An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ

In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random selection.To address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN model.In addition,different types and parameter ranges of existing genetic algorithms are used.Acomparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.

Enhancing the dynamic load balancing technique for cloud computing using HBATAABC algorithm

Cloud computing brings incipient transmutations in different fields of life and consists of different characteristics and virtualization is one of them.Virtual machine(VM)is one of the main elements of virtualization.VM is a process in which physical server changes into the virtual machine and works as a physical server.When a user sends data or request for data in cloud data center,a situation can occur that may cause the virtual machines to underload data or overload data.The aforementioned situation can lead to failure of the system or delay the user task.Therefore,appropriate load balancing techniques are required to surmount the above two mentioned problems.Load balancing is a technique utilized in cloud computing for management of the resource by a condition such that a maximum throughput is achieved with slightest reaction time and additionally dividing the traffic between different servers or VM so that it can get data without any delay.For the amelioration of load balancing technique in this study,a novel technique is used which is coalescence of BAT and ABC algorithms both of which are nature-inspired algorithms.When the ABC algorithm local search section changes with BAT algorithm local search section,a second modification takes place in the fitness function of BAT algorithm.The proposed technique is known as HBATAABC algorithm.The novel technique implemented by utilizing transfer strategy policy in VM improves the performance of data allocation system of VM in the cloud data center.To check the performance of the proposed algorithm,three main parameters are used which are network average time,network stability and throughput.The performance of the proposed novel technique is verified and tested with the help of cloudsim simulator.The result shows that the suggested modified algorithm increases performance by 1.30%of network average time,network stability and throughput as compared with BAT algorithm,ABC algorithm and RRA algorithm.Nevertheless,the proposed algorithm is more precise and expeditious as compared with the three models.