jaccard and 39 according to the Hxyz metric

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

scholarly journals Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms

2021 ◽  
Vol 11 (2) ◽  
pp. 485
Author(s):  
Amirreza Kandiri ◽  
Farid Sartipi ◽  
Mahdi Kioumarsi
Keyword(s):  
Compressive Strength ◽  
Optimization Algorithms ◽  
Recycled Aggregate ◽  
Recycled Aggregates ◽  
Tree Model ◽  
Grasshopper Optimization Algorithm ◽  
Compressive Strength Of Concrete ◽  
Grasshopper Optimization ◽  
Artificial Neural Network Ann ◽  
Industry Needs

Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the required information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the efficiency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other.

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.

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