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rider optimization algorithm Based Optimal Cloud Server Selection in E-Learning

Computer Systems Science & Engineering 2023年第2期44卷 1749-1762页

作者： R.Soundhara Raja Pandian C.Christopher Columbus Department of Computer Science and Engineering PSN College of Engineering and TechnologyMelathediyoorTamil Nadu627152India

Currently,e-learning is one of the most prevalent educational methods because of its need in today’s *** classrooms and web-based learning are becoming the new method of teaching *** students experience a lack of access to resources commonly the educational *** remote loca-tions,educational institutions face signiﬁcant challenges in accessing various web-based materials due to bandwidth and network infrastructure *** objective of this study is to demonstrate an optimization and queueing tech-nique for allocating optimal servers and slots for users to access cloud-based e-learning *** proposed method provides the optimization and queue-ing algorithm for multi-server and multi-city constraints and considers where to locate the best *** optimal server selection,the rider optimization Algo-rithm(ROA)is utilized.A performance analysis based on time,memory and delay was carried out for the proposed methodology in comparison with the exist-ing *** proposed rider optimization algorithm is compared to Par-ticle Swarm optimization(PSO),Genetic algorithm(GA)and Fireﬂy algorithm(FFA),the proposed method is more suitable and effective because the other three algorithms drop in local optima and are only suitable for small numbers of user *** the proposed method outweighs the conventional techniques by its enhanced performance over them.

关键词： optimization queuing slot selection server selection rider optimization algorithm

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A Discrete Multi-Objective rider optimization algorithm for Hybrid Flowshop Scheduling Problem Considering Makespan, Noise and Dust Pollution

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IEEE ACCESS 2020年 8卷 88527-88546页

作者： Fu, Yanming Li, Zhuohang Chen, Ningjiang Qu, Chiwen Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Peoples R China Hunan Normal Univ Coll Math & Stat Changsha 410081 Peoples R China Baise Univ Sch Informat Engn Baise 533000 Peoples R China

Many optimization algorithms have been proposed to solve hybrid flowshop scheduling problem (HFSP). However, with the development of industry and society, labor right and labor safety have become important problem to consider in production scheduling. So the green HFSP considering makespan, noise and dust pollution becomes an urgent problem to be solved. In this paper, the rider optimization algorithm (ROA) is modified into the multi-objective rider optimization algorithm (MOROA) using Pareto archive and neighborhood sorting techniques. The Pareto archive and neighborhood sorting technology make the Pareto optimal solution set of MOROA have higher coverage and more solutions. Then MOROA is discretized into discrete MOROA (DMOROA) to solve the HFSP considering makespan, noise and dust pollution. DMOROA is tested on 10, 30 and 50 jobs HFSP considering makespan, noise and dust pollution. The test results are compared with two multi-objective algorithms to verify the performance of DMOROA. And the test results verify that the DMOROA is superior to the comparison algorithms in search accuracy, number of non-dominated solutions, diversity of solution set and stability. Therefore, DMOROA is effective in solving multi-objective HFSP considering makespan, noise and dust pollution.

关键词： Hybrid flowshop scheduling problem multi-objective meta-heuristic algorithm rider optimization algorithm labor right

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A novel image compression model by adaptive vector quantization: modified rider optimization algorithm

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2020年第1期45卷 1-15页

作者： Chavan, Pratibha Pramod Rani, B. Sheela Murugan, M. Chavan, Pramod Sathyabama Inst Sci & Technol Rajiv Gandhi Salai Chennai 600119 Tamil Nadu India Trinity Coll Engn & Res Dept E&TC Engn Pune 411048 Maharashtra India SRM Valliammai Engn Coll ECE Dept Kattankulathur 603203 Tamil Nadu India KJ Coll Engn & Management Res Dept E&TC Engn Pune 411048 Maharashtra India

In recent days over the internet, the uploading of enormous new images is being made every day, and they necessitate large storage to accumulate the image data. For the earlier few decades, more analysts have evolved skillful image compression schemes to enhance the compression rates and the image quality. In this work, Vector Quantization is used, which uses theLinde-Buzo-Gray algorithm. As a novel intention, the codebooks are optimized by an improved optimization algorithm. In this approach, the database image is firstly separated into a set of blocks, i.e., pixels, and these sets of blocks are referred to as vectors. Then a suitable codeword is selected for each vector such that is the closest representation of that input vector. The encoder generates a codebook by mapping the vectors on the basis of these code words, and the compression of the vectors takes place. The encoder then sends a compressed stream of these vectors by pointing out their indices from the codebook to the decoder through a channel. The decoder then decodes the index to find out the compressed vector and places it on the image. For attaining a better image compression effect, the codebook is optimized using the Best Fitness Updated rider optimization algorithm. The optimization of codebooks is done so that the summation of the compression ratio and the error difference between the original and decompressed images has to be minimized. Moreover, the proposed model is scruntized with other existing algorithms, and the experimental outcomes are validated.

关键词： Image compression vector quantization Linde-Buzo-Gray codebook rider optimization algorithm fitness

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An Improved rider optimization algorithm for Solving Engineering optimization Problems

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IEEE ACCESS 2019年 7卷 80570-80576页

作者： Wang, Guohu Yuan, Yongliang Guo, Wenwen Zhengzhou Univ Ind Technol Sch Mechatron Engn Zhengzhou 451100 Henan Peoples R China Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

This paper proposed an improved optimization algorithm for engineering optimization problems. This improved optimization algorithm, namely, gravitational search strategy-assisted rider optimization algorithm (GSSROA), combines the gravitational search strategy (GSS) with the rider optimization algorithm (ROA). In the GSSROA, the search step can be adaptively adjusted in each iteration. The performance of the GSSROA has been verified with three benchmark engineering design problems. The results indicate that the proposed GSSROA has the feasibility and good robustness. Then, the GSSROA is applied to optimize the weight of the front axle of an automobile under the non-linear constraints. The results show that the GSSROA can achieve the optimal structure with a weight that is reduced by 16.82% compared with the initial design.

关键词： rider optimization algorithm gravitational search strategy engineering design problems parameter identification

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ExpSFROA-Based DRN: Exponential Sunflower rider optimization algorithm-Driven Deep Residual Network for the Intrusion Detection in IOT-Based Plant Disease Monitoring

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INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING 2023年第1期17卷 5-31页

作者： Deelip, Mali Shrikant Govinda, K. Vellore Inst Technol VIT Vellore Tamil Nadu India

Internet of Things (IoT) significantly gained attraction nowadays, as it assists in numerous purposes. Several computerized technologies are employed for monitoring the plant disease in the IoT paradigm, and the major challenging lies in detecting the intrusions while monitoring the plant disease. This paper proposes an Exponential Sun Flower rider optimization algorithm-driven Deep Residual Network (ExpSFROA-based DRN) for achieving effective intrusion detection results in the IoT. The proposed ExpSFROA is devised by incorporating Exponential Sun Flower optimization (Exponential-SFO) and rider optimization algorithm (ROA). Meanwhile, Exponential-SFO is designed by combining the Exponential Weighted Moving Average (EWMA) and Sunflower optimization (SFO) algorithm. Here, the information is collected from the simulated IoT nodes based on the Cluster Head (CH), and performs the process of routing in order to predict the leaf disease more effectively. Based on the disease prediction process, the intrusion detection process is achieved by the devised ExpSFROA-based DRN. The performance of the newly developed ExpSFROA-based DRN is evaluated using four metrics such as accuracy, throughput, energy, and True Positive Rate (TPR). The developed method attained better results than the existing methods, such as SecTrust-RPL+DRN, OSEAP+IBFO+DRN, LASeR++DRN with a maximum accuracy of 0.950, higher throughput of 7533350 bps, minimal energy of 49.74 J, and higher TPR of 0.956. The proposed method is used in the agricultural land, which helps the farmers for monitoring the conditions of the fields anywhere and anytime, thereby minimizing the manpower and time.

关键词： rider optimization algorithm Internet of Things deep residual network plant disease monitoring intrusion detection

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Prediction of rock mass rating using neural network with an improved rider optimization algorithm

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EVOLUTIONARY INTELLIGENCE 2022年第4期15卷 2567-2579页

作者： Chen, Wei Wan, Wen Peng, Wenqing Hunan Univ Sci & Technol Sch Resource Environm & Safety Engn Xiangtan 411201 Hunan Peoples R China

Evaluation of TBM performance is critical for the choice of TBM specifications and tunnel design. In the past decades, the hypothetical schemes depending on the rock fragmentation process and the experimental models up to field surveillance as well as machine performance are the two main methods. Traditional and conventional approaches for rock mass rate (RMR) prediction usually consider excessive parameters and the accuracies are far from actual values. A new RMR prediction model based on the optimized neural network (NN) is designed. To improve the prediction accuracy, this paper proposed a new self-adaptive rider optimization algorithm (SA-ROA), which applied optimization logic to train the NN by updating the weight as wave velocity (Vp), transverse wave velocity (Vs), Vp/Vs, statistics (Stat), orientation, magnitude, polarity, wave type, and metre. Finally, the RMR prediction analysis of the adopted NN-SA-ROA model is compared to the conventional and traditional classifiers with varied learning percentages: 50%, 60%, 70%, and 80% for three data sets, respectively. Subsequently, the performance of the proposed work is verified using other approaches based on error analysis. The predicted mean absolute errors (MAEs) and the mean absolute percentage errors (MAPEs) of SA-ROA are smaller than conventional and traditional schemes. The results show that the proposed method can successfully predict the actual RMR.

关键词： Tunneling scheme RMR prediction Neural network rider optimization algorithm

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Secure-Aware Multipath Routing Using Atom Search rider optimization algorithm in Wireless Sensor Networks

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INTERNATIONAL JOURNAL OF MOBILE COMPUTING AND MULTIMEDIA COMMUNICATIONS 2021年第2期12卷 36-55页

作者： Shyjith, M. B. Maheswaran, C. P. Reshma, V. K. Noorul Islam Ctr Higher Educ Dept CSE Kumaracoil India Jawaharlal Coll Engn & Technol Dept CSE Palakkad India

WSN is comprised of sensor nodes that sense the data for various applications. The nodes are employed for transmitting sensed data to BS through intermediate nodes or the cluster heads in multi-hop environment. Erroneous selection of CHs may lead to large energy consumption and thereby degrades system performance. Hence, an effective technique was developed by proposing rider-ASO for secure-aware multipath routing in the WSN. The proposed routing protocol offers security to the network concerning various trust factors. Initially, cluster head selection is done using RCSO. Then, the trust values of the cluster heads that are selected is computed to ensure security while routing. For the multipath routing, proposed rider-ASO is developed by combining ASO and ROA. Thus, the proposed algorithm finds multiple secured paths from the source into destination based on selected CHs. The developed rider-ASO outperformed other methods with minimal delay of 0.009 sec, maximal average residual energy 0.5494 J, maximal PDR of 97.82%, maximal throughput rate of 96.07%, respectively.

关键词： Atom Search optimization Energy Multi-Path Secure Routing rider optimization algorithm Wireless Sensor Network

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Hybrid Arithmetic-rider optimization algorithm as new intelligent model for travelling salesman problem

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International Journal of Information Technology (Singapore) 2024年 1-18页

作者： Krishna, Madugula Murali Majhi, Santosh Kumar Panda, Nibedan Department of Computer Science and Engineering Veer Surendra Sai University of Technology Odisha Burla 768018 India Department of Computer Science and Information Technology Guru Ghasidas Viswavidyalaya C.G. Bilaspur India School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar 751024 India

Travelling Salesman Problem (TSP) is one of the significant NP-hard benchmark problems for performing discrete optimization. In recent times, determining the optimal route mechanism has been implemented and ensured as an important mechanism for solving practical applications. In real-time applications, energy-saving, reaching the destination at the assigned time, and identifying the shortest route is very essential. Route determination is researched by different scientists and engineers. Numerous research is done on this problem, and it is still challenging. Therefore, meta-heuristics are involved in this problem as it is inspired by the biological species. This article constitutes the continuation of the work on adapting the TSP using the hybridized algorithm named Arithmetic-ROA (A-ROA). The ultimate aim of the proposed TSP is to reduce the distance travelled by the salesman while focusing on the entire city. To attain this optimal solution, the novel hybrid A-ROA optimizes the number of the city to be travelled. The results acquired by the new hybrid heuristic are compared with other heuristic algorithms. The computational results confirm that the developed algorithm obtained better results when compared with existing algorithms. The developed optimization algorithm also provides enhanced performance within a realistic amount of computational time. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Arithmetic optimization algorithm Arithmetic-rider optimization algorithm Benchmark Datasets Meta-heuristic algorithms rider optimization algorithm Travelling Salesman Problem

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Privacy preservation of data using modified rider optimization algorithm: Optimal data sanitization and restoration model

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EXPERT SYSTEMS 2021年第3期38卷 e12663-e12663页

作者： Shivashankar, Mohana Mary, Sahaaya Arul Saranathan Coll Engn Dept Comp Sci & Engn Trichy India

Data preservation is the mechanism of protecting and safeguarding the confidentiality and integrity of data. Data stored in huge databases may contain metadata, elements that may be imprecise and unstable, It may include sensitive data, personal profiles and so on, which is vulnerable to third parties such as hackers or attackers. They may misuse the data and as a consequence of this the confidentiality and privacy of the data gets lost. There is a need to conserve the data and make it available for reuse when needed. Hence, it needs a proficient method to maintain and protect individuals' data privacy regarding confidentiality and reliability. This paper intends to develop an advanced model for privacy preservation of huge data with the accomplishment of two stages, namely data sanitization and data restoration. Data sanitization process preserves the safety of sensitive data stored in huge databases, by means of hiding those sensitive data from unauthorized users. Data restoration is the process of recovering or restoring of data that is sanitized at the sender side. Concerning the secrecy, there is a need for an optimal key to hide the sensitive data at sender as well as receiver side. Subsequent to the data sanitization, it requires the same key to restore the sanitized data. Thus, the optimal key generation plays a vital role to maintain privacy preservation. In order to choose an optimal key, a modified rider optimization algorithm (ROA) named as Randomized ROA (RROA) model is implemented in this work. Furthermore, the efficiency of the proposed work is compared over the state-of-the-arts models by concerning the sanitization as well as restoration efficiency.

关键词： data restoration data sanitization optimal key generation privacy preservation rider optimization algorithm

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A modified rider optimization algorithm for multihop routing inWSN

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2020年第6期33卷 e2764-e2764页

作者： Augustine, Susan Ananth, J. P. Sri Krishna Coll Engn & Technol Coimbatore 641008 Tamil Nadu India

Wireless Sensor Networks (WSNs) have an important role in establishing the communication between the Internet of things (IoT) devices. Routing is an important research field in the WSN as it helps in finding the suitable paths for the communication. This paper proposes a multipath routing algorithm based on the optimization approach. The proposed routing algorithm has two important phases, namely, Cluster head selection and multipath routing. Initially, the cluster head selection is carried out by the kernel based Fuzzy C Means (kernel FCM) algorithm. Then, the multipath routing is established by the newly developed rider Salp Swarm optimization algorithm (RSSA), which is the integration of the rider optimization algorithm (ROA) and Salp Swarm algorithm (SSA). The fitness function of the proposed RSSA is designed by considering the several factors, such as energy, QoS and trust. The simulation of the proposed work is carried out using different WSN setups, and the results are compared with several comparative techniques. From the results, it can be summarized that the proposed RSSA based multipath routing has better performance with values of 0.2526, 0.0764, 0.5, and 26 for delay, energy, throughput and number of alive nodes, respectively.

关键词： cluster head selection kernel based FCM algorithm multipath routing rider optimization algorithm wireless sensor networks

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