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lion algorithm with Levy Update: Load frequency controlling scheme for two-area interconnected multi-source power system

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2019年第14期41卷 4084-4099页

作者： Sharma, Deepesh Yadav, Naresh Kumar DCR Univ Sci & Technol Murthal 131039 Sonepat India DeenbandhuChhotu Ram Univ Sci & Technol Murthal Haryana India

In an interconnected multi-area power system, Load Frequency Control (LFC) is a main challenging problem. This paper presents the Fractional Order Proportional Integral (FOPI) controller for an interconnected two-area power system, wherein each area has multi-source power systems. The gains of the proposed controller are being optimized by lion algorithm (LA), utilizing an integral square error (ISE) criterion, to develop the proposed lion with Levy Update-based FOPI controller (LLUFOPI). The proposed LA schedules the gain of the LLUFOPI controller by achieving the least possible error. Hence, the LLUFOPI controller assures better LFC in the two-area interconnected power system. The performance of the proposed controller is assessed by considering the practical constraints in power system such as Generation Rate Constraints (GRC), communication delay, AC/DC link, step load variation and Capacitive Energy Storage (CES) device. Finally, the simulation results show that the LLUFOPI controller provides a well- optimized gain that is 89% higher than the other algorithms with better stability. The Integral Square Error (ISE) value of the proposed controller is 81.1% lesser than the other algorithms. Better LFC in the two-area multi-source-interconnected power system is hence achieved with minimum ISE.

关键词： Load frequency control lion algorithm LLU fractional order proportional integral multi-source gain scheduling

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lionRank: lion algorithm-based metasearch engines for re-ranking of webpages

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Science China(Information Sciences) 2018年第12期61卷 65-80页

作者： P.VIJAYA Satish CHANDER Waljat College of Applied Sciences

Due to the rapid growth of the web, the process of collecting the relevant web pages based on the user query is one of the major challenging tasks in recent days. Hence, it is very complicated for the users to know the most relevant information even though various search engines are widely employed. To deal with users' trouble in identifying the relevant information from the web, we have proposed a meta-lion search engine to capture and analyze the ranking scores of various search engines and thereby, generate the re-ranked score results. Accordingly, lionRank, a lion algorithm-based meta-search engine is proposed for the re-ranking of the web pages. Here, different features like text based, factor based, rank based and classifier based features are used by the underlying search engines. In classifier based feature extraction,we have used the fuzzy integrated extended nearest neighbor(FENN) classifier to include the semantics in feature extraction. Moreover, an intelligent re-ranking process is proposed based on the lion algorithm to fuse the features scores optimally. Finally, the results of the proposed lionRank is analyzed with the web page database collected through four benchmark queries, and the quantitative performance are analyzed using precision, recall, and F-score. From the results, we proved that the proposed lionRank obtained the maximum F-score of 81% as compared with that of existing search engines like QuadRank, Outrank, Google,Yahoo, and Bing.

关键词： web technology meta-search engine feature extraction lion algorithm FENN classifier

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Combination of Fitness-Mated lion algorithm with Neural Network for Optimal Query Ordering Data Aggregation Model in WSN

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WIRELESS PERSONAL COMMUNICATIONS 2021年第1期116卷 513-538页

作者： Sarode, Prachi Reshmi, T. R. Pattabiraman, Venkatasubbu Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India Soc Elect Transact & Secur Chennai Tamil Nadu India

Query processing can be briefly defined as a database that comprises of an organized collection of data for one or more users either in digital form or in analog form such that it can portray exactly. A Wireless Sensor Network (WSN) is a specialized network of minimum cost, and power sensor nodes that can be described as the ability of performing some processing, gathering sensory information and communicating with each other. Query ordering with data aggregation is the process of scheduling of the nodes to receive the useful data from sensors. Data aggregation is considered as one of the fundamental processing procedures for saving the energy. In WSN data aggregation is an effective way to save the limited resources. This paper proposes a novel query-based data aggregation model with the aid of intelligent techniques. The framing of the query order takes place and the frames are ranked on the basis of a multi-objective function. The newly developed multi-objective function includes Latency, Throughput, and Data freshness. Initially, the solution corresponding to query order is trained in NN using the proposed Fitness-Mated lion algorithm (FM-LA). The optimally generated query order from NN is further given for second-level solution generation, which is again applied to FM-LA for subsequent query order optimization. Hence the two-stage optimization process with NN for query ordering is compared over the conventional methods in terms of performance measures like Latency, throughput, and data freshness. Hence, substantiated performance and comparative analysis validate the improved performance of the proposed model.

关键词： Wireless sensor network Data aggregation Query processing Query ordering Data freshness lion algorithm

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Sustainable Investment Forecasting of Power Grids Based on theDeep Restricted Boltzmann Machine Optimized by the lion algorithm

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Computer Modeling in Engineering & Sciences 2022年第1期130卷 269-286页

作者： Qian Wang Xiaolong Yang Di Pu Yingying Fan School of Economics andManagement Jilin Institute of Chemical TechnologyJilin132022China School of Economics andManagement Northeast Electric Power UniversityJilin132012China Digital Grid Research Institute China Southern Power GridGuangzhou510000China School of Art Hebei GEO UniversityShijiazhuang050031China

This paper proposes a new power grid investment prediction model based on the deep restricted Boltzmann machine(DRBM)optimized by the lion algorithm(LA).Firstly,two factors including transmission and distribution price reform(TDPR)and 5G station construction were comprehensively incorporated into the consideration of influencing factors,and the fuzzy threshold method was used to screen out critical influencing ***,the LA was used to optimize the parameters of the DRBM model to improve the model’s prediction accuracy,and the model was trained with the selected influencing factors and ***,the LA-DRBM model was used to predict the investment of a power grid enterprise,and the final prediction result was obtained by modifying the initial result with the modifying *** LA-DRBMmodel compensates for the deficiency of the singlemodel,and greatly improves the investment prediction accuracy of the power *** this study,a power grid enterprise was taken as an example to carry out an empirical analysis to prove the validity of the model,and a comparison with the RBM,support vector machine(SVM),back propagation neural network(BPNN),and regression model was conducted to verify the superiority of the *** conclusion indicates that the proposed model has a strong generalization ability and good robustness,is able to abstract the combination of low-level features into high-level features,and can improve the efficiency of the model’s calculations for investment prediction of power grid enterprises.

关键词： lion algorithm deep restricted boltzmann machine fuzzy threshold method power grid investment forecasting

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Optimal resource allocation in 5G system using modified lion algorithm

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IET COMMUNICATIONS 2021年第1期15卷 126-141页

作者： Kanaga Suba Raja, Subramanian Bamila Virgin Louis, Augusteen Easwari Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India St Xaviers Catholic Coll Engn Dept Comp Sci & Engn Nagercoil India

This research work intends to examine the resource allocation issues within the hybrid multi-carrier non-orthogonal multiple access (MC-NOMA) systems, which includes the NOMA and orthogonal multiple access (OMA) modes. This is exploited to attain the energy efficiency (EE) and spectral efficiency (SE) tradeoff with the minimum rate requirements of users. The entire degree of freedom that is incorporated within the resource allocation comprised user clustering, power allocation, choice of multiple access (MA) modes, and subcarrier assignment as well. This work mainly focuses on the resource allocation issue having every minimum rate requirement of users for the SE-EE tradeoff in hybrid MC-NOMA systems. Moreover, this work aims to make this possible with the incorporation of the optimisation aspect since it is the most significant way of solving multi-objective problems. Here, the resource allocation along with power allocation seems to be the crucial fact that is modelled or designed as the single-objective problem. Thereby, the subcarriers, and powers are optimally allocated by means of a new algorithm named lion algorithm with Probabilistic Mating, which is the modification of the lion algorithm. Finally, the performance of the proposed work is compared over other state-of-the-art methods in terms of cost analysis.

