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Zeitschriftenartikel zum Thema „Lion optimisation algorithm“

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Veröffentlicht am 25. März 2025

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1

Chander, Satish, P. Vijaya und Praveen Dhyani. „ADOFL: Multi-Kernel-Based Adaptive Directive Operative Fractional Lion Optimisation Algorithm for Data Clustering“. Journal of Intelligent Systems 27, Nr. 3 (26.07.2018): 317–29. http://dx.doi.org/10.1515/jisys-2016-0175.

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Abstract The progress of databases in fields such as medical, business, education, marketing, etc., is colossal because of the developments in information technology. Knowledge discovery from such concealed bulk databases is a tedious task. For this, data mining is one of the promising solutions and clustering is one of its applications. The clustering process groups the data objects related to each other in a similar cluster and diverse objects in another cluster. The literature presents many clustering algorithms for data clustering. Optimisation-based clustering algorithm is one of the recently developed algorithms for the clustering process to discover the optimal cluster based on the objective function. In our previous method, direct operative fractional lion optimisation algorithm was proposed for data clustering. In this paper, we designed a new clustering algorithm called adaptive decisive operative fractional lion (ADOFL) optimisation algorithm based on multi-kernel function. Moreover, a new fitness function called multi-kernel WL index is proposed for the selection of the best centroid point for clustering. The experimentation of the proposed ADOFL algorithm is carried out over two benchmarked datasets, Iris and Wine. The performance of the proposed ADOFL algorithm is validated over existing clustering algorithms such as particle swarm clustering (PSC) algorithm, modified PSC algorithm, lion algorithm, fractional lion algorithm, and DOFL. The result shows that the maximum clustering accuracy of 79.51 is obtained by the proposed method in data clustering.
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2

Gupta, Deepak, Moolchand Sharma, Rishabh Jain und Prerna Sharma. „Parkinson's diagnosis using ant-lion optimisation algorithm“. International Journal of Innovative Computing and Applications 10, Nr. 3/4 (2019): 138. http://dx.doi.org/10.1504/ijica.2019.10025053.

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3

Sharma, Prerna, Rishabh Jain, Moolchand Sharma und Deepak Gupta. „Parkinson's diagnosis using ant-lion optimisation algorithm“. International Journal of Innovative Computing and Applications 10, Nr. 3/4 (2019): 138. http://dx.doi.org/10.1504/ijica.2019.103370.

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4

Yazdani, Reza, Mohammad Javad Taghipourian, Mohammad Mahdi Pourpasha und Seyed Shamseddin Hosseini. „Attracting Potential Customers in E-Commerce Environments: A Comparative Study of Metaheuristic Algorithms“. Processes 10, Nr. 2 (14.02.2022): 369. http://dx.doi.org/10.3390/pr10020369.

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Internet technology has provided an indescribable new way for businesses to attract new customers, track their behaviour, customise services, products, and advertising. Internet technology and the new trend of online shopping have resulted in the establishment of numerous websites to sell products on a daily basis. Products compete to be displayed on the limited pages of a website in online shopping because it has a significant impact on sales. Website designers carefully select which products to display on a page in order to influence the customers’ purchasing decisions. However, concerns regarding appropriate decision making have not been fully addressed. As a result, this study conducts a comprehensive comparative analysis of the performance of ten different metaheuristics. The ant lion optimiser (ALO), Dragonfly algorithm (DA), Grasshopper optimisation algorithm (GOA), Harris hawks optimisation (HHO), Moth-flame optimisation algorithm (MFO), Multi-verse optimiser (MVO), sine cosine algorithm (SCA), Salp Swarm Algorithm (SSA), The whale optimisation algorithm (WOA), and Grey wolf optimiser (GWO) are some of the recent algorithms that were chosen for this study. The results show that the MFO outperforms the other methods in all sizes. MFO has an average normalised objective function of 81%, while ALO has a normalised objective function of 77%. In contrast, HHO has the worst performance of 16%. The study’s findings add new theoretical and practical insights to the growing body of knowledge about e-commerce environments and have implications for planners, policymakers, and managers, particularly in companies where an unplanned advertisement wastes the budget.
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Mishra, Mayank, Swarup Kumar Barman, Damodar Maity und Dipak Kumar Maiti. „Ant lion optimisation algorithm for structural damage detection using vibration data“. Journal of Civil Structural Health Monitoring 9, Nr. 1 (15.12.2018): 117–36. http://dx.doi.org/10.1007/s13349-018-0318-z.

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6

Suganthi, M., S. Selvarajan und M. N. Sudha. „Feature selection using improved lion optimisation algorithm for breast cancer classification“. International Journal of Bio-Inspired Computation 14, Nr. 4 (2019): 237. http://dx.doi.org/10.1504/ijbic.2019.10025528.

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7

Sudha, M. N., S. Selvarajan und M. Suganthi. „Feature selection using improved lion optimisation algorithm for breast cancer classification“. International Journal of Bio-Inspired Computation 14, Nr. 4 (2019): 237. http://dx.doi.org/10.1504/ijbic.2019.103963.

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8

Chander, Satish, P. Vijaya und Praveen Dhyani. „DOFL: Kernel Based Directive Operative Fractional Lion Optimisation Algorithm for Data Clustering“. International Review on Computers and Software (IRECOS) 11, Nr. 8 (31.08.2016): 701. http://dx.doi.org/10.15866/irecos.v11i8.9654.

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9

Marichelvam, M. K., P. Manimaran und M. Geetha. „Solving flexible job shop scheduling problems using a hybrid lion optimisation algorithm“. International Journal of Advanced Operations Management 10, Nr. 2 (2018): 91. http://dx.doi.org/10.1504/ijaom.2018.093257.

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10

Geetha, M., P. Manimaran und M. K. Marichelvam. „Solving flexible job shop scheduling problems using a hybrid lion optimisation algorithm“. International Journal of Advanced Operations Management 10, Nr. 2 (2018): 91. http://dx.doi.org/10.1504/ijaom.2018.10014266.

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11

Yang, Jin, Wenke Zhu, Guanqi Liu, Weisi Dai, Zhuonong Xu, Li Wan und Guoxiong Zhou. „ICPNet: Advanced Maize Leaf Disease Detection with Multidimensional Attention and Coordinate Depthwise Convolution“. Plants 13, Nr. 16 (15.08.2024): 2277. http://dx.doi.org/10.3390/plants13162277.

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Maize is an important crop, and the detection of maize diseases is critical for ensuring food security and improving agricultural production efficiency. To address the challenges of difficult feature extraction due to the high similarity among maize leaf disease species, the blurring of image edge features, and the susceptibility of maize leaf images to noise during acquisition and transmission, we propose a maize disease detection method based on ICPNet (Integrated multidimensional attention coordinate depthwise convolution PSO (Particle Swarm Optimization)-Integrated lion optimisation algorithm network). Firstly, we introduce a novel attention mechanism called Integrated Multidimensional Attention (IMA), which enhances the stability and responsiveness of the model in detecting small speckled disease features by combining cross-attention and spatial channel reconstruction methods. Secondly, we propose Coordinate Depthwise Convolution (CDC) to enhance the accuracy of feature maps through multi-scale convolutional processing, allowing for better differentiation of the fuzzy edges of maize leaf disease regions. To further optimize model performance, we introduce the PSO-Integrated Lion Optimisation Algorithm (PLOA), which leverages the exploratory stochasticity and annealing mechanism of the particle swarm algorithm to enhance the model’s ability to handle mutation points while maintaining training stability and robustness. The experimental results demonstrate that ICPNet achieved an average accuracy of 88.4% and a precision of 87.3% on the self-constructed dataset. This method effectively extracts the tiny and fuzzy edge features of maize leaf diseases, providing a valuable reference for disease control in large-scale maize production.
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12

Manimaran, P., und M. K. Marichelvam. „Ant lion optimisation algorithm for two stage supply chain network associated with fixed charges“. International Journal of Services and Operations Management 37, Nr. 3 (2020): 407. http://dx.doi.org/10.1504/ijsom.2020.10032886.

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13

Manimaran, P., und M. K. Marichelvam. „Ant lion optimisation algorithm for two stage supply chain network associated with fixed charges“. International Journal of Services and Operations Management 37, Nr. 3 (2020): 407. http://dx.doi.org/10.1504/ijsom.2020.111035.

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14

Menaga, D., und S. Revathi. „Least lion optimisation algorithm (LLOA) based secret key generation for privacy preserving association rule hiding“. IET Information Security 12, Nr. 4 (01.07.2018): 332–40. http://dx.doi.org/10.1049/iet-ifs.2017.0634.

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15

Ali, Ziad M., Shady H. E. Abdel Aleem, Ahmed I. Omar und Bahaa Saad Mahmoud. „Economical-Environmental-Technical Operation of Power Networks with High Penetration of Renewable Energy Systems Using Multi-Objective Coronavirus Herd Immunity Algorithm“. Mathematics 10, Nr. 7 (06.04.2022): 1201. http://dx.doi.org/10.3390/math10071201.

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This paper proposes an economical-environmental-technical dispatch (EETD) model for adjusted IEEE 30-bus and IEEE 57-bus systems, including thermal and high penetration of renewable energy sources (RESs). Total fuel costs, emissions level, power losses, voltage deviation, and voltage stability are the five objectives addressed in this work. A large set of equality and inequality constraints are included in the problem formulation. Metaheuristic optimization approaches—Coronavirus herd immunity optimizer (CHIO), salp swarm algorithm (SSA), and ant lion optimizer (ALO)—are used to identify the optimal cost of generation, emissions, voltage deviation, losses, and voltage stability solutions. Several scenarios are reviewed to validate the problem-solving competency of the defined optimisation model. Numerous scenarios are studied to verify the proficiency of the optimisation model in problem-solving. The multi-objective problem is converted into a normalized one-objective issue through a weighted sum-approach utilizing the analytical hierarchy process (AHP). Additionally, the technique for order preference by similarity to ideal solution (TOPSIS) is presented for identifying the optimal value of Pareto alternatives. Ultimately, the results achieved reveal that the proposed CHIO performs the other approaches in the EETD problem-solving.
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16

Yazdani, Reza, Mirpouya Mirmozaffari, Elham Shadkam und Seyed Mohammad Khalili. „A lion optimisation algorithm for a two-agent single-machine scheduling with periodic maintenance to minimise the sum of maximum earliness and tardiness“. International Journal of Industrial and Systems Engineering 44, Nr. 4 (2023): 515–31. http://dx.doi.org/10.1504/ijise.2023.132730.

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17

Pal, Subham, Kanak Kalita und Salil Haldar. „Comparison of nature‐inspired algorithms in finite element‐based metaheuristic optimisation of laminated shells“. Expert Systems, 14.05.2024. http://dx.doi.org/10.1111/exsy.13620.

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AbstractThis work presents a unique technique for optimising composite laminates used as structural components, which is critical for situations where failure might result in disastrous effects. Unlike traditional surrogate‐based optimisation approaches, this methodology combines the accurate modelling capabilities of finite element (FE) analysis with the iterative refining capacity of metaheuristic algorithms. By combining these two methodologies, our method intends to improve the design process of laminated shell structures, assuring robustness and dependability is crucial. Compared to existing benchmark solutions, the current FE shows a <1% error for cylindrical and spherical shells. The prime objective of this study is to identify the optimum ply angles for attaining a high fundamental frequency. The problem is NP‐hard because the possible ply angles span a wide range (±90°), making it difficult for optimisation algorithms to find a solution. Seven popular metaheuristic algorithms, namely the genetic algorithm (GA), the ant lion optimisation (ALO), the arithmetic optimisation algorithm (AOA), the dragonfly algorithm (DA), the grey wolf optimisation (GWO), the salp swarm optimisation (SSO), and the whale optimisation algorithm (WOA), are applied to and compared on a wide range of shell design problems. It assesses parameter sensitivity, discovering significant design elements that influence dynamic behaviour. Convergence studies demonstrate the superior performance of AOA, GWO, and WOA optimisers. Rigorous statistical comparisons assist practitioners in picking the best optimisation technique. FE‐GWO, FE‐DA, and FE‐SSA methods surpass the other techniques as well as the layerwise optimisation strategy. The findings obtained, employing the GWO, DA, and SSA optimisers, demonstrate ~3% improvement over the existing literature. With respect to conventional layup designs (cross‐ply and angle‐ply), the current optimised designs are better by at least 0.43% and as much as 48.91%.
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18

Vennila, H., Nimay Chandra Giri, Manoj Kumar Nallapaneni, Pