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A lion optimization algorithm for an integrating maintenance planning and production scheduling problem with a total absolute deviation of completion times objective

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SOFT COMPUTING 2022年第24期26卷 13953-13968页

作者： Yazdani, Reza Alipour-Vaezi, Mohammad Kabirifar, Kamyar Kojour, Azim Salahi Soleimani, Faezeh Tech & Vocat Univ TVU Dept Accounting Tehran Iran Univ New South Wales Sch Built Environm Sydney NSW 2052 Australia Univ Tehran Coll Engn Sch Ind & Syst Engn Tehran Iran Islamic Azad Univ Dept Business Management Chalous Branch Chalous Iran Ball State Univ Dept Math Sci Muncie IN 47306 USA

In today's competitive business environment, the need for continuous production, quality improvement, and fast delivery necessitates highly reliable production and delivery processes. A more reliable system can be ensured by performing routine maintenance on the equipment. Maintenance, on the other hand, causes a temporary reduction in production capacity. To ensure a high level of system performance, it is essential to coordinate maintenance and production. The study integrates maintenance and production decisions in order to maximise efficiency by ensuring high-quality output and efficient resource utilisation;however, limited studies have been carried out addressing this type of scheduling problem with the objective function of total absolute deviation of completion times (TADC). Thus, this study aims to investigate the scheduling problem on a parallel machine under periodic maintenance in order to minimise the TADC of the jobs. Due to the complexity of the problem, a metaheuristic method called the lion optimization algorithm (LOA) is presented to solve the problem. This study performs a comprehensive comparative analysis to demonstrate the proposed algorithm's reliability. The dragonfly algorithm, the grasshopper optimization algorithm, the multi-verse optimization algorithm, the sine cosine algorithm, the salp swarm algorithm, and the whale optimization algorithm are presented and their performance is compared to the LOA in this study The results demonstrate that the LOA consistently outperforms the other presented algorithms across all size ranges. The study's findings contribute new theoretical and practical insights to the growing body of knowledge about manufacturing environments and have implications for planners and managers, particularly in businesses where unplanned production wastes financial resources.

关键词： lion optimization algorithm Scheduling Maintenance activity Total absolute deviation completion times (TADC)

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Automatic crack detection in the pavement with lion optimization algorithm using deep learning techniques

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年 1-10页

作者： Vinodhini, Kanchi Anantharaman Sidhaarth, Kovilvenni Ramachandran Aswin Veltech Rangarajan Dr Sagunthala R&D Inst Sci & Te Dept Civil Engn Veltech Rangarajan Dr Chennai India

Cracks in the pavement first appear in the outer layer, visible through the eyes;they deteriorate on a deeper layer, affecting the entire construction. Nowadays, technical advancement plays a vital role in the detection of cracks. Artificial intelligence and machine learning algorithms are used for pixel-level classification in computer vision and image processing. The detection of cracks in the pavement is a crucial task, and it requires an ample amount of time. In the proposed crack detection model, the feature extraction and feature selection are based on the lion optimization algorithm (LOA), which extracts the best features for the selected area of the bounding box. The parts are removed with the lion's optimal position and velocity, which can also be used for the best feature selection. The convolutional neural network trains and tests the model with transfer learning methods. The proposed LOA-based crack detection algorithm outperforms the existing crack detection algorithms with a precision of 94.7 percent, a recall of 95.22 percent, and an F1 score of 94.89 percent.

关键词： Convolutional neural networks lion optimization algorithm Transfer learning Crack detection Pavements

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LOA-RPL:Novel Energy-Efficient Routing Protocol for the Internet of Things Using lion optimization algorithm to Maximize Network Lifetime

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Computers, Materials & Continua 2021年第10期69卷 351-371页

作者： Sankar Sennan Somula Ramasubbareddy Anand Nayyar Yunyoung Nam Mohamed Abouhawwash Department of Computer Science Engineering Sona College of TechnologySalem636005India VNR Vignana Jyothi Institute of Engineering&Technology Hyderabad500090India Graduate School Duy Tan UniversityDa NangVietnam550000Vietnam Faculty of Information Technology Duy Tan UniversityDa NangVietnam550000Vietnam Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMI48824USA

Energy conservation is a significant task in the Internet of Things(IoT)because IoT involves highly resource-constrained *** is an effective technique for saving energy by reducing duplicate *** a clustering protocol,the selection of a cluster head(CH)plays a key role in prolonging the lifetime of a ***,most cluster-based protocols,including routing protocols for low-power and lossy networks(RPLs),have used fuzzy logic and probabilistic approaches to select the CH ***,early battery depletion is produced near the *** overcome this issue,a lion optimization algorithm(LOA)for selecting CH in RPL is proposed in this ***-RPL comprises three processes:cluster formation,CH selection,and route establishment.A cluster is formed using the Euclidean *** selection is performed using *** establishment is implemented using residual energy *** extensive simulation is conducted in the network simulator ns-3 on various parameters,such as network lifetime,power consumption,packet delivery ratio(PDR),and *** performance of LOA-RPL is also compared with those of RPL,fuzzy rule-based energyefficient clustering and immune-inspired routing(FEEC-IIR),and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm(RISARPL).The performance evaluation metrics used in this study are network lifetime,power consumption,PDR,and *** proposed LOARPL increases network lifetime by 20%and PDR by 5%–10%compared with RPL,FEEC-IIR,and ***-RPL is also highly energy-efficient compared with other similar routing protocols.

关键词： Internet of things cluster head clustering protocol optimization algorithm lion optimization algorithm network lifetime routing protocol wireless sensor networks energy consumption low-power and lossy networks

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Self-Adaptive Hybridized lion optimization algorithm With Transfer Learning for Ancient Tamil Character Recognition in Stone Inscriptions

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IEEE ACCESS 2023年 11卷 39621-39634页

作者： Priya, R. Devi Karthikeyan, S. Jaganathan, Indra Rameshbabu, Kiruba Shankar Abraham, Ajith Gabralla, Lubna A. Sivaraj, R. Nandhagopal, S. M. KPR Inst Engn & Technol Coimbatore 641407 India Flame Univ Fac Comp & Data Sci Pune 412115 India Princess Nourah bint Abdulrahman Univ Appl Coll Riyadh 11671 Saudi Arabia Nandha Engn Coll Erode India HKBK Coll Engn Bengaluru 560045 India

Tamil character recognition serves as a vital research problem in pattern recognition since there are many serious technical difficulties due to similarity and complexity of characters when compared with other languages. Stone inscriptions reveal details of luxury, lifestyle, economic status, cultural practices, administrative tasks followed by various rulers and dynasties of Tamil Nadu. Since ancient stone inscriptions are in existence for a longer period, there are possibilities of natural erosion and no early protection measures are available. The ancient stone inscriptions are always not complete which creates many difficulties in reading and understanding them and their aesthetic appreciation. There is a difficulty in recognizing Tamil characters mainly because of the characters with a number of holes, loops and curves. The number of letters in Tamil language is higher when compared to other languages. Even though there are various approaches provided by the researchers, challenges and issues still prevail in recognition of tamil text in stone inscriptions. In the existing systems, detection algorithms fail to produce desired accuracy and hence stone inscription recognition using transfer learning, a promising method is proposed here. lion optimization algorithm (LOA) is applied to optimize brightness and contrast and then stone inscription images are pre-processed for noise removal and then each character is separated by identifying contours. Characters are recognized using Transfer Learning (TL), a Deep Convolution Neural Network-based multi classification approach. The proposed hybrid model Self-Adaptive lion optimization algorithm with Transfer Learning (SLOA-TL) when implemented in images of stone inscriptions achieves better accuracy and speed than other existing methods. It serves as an efficient design for recognition of tamil characters in stone inscriptions and preserving tamil traditional knowledge.

关键词： Character recognition optimization Feature extraction Transfer learning Convolutional neural networks Optical character recognition Classification algorithms lion optimization algorithm preprocessing stone inscription transfer learning Tamil character recognition

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Multi-Objective optimization for Dynamic Resource Provisioning in a Multi-Cloud Environment using lion optimization algorithm 20

Multi-Objective Optimization for Dynamic Resource Provisioni...

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20th IEEE International Symposium on Computational Intelligence and Informatics (CINTI)

作者： Chaitra, T. Agrawal, Shivani Jijo, Jeny Arya, Arti PESIT Bangalore South Campus Dept ISE Bangalore Karnataka India PES Univ Dept CSE Bangalore Karnataka India

ISBN: (纸本)9781728183398

Cloud computing offers surfeit of services like storage, computing power, run-time environment, network etc. that everyone is accustomed to use it in day-to-day lives. In cloud computing, resources need to be dynamically provisioned on a metered basis. Quality of Services(QoS) is promised like performance, scalability, efficiency, fault tolerance, availability, reliability, throughput, and so on. Several meta-heuristic natureinspired optimization algorithms like Particle Swarm optimization(PSO), Ant Colony optimization(ACO) etc. deployed to meet Service Level Agreement parameters like minimum downtime and low latency, but still have challenges in dynamically allocating resources. To overcome the above stated challenges, a new dynamic resource provisioning technique in a multicloud environment is proposed that uses lion optimization algorithm(LOA) wherein characteristics of nomad and pride lion groups are taken into account. The multi-cloud environment provides the organization or customer to choose a provider that meets the specific requirements. As compared to PSO, this approach achieved better results while optimizing multiple objectives like completion time, average response time, makespan, cost, and average resource utilization. This study proves that the completion time and cost for LOA has outperformed when compared to PSO for a given number of tasks. The makespan and average response time for LOA improves slowly with more number of tasks as compared to PSO.

关键词： Cloud Computing Resource Provisioning Nature-inspired algorithm lion optimization algorithm CloudSim Multi-cloud environment multi-objective optimization

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Transmission Congestion Relief with Integration of Photovoltaic Power Using lion optimization algorithm 7th

Transmission Congestion Relief with Integration of Photovolt...

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International Conference on Soft Computing for Problem Solving (SocProS)

作者： Gope, Sadhan Dawn, Subhojit Mitra, Rituparna Goswami, Arup Kr Tiwari, Prashant Kr Mizoram Univ Elect Engn Dept Aizawl Mizoram India Siliguri Inst Technol Elect Engn Dept Siliguri W Bengal India Natl Inst Technol Silchar Elect Engn Dept Silchar Assam India

ISBN: (纸本)9789811315923;9789811315916

Transmission congestion is a vital problem in a deregulated power system. This paper proposes a novel transmission congestion management approach considering photovoltaic (PV) power using lion optimization algorithm (LOA). The main contributions of this paper have twofolds. Initially, the values of bus sensitivity factor (BSF) and generator sensitivity factor (GSF) are, respectively, used to select the optimal bus to integrate PV power and to select the participating generators for congestion management. Finally, LOA is used to determine the active power rescheduling amount and congestion cost. Test results on modified 39 bus New England system indicate that the LOA approach could provide a less active power rescheduling amount and congestion cost with integration of PV power compared to particle swarm optimization (PSO) and ant lion optimizer (ALO) algorithm.

关键词： Photovoltaic power Bus sensitivity factor Generator sensitivity factor lion optimization algorithm

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An Optimized Routing Scheme in SDN-Based IoT Using the lion optimization algorithm

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IETE JOURNAL OF RESEARCH 2024年第8期70卷 6745-6756页

作者： Jayaprakash, M. Priya, R. Devi RMK Engn Coll Kavaraipettai Tamil Nadu India KPR Inst Engn & Technol Coimbatore Tamil Nadu India

The Internet of Things (IoT) speaks to the current and future state of the Internet. Numerous things (objects) related to the Internet generate a large amount of information that requires a ton of labor and prepare tasks to move to valuable data. In addition, for the association and control of this enormous information, the original ideas in the project and the executives of the IoT system need to expedite and improve its presentation. A software-defined networking (SDN) system is another worldview that has recently emerged to cover all the complexities in the traditional system architecture by summarizing all the controls and management functions from the basic devices (things in IoT). The open-flow protocol is used to make messages between switches and controllers. It is a correspondence conference, through which the controller integrates the path of the network pocket with switches. Therefore, data packets should be directed to the optimum path to improve system operation. The best or optimal path for packet routing in the SDN data plane is found using the lion optimization algorithm (LOA), which is proposed in this study. Response times and packet loss rates can both be decreased by choosing the best route between the hosts. The suggested LOA outperforms current optimal path detection methods in terms of performance, delay, packet delivery rate, performance, and pocket loss rate, according to simulation findings.

关键词： IoT lion optimization algorithm Protocol Routing and optimal path SDN Switch

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Paddy insect identification using deep features with lion optimization algorithm

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HELIYON 2024年第12期10卷 e32400页

作者： Elmagzoub, M. A. Rahman, Wahidur Roksana, Kaniz Islam, Md. Tarequl Sadi, A. H. M. Saifullah Rahman, Mohammad Motiur Rajab, Adel Rajab, Khairan Shaikh, Asadullah Najran Univ Coll Comp Sci & Informat Syst Dept Network & Commun Engn Najran 61441 Saudi Arabia Uttara Univ Dept Comp Sci & Engn Dhaka 1206 Bangladesh Khwaja Yunus Ali Univ Dept Comp Sci & Engn Sirajganj 6751 Bangladesh Mawlana Bhashani Sci & Technol Dept Comp Sci & Engn Tangail 1902 Bangladesh Najran Univ Coll Comp Sci & Informat Syst Dept Comp Sci Najran 61441 Saudi Arabia Najran Univ Coll Comp Sci & Informat Syst Dept Informat Syst Najran 61441 Saudi Arabia

Pests are a significant challenge in paddy cultivation, resulting in a global loss of approximately 20 % of rice yield. Early detection of paddy insects can help to save these potential losses. Several ways have been suggested for identifying and categorizing insects in paddy fields, employing a range of advanced, noninvasive, and portable technologies. However, none of these systems have successfully incorporated feature optimization techniques with Deep Learning and Machine Learning. Hence, the current research provided a framework utilizing these techniques to detect and categorize images of paddy insects promptly. Initially, the suggested research will gather the image dataset and categorize it into two groups: one without paddy insects and the other with paddy insects. Furthermore, various pre-processing techniques, such as augmentation and image filtering, will be applied to enhance the quality of the dataset and eliminate any unwanted noise. To determine and analyze the deep characteristics of an image, the suggested architecture will incorporate 5 pre-trained Convolutional Neural Network models. Following that, feature selection techniques, including Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and an optimization algorithm called lion optimization, were utilized in order to further reduce the redundant number of features that were collected for the study. Subsequently, the process of identifying the paddy insects will be carried out by employing 7 ML algorithms. Finally, a set of experimental data analysis has been conducted to achieve the objectives, and the proposed approach demonstrates that the extracted feature vectors of ResNet50 with Logistic Regression and PCA have achieved the highest accuracy, precisely 99.28 %. However, the present idea will significantly impact how paddy insects are diagnosed in the field.

关键词： Convolutional neural network lion optimization algorithm Principal component analysis Linear discriminant analysis Deep learning Machine learning Pest identification

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An evolutionary lion optimization algorithm-based image compression technique for biomedical applications

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EXPERT SYSTEMS 2021年第1期38卷 e12508-e12508页

作者： Geetha, Karuppaiah Anitha, Veerasamy Elhoseny, Mohamed Kathiresan, Shankar Shamsolmoali, Pourya Selim, Mahmoud M. MIET Engn Coll Dept Comp Sci & Engn Tiruchirappalli Tamil Nadu India Anna Univ Imayam Coll Engn Dept Comp Sci & Engn Chennai Tamil Nadu India Mansoura Univ Fac Comp & Informat Mansoura Egypt Sci Grp Res & Technol Cairo Egypt Alagappa Univ Dept Comp Applicat Karaikkudi Tamil Nadu India Huawei Technol Corp Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China Prince Sattam Bin Abdulaziz Univ Al Aflaj Coll Sci & Human Studies Dept Math Al Kharj Saudi Arabia

Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde-Buzo-Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2-LBG method utilizing the lion optimization algorithm (LOA) and Lempel Ziv Markov chain algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search-based LBG (CS-LBG), Firefly-based LBG (FF-LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2-LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS-LBG, FA-LBG, and JPEG2000 methods. The experimental values revealed that the L2-LBG process yielded effective compression performance with a better-quality reconstructed image.

关键词： evolutionary algorithms lion optimization algorithm medical data LZMA vector quantization biomedical applications

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A lion-Whale optimization-based migration of virtual machines for data centers in cloud computing

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2018年第8期31卷 e3539-e3539页

作者： Krishna, J. Venkata Naidu, G. Apparao Upadhayaya, Niraj Sree Vahini Inst Sci & Technol Dept CSE Tiruvuru AP India JB Inst Engn & Technol Hyderabad Telangana India

The scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service-level agreement violation. In this paper, a novel VMM algorithm based on lion-Whale optimization is developed by integrating the lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the lion-Whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization-based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed lion-Whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09.

关键词： cloud computing lion optimization algorithm virtual machine migration Whale optimization algorithm

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