ABCADF: Deploy Artificially Bee Colony Algorithm for Model Transformation Cohesive with Fitness Function of Adaptive Dragonfly Algorithm

2020 ◽  
pp. 277-287
Author(s):  
Pramod P. Jadhav ◽  
Shashank D. Joshi
Keyword(s):  
Model Transformation ◽  
Fitness Function ◽  
Dragonfly Algorithm ◽  
Bee Colony

WOADF: Whale Optimization Integrated Adaptive Dragonfly Algorithm Enabled with the TDD Properties for Model Transformation

2019 ◽  
Vol 18 (04) ◽  
pp. 1950026 ◽  
Author(s):  
Pramod P. Jadhav ◽  
Shashank D. Joshi
Keyword(s):  
Model Transformation ◽  
Fitness Function ◽  
Test Cases ◽  
Relational Schema ◽  
Dragonfly Algorithm ◽  
Proposed Model ◽  
Fitness Value ◽  
Whale Optimization ◽  
Input Model ◽  
Class Diagrams

Model Transformation (MT) has led the researchers to concentrate more in the field of software engineering. MT focuses mainly on transforming the input model to the target model to make it easily understandable. For the transformation, using optimal rules among a set of rules makes the design simpler. This paper proposes an algorithm, namely Whale Optimization integrated Adaptive Dragonfly (WOADF) algorithm, which integrates Adaptive Dragonfly (ADF) algorithm and Whale Optimization Algorithm (WOA), for transforming class diagrams (CLDs) to Relational Schema (RS). Further, the UML CLD is transformed into the RS model based on specific rules incorporated by the proposed WOADF algorithm. The fitness function of the proposed model is evaluated to select the optimal rule, by including the test cases to evaluate the optimal blocks. Then, the optimal blocks obtained from the proposed WOADF algorithm are used for achieving the transformation from CLD to the RS model. The effectiveness of the proposed WOADF algorithm is checked with Automatic Correctness (AC) and fitness values and is evaluated to be the best when compared to other existing techniques with maximum AC value measured to be 0.812 and fitness value to be 0.897, respectively.

scholarly journals BADF: Bat inspired Optimize solution for model transformation assisted with fitness function of Adaptive dragonfly algorithm (ADF)

2021 ◽  
Author(s):  
Pramod Pandurang Jadhav
Keyword(s):  
Software Engineering ◽  
Model Transformation ◽  
Fitness Function ◽  
Class Diagram ◽  
Model Driven Engineering ◽  
Engineering Model ◽  
Relational Schema ◽  
Dragonfly Algorithm ◽  
Model Driven

Abstract Model transformation is the conspicuous research statement in the area of software engineering. Model transformation (MT) is playing the measure role in the Model driven engineering (MDE), which is helpful to transfer the model from one set of databases to another set of databases by considering the simulation and also support to various language. Propose work elaborate the Bat inspired optimize solution for model transformation using Adaptive Dragonfly Algorithm (BADF), and transform Class diagram (CLD) in to the relational schema (RS), accompanied by fitness function. Further performance of the proposed algorithm is appraised using Automatic Correctness (AC) and fitness measure, by comparing existing algorithm.

Adaptive-Sunflower Based Grey Wolf Algorithm For Multipath Routing In Iot Network

2021 ◽  
Vol 17 (2) ◽  
pp. 0-0
Keyword(s):  
Fitness Function ◽  
Optimization Technique ◽  
Multipath Routing ◽  
High Energy ◽  
Grey Wolf ◽  
Maximum Throughput ◽  
Route Maintenance ◽  
Bee Colony ◽  
Direct Trust ◽  
Indirect Trust

This paper devises a routing method for providing multipath routing inan IoT network. Here the Fractional Artificial Bee colony(FABC)algorithm is devised for initiating clustering process. Moreover the multipath routing is performed by the newly devised optimization technique, namely Adaptive-Sunflower based grey wolf(Adaptive-SFG)optimization technique which is designed by incorporating adaptive idea in Sunflower based grey wolf technique. In addition the fitness function is newly devised by considering certain factors that involves Context awareness, link lifetime Energy, Trust, and Delay.For the computation of the trust, additional trust factors like direct trust indirect trust recent trust and forwarding rate factor is considered. Thus, the proposed Adaptive SFG algorithm selects the multipath for routing based on the fitness function.Finally, route maintenance is performed to ensure routing without link breakage.The proposed Adaptive-SFG outperformed other methods with high energy of0.185Jminimal delay of 0.765sec maximum throughput of47.690%and maximum network lifetime of98.7%.

