Hybrid Group Search Optimization Research Articles

Retraction Note to: Non functional requirement based software architecture scheme with security requirement using hybrid group search optimization and genetic algorithm

Retraction Note to: Non functional requirement based software architecture scheme with security requirement using hybrid group search optimization and genetic algorithm

RETRACTED ARTICLE: Non functional requirement based software architecture scheme with security requirement using hybrid group search optimization and genetic algorithm

Managing non functional requirements has been a challenge onto software engineers for a long time. Throughout the years, numerous strategies and methods have been proposed to enhance their elicitation, documentation and approval. Similarly, security requirements emphatically impact the building configuration of complex IT frameworks comparatively as non-functional requirements. Both security building and software engineering furnish strategies to manage such necessities. Nonetheless, there is still a basic crevice concerning the joining of techniques for these two different fields. In this paper we close this crevice as for security prerequisites by proposing a technique that joins software engineering technologies with the best in class security engineering standards. An efficient quality requirement based software model with security requirements is developed by fusing Group Search Optimization and genetic algorithm in our proposed methodology.

Design of Interleaver with High Dimensional Encoding Principle using Hybrid Group Search Optimization

Design of Interleaver with High Dimensional Encoding Principle using Hybrid Group Search Optimization

A hybrid group search optimization: firefly algorithm-based big data framework for ancient script recognition

Optical character recognition is becoming one of the widely researched areas in recent times. This research paper presents an optimization framework for ancient script recognition using the process of script or character segmentation. The proposed algorithm is based on evolutionary algorithm and capable of handing a continuous script of high-resolution data using concepts of big data. A hybrid combination of group search and firefly algorithm has been proposed in this research work and compared against recent works. Optimal classifications results are observed and recorded in this research paper.

Hybrid Group Search Optimizer for Plant Leaf Classification

The analysis of Big Data is a data mining discipline in which large quantity of unstructured data is analysed which can be challenging to store and also to retrieve efficiently. The classification of plants is based on the identification of leaf that has a very broad application in both agriculture and medicine. In this work, a method which is computerized is used to recognize a plant leaf based on images is proposed. The proposed method extracts features from the image and these are used for classifying the plant leaf. The process of deciding on the subset for all relevant features to be used in the construction of a system is known as feature selection. The Group Search Optimizer (GSO) is a nature-inspired algorithm that possesses all the qualities used effectively to solve feature selection tasks. In this work, there is a GSO-based algorithm of feature selection along with fuzzy logic and the classifier of a Neural Network (NN) is proposed. The results of the experiment prove the proposed method (GSO-NN) was able to achieve a better level of performance compared to the other methods.

Building Large Scale Cloud System for Product Sentiment Analysis using Hybrid Group Search Optimization Based Feature Selection

A very powerful technology that performs complex computing in a massive scale is known as Cloud computing. There has been a massive growth that has been observed in the data scale which may also be big data which is generated by means of cloud computing which is observed. Sentiment Analysis, on the other hand, denotes the opinion extraction of users from the documents used for review. A sentiment classification that makes use of methods of Machine Learning (ML) can face problems in high dimensionality for a feature vector. Thus, the method of feature selection is needed for the elimination of all noisy and irrelevant features from a feature vector for efficiently working the ML algorithms. All chosen features will be sub-optimal owing to a Non-Deterministic Polynomial (NP) hard type of technique that was used. The Group Search Optimization (GSO) based algorithm which was on the basis of a method of feature selection will find some optimal feature subsets through the elimination of all redundant features. For this work, the method of feature selection based on the GSO was applied to the sentiment classification. There was also a method of feature selection which was hybrid and based on the GSO and Local Beam Search (LBS) that has been proposed for a sentiment classification. The methods proposed were evaluated based on the product review dataset of Amazon. The results of the experiment proved that this method of a hybrid feature selection can outperform all other methods of feature selection for a sentiment classification.

Efficient hybrid group search optimizer for assembling printed circuit boards

AbstractAssembly optimization of printed circuit boards (PCBs) has received considerable research attention because of efforts to improve productivity. Researchers have simplified complexities associated with PCB assembly; however, they have overlooked hardware constraints, such as pick-and-place restrictions and simultaneous pickup restrictions. In this study, a hybrid group search optimizer (HGSO) was proposed. Assembly optimization of PCBs for a multihead placement machine is segmented into three problems: the (1) auto nozzle changer (ANC) assembly problem, (2) nozzle setup problem, and (3) component pick-and-place sequence problem. The proposed HGSO proportionally applies a modified group search optimizer (MGSO), random-key integer programming, and assigned number of nozzles to an ANC to solve the component picking problem and minimize the number of nozzle changes, and the place order is treated as a traveling salesman problem. Nearest neighbor search is used to generate an initial place order, which is then improved using a 2-opt method, where chaos local search and a population manager improve efficiency and population diversity to minimize total assembly time. To evaluate the performance of the proposed HGSO, real-time PCB data from a plant were examined and compared with data obtained by an onsite engineer and from other related studies. The results revealed that the proposed HGSO has the lowest total assembly time, and it can be widely employed in general multihead placement machines.

Deadline Constrained Task Scheduling Method Using a Combination of Center-Based Genetic Algorithm and Group Search Optimization

Abstract The present paper describes a hybrid group search optimization (GSO) and center-based genetic algorithm (CBGA)-based model for task scheduling in cloud computing. The proposed hybrid model combines the GSO, which has been successful in its application in task scheduling, with the use of the CBGA. The basic scheme of our approach is to utilize the benefits of both the GSO algorithm and CBGA excluding their disadvantages. In our work, we introduce the hybrid clouds, which are needed to determine which task to be outsourced and to what cloud provider. These choices ought to minimize the expense of running an allotment of the aggregate task on one or various public cloud providers while considering the application prerequisites, e.g. deadline constraints and data requirements. In the hybridization approach (HGSOCBGA), each dimension of a solution represents a task and the solution as a whole signifies all the task priorities. The vital issue is how to allocate the user tasks to exploit the profit of the infrastructure as a service (IaaS) provider while promising the quality of service (QoS). The generated solution proficiently assures the user-level QoS and improves the IaaS providers’ credibility and economic benefit. The HGSOCBGA method also designs the hybridization process and suitable fitness function of the corresponding task. According to the evolved results, it has been found that our algorithm always outperforms the traditional algorithms.

DEGSO: Hybrid Group Search Optimizer with Differential Evolution Operator

In standard group search optimizer (GSO) algorithm, scroungers will converge to the similar position if the producer cannot find a better position than the old one in a number of successive iterations and the group may suffer from the premature convergence. In this paper, a hybrid GSO with differential evolution (DE) operator named DEGSO is proposed to enhance the diversity of standard group search optimizer. In this method, the standard GSO algorithm and the DE operator alternate at the odd iterations and at the even iterations. The results of the experiments indicate that DEGSO is competitive to some other evolutionary computation (EA) algorithms.

Application of HGSO to security based optimal placement and parameter setting of UPFC

This paper presents a novel method to solve security based optimal placement and parameter setting of unified power flow controller (UPFC) problem based on hybrid group search optimization (HGSO) technique. Firstly, HGSO is introduced in order to solve mix-integer type problems. Afterwards, the proposed method is applied to the security based optimal placement and parameter setting of UPFC problem. The focus of the paper is to enhance the power system security through eliminating or minimizing the over loaded lines and the bus voltage limit violations under single line contingencies. Simulation studies are carried out on the IEEE 6-bus, IEEE 14-bus and IEEE 30-bus systems in order to verify the accuracy and robustness of the proposed method. The results indicate that by using the proposed method, the power system remains secure under single line contingencies.