关键词： lion algorithm resource allocation hybrid MC-NOMA systems multiobjective problems optimisation OFDM modulation resource allocation issue resource allocation comprised user clustering optimal resource allocation hybrid multicarrier nonorthogonal multiple access systems multiple access modes energy conservation SE-EE tradeoff power allocation multi-access systems

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Mutation probability-based lion algorithm for design and optimization of microstrip patch antenna

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EVOLUTIONARY INTELLIGENCE 2020年第3期13卷 331-344页

作者： Guttula, Ramakrishna Nandanavanam, Venkateswara Rao Acharya Nagarjuna Univ Guntur 522510 Andhra Pradesh India Bapatla Engn Coll Bapatla India

The advantages and performance of microstrip patch antennas (MPA) namely, reduced weight, reduced profile, and reduced cost formulate them the ideal choice for communication networks. However, the difficulties in structure size and design remain a major concern. Hence, this paper aims to propose a new model that derives a nonlinear objective model for helping in the design of solution space of antenna parameters. To attain this, it is planned to incorporate the optimization concept, thereby a new mutation probability based lion algorithm (MP-LA), which is proposed for the tuning MPA constraints. The main objective model of the antenna design is to maximize the gain by optimizing the patch length, width, thickness of substrate, and value of dielectric substrate. After executing the simulation model, this paper compares the performance of proposed MP-LA-based antenna design with numerous conventional approaches namely, antenna design without optimization, artificial bee colony-based AD, genetic-based AD, firefly-based AD, part icle swarm optimization-based AD and grey wolf optimization-based AD, proposed GWO-based AD and lion optimization algorithm-based AD. Moreover, the analysis is done with respect to radiation pattern, E-plane, and H-plane of proposed and conventional antenna designs. Other performance analysis on characteristics impedance, directivity, efficiency and gain of proposed and conventional models is done.

关键词： Microstrip patch antenna Parameter optimization Gain Directivity lion algorithm Mutation probability

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Efficient implementation for classifying and segmenting of computed tomography brain tumour images using modified region growing with lion algorithm

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INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2017年第2-3-4期23卷 159-179页

作者： Ramakrishnan, Thiagarajan Sankaragomathi, Balasubramanian Natl Engn Coll Dept Elect & Instrumentat Engn Kovilpatti 628503 Tamil Nadu India

The study of Computed Tomography (CT) images considered in image segmentation is very important as it plays a vital part in identifying the different kinds of tumour. The classification of the tumour and the non-tumour images is followed by the segmentation of tumour region in CT images which are done by the proposed methodology. The process of classifying is done by Support Vector Machine (SVM) with linear kernel as well as Sequential Minimal Optimisation (SMO). After the classification process, segmentation is performed by the Modified Region Growing (MRG) with a threshold optimisation using lion algorithm (LA). In comparison, the sensitivity value of the proposed approach is appreciably higher as 91.52% than that of the existing method of MRG-Grey Wolf Optimisation (MRG-GWO). The comparative analysis in terms of sensitivity, specificity and accuracy is done for the proposed as well as for the existing techniques.

关键词： feature extraction support vector machine SMO sequential minimal optimisation quadratic programming least squares classification modified region growing threshold optimisation lion algorithm segmentation

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Optimization using lion algorithm: a biological inspiration from lion's social behavior

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EVOLUTIONARY INTELLIGENCE 2018年第1-2期11卷 31-52页