An Optimal Way of VM Placement Strategy in Cloud Computing Platform Using ABCS Algorithm

2021 ◽  
Vol 12 (3) ◽  
pp. 16-38
Author(s):  
Pushpa R. ◽  
M. Siddappa
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Virtual Machines ◽  
Fitness Function ◽  
Vm Placement ◽  
Migration Strategy ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Migration Cost ◽  
Chicken Swarm Optimization

In this paper, VM replacement strategy is developed using the optimization algorithm, namely artificial bee chicken swarm optimization (ABCSO), in cloud computing model. The ABCSO algorithm is the integration of the artificial bee colony (ABC) in chicken swarm optimization (CSO). This method employed VM placement based on the requirement of the VM for the completion of the particular task using the service provider. Initially, the cloud system is designed, and the proposed ABCSO-based VM placement approach is employed for handling the factors, such as load, CPU usage, memory, and power by moving the virtual machines optimally. The best VM migration strategy is determined using the fitness function by considering the factors, like migration cost, load, and power consumption. The proposed ABCSO method achieved a minimal load of 0.1688, minimal power consumption of 0.0419, and minimal migration cost of 0.0567, respectively.

scholarly journals A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2790
Author(s):  
Qi Xiong ◽  
Xinman Zhang ◽  
Shaobo He ◽  
Jun Shen
Keyword(s):  
Fractional Order ◽  
Iris Recognition ◽  
Search Algorithm ◽  
Fitness Function ◽  
Histogram Equalization ◽  
Enhancement Effect ◽  
Local Optimum ◽  
Long Distance ◽  
Iris Image ◽  
Bee Colony

At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

scholarly journals An Efficient Novel Load Balancing Algorithm to Improve the Performance of the System in Cloud Environment

2019 ◽  
Vol 8 (S3) ◽  
pp. 105-108
Author(s):  
P. Neelima ◽  
A. Rama Mohan Reddy
Keyword(s):  
Load Balancing ◽  
Fitness Function ◽  
Dynamic Environment ◽  
Computing System ◽  
Dragonfly Algorithm ◽  
Main Challenge ◽  
Cloud Computing System ◽  
Load Balancing Algorithm ◽  
Load Balancer ◽  
Is Design

Distribution of workload in a balanced manner is a main challenge in cloud computing system. It distributes workload among multiple nodes, hence resources are properly utilized. This is an optimization problem and a good load balancer should be involved for this strategy to the types of tasks and dynamic environment. To overcome load balancing problem here a Novel Load balancing Algorithm is develop i.e. Dragonfly Algorithm is design and developed, to execute the entire task with shortest completion time and load balanced. Our algorithm will be presented with efficient solution representation, derivation of efficient fitness function (or multi-objective function) along with the usual Dragonfly operators. The performance of the algorithm will be analyzed based on the different evaluation measures. The algorithms like particle swarm optimization (PSO) and Genetic algorithm (GA) will be taken for the comparative analysis.

Topology Optimization of Multicell Tubes Under Out-of-Plane Crushing Using a Modified Artificial Bee Colony Algorithm

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Jianguang Fang ◽  
Guangyong Sun ◽  
Na Qiu ◽  
Grant P. Steven ◽  
Qing Li
Keyword(s):  
Topology Optimization ◽  
Artificial Bee Colony ◽  
Fitness Function ◽  
Food Sources ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Design Variables ◽  
Highly Nonlinear ◽  
Out Of Plane ◽  
Energy Absorbing

Multicell tubal structures have generated increasing interest in engineering design for their excellent energy-absorbing characteristics when crushed through severe plastic deformation. To make more efficient use of the material, topology optimization was introduced to design multicell tubes under normal crushing. The design problem was formulated to maximize the energy absorption while constraining the structural mass. In this research, the presence or absence of inner walls were taken as design variables. To deal with such a highly nonlinear problem, a heuristic design methodology was proposed based on a modified artificial bee colony (ABC) algorithm, in which a constraint-driven mechanism was introduced to determine adjacent food sources for scout bees and neighborhood sources for employed and onlooker bees. The fitness function was customized according to the violation or the satisfaction of the constraints. This modified ABC algorithm was first verified by a square tube with seven design variables and then applied to four other examples with more design variables. The results demonstrated that the proposed heuristic algorithm is capable of handling the topology optimization of multicell tubes under out-of-plane crushing. They also confirmed that the optimized topological designs tend to allocate the material at the corners and around the outer walls. Moreover, the modified ABC algorithm was found to perform better than a genetic algorithm (GA) and traditional ABC in terms of best, worst, and average designs and the probability of obtaining the true optimal topological configuration.

scholarly journals Hybrid Binary Dragonfly Optimization Algorithm with Statistical Dependence for Feature Selection

2020 ◽  
Vol 5 (6) ◽  
pp. 1420-1428
Author(s):  
Omar S. Qasim ◽  
Mohammed Sabah Mahmoud ◽  
Fatima Mahmood Hasan
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Feature Selection Method ◽  
Statistical Dependence ◽  
K Nearest Neighbors ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Dragonfly Algorithm ◽  
The Cost

The aim of the feature selection technique is to obtain the most important information from a specific set of datasets. Further elaborations in the feature selection technique will positively affect the classification process, which can be applied in various areas such as machine learning, pattern recognition, and signal processing. In this study, a hybrid algorithm between the binary dragonfly algorithm (BDA) and the statistical dependence (SD) is presented, whereby the feature selection method in discrete space is modeled as a binary-based optimization algorithm, guiding BDA and using the accuracy of the k-nearest neighbors classifier on the dataset to verify it in the chosen fitness function. The experimental results demonstrated that the proposed algorithm, which we refer to as SD-BDA, outperforms other algorithms in terms of the accuracy of the results represented by the cost of the calculations and the accuracy of the classification.

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