作者： Boothalingam, Rajakumar Resbee Info Technol Nagercoil Tamil Nadu India

Nature-inspired optimization algorithms, especially evolutionary computation-based and swarm intelligence-based algorithms are being used to solve a variety of optimization problems. Motivated by the obligation of having optimization algorithms, a novel optimization algorithm based on a lion's unique social behavior had been presented in our previous work. Territorial defense and territorial takeover were the two most popular lion's social behaviors. This paper takes the algorithm forward on rigorous and diverse performance tests to demonstrate the versatility of the algorithm. Four different test suites are presented in this paper. The first two test suites are benchmark optimization problems. The first suite had comparison with published results of evolutionary and few renowned optimization algorithms, while the second suite leads to a comparative study with state-of-the-art optimization algorithms. The test suite 3 takes the large-scale optimization problems, whereas test suite 4 considers benchmark engineering problems. The performance statistics demonstrate that the lion algorithm is equivalent to certain optimization algorithms, while outperforming majority of the optimization algorithms. The results also demonstrate the trade-off maintainability of the lion algorithm over the traditional algorithms.

关键词： lion algorithm Optimization Bio-inspired Large-scale Crossover Mutation

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Resource Allocation Model and Power Control in Macrocell-Femtocell Networks by lion algorithm 6

Resource Allocation Model and Power Control in Macrocell-Fem...

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6th International Conference for Convergence in Technology (I2CT)

作者： Mohite, Shailaja Sanjay Kolekar, Uttam DJSCOE Elect & Telecommun Dept Mumbai Maharashtra India APSIT Thana India

ISBN: (纸本)9781728188768

In the macro-femto HetNets, FBSs and its deployment are done by users unknowning to Macroocell BS, thus, the intercell interference defines the power allocation, subcarrier assignment or allocation and price allocation as well. This paper scrutinize crosstier interference mitigation in FDOFDMA based HetNets consisting of macrocell-femtocells. In this paper three major contributions will be subjected: defining a single objective problem that includes three constraints like power allocation, subcarrier allocation and price allocation of Macro-Femto Networks, as the work concentrates on the optimization logics using lion algorithm which based on lions social behavior like territorial defence&territorial takeover. The solution encoding process seems to be the most important aspect which introduce a new encoding transformation model of all the three allocation strategies, introducing lion algorithm LA algorithm are required to solve the above mentioned optimization problem in Macrocell-Femtocell Networks. The performance of algorithm is the trade of maintainability state-of-the-arts models concerning convergence and statistical analysis of LA algorithm.

关键词： Resource Allocation Heterogeneous Networks Power Allocation lion algorithm

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Exploiting lion optimization algorithm for sustainable energy management system in industrial applications

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartC期52卷

作者： Hussain, Irshad Ullah, Ibrar Ali, Wajid Muhammad, Ghulam Ali, Zulfiqar Univ Engn & Technol Peshawar Fac Elect & Comp Engn Peshawar 25000 Khyber Pakhtunk Pakistan King Saud Univ Dept Comp Engn Coll Comp & Informat Sci Riyadh Saudi Arabia Univ Essex Sch Elect Engn & Comp Sci Colchester CO4 3SQ Essex England

Energy is the soul of each new invention on this Bio-sphere. The upturn in this advancement and technological growth, energy resources are getting scarce. As the energy resources are limited and can not be increased in the same proportion as with the exponential rising demand, that is why, we have to manage our energy consumption smartly. An optimal integration of the Renewable energy sources (RESs) for this purpose is the need of the day. This paper proposes a bio-inspired algorithm, namely, the lion's algorithm (LA), for an efficient Energy Management System (EMS) in industrial areas, along with a beneficial utilization of RES and energy storing units (ESUs). Different objectives, like, Total Energy Cost (TEC), Peak to Average power Ratio (PAR), Hourly Load (HL) and maximization of end-user comfort (the reduction in waiting time) are analyzed and observed. LA algorithm is specially designed to achieve these objectives up-to maximum optimal limits. The MATLAB simulation results illustrate that, our proposed algorithm reduced the cost up to 42.66% and PAR 35.94% compared to unscheduled and scheduled with other state-of-the-art algorithms, with an average waiting time of only 0.216 h (12.96 s).

关键词： Bio-inspired algorithms Energy management system Energy optimization lion algorithm Smart grid Traditional electric power grid Sustainable energy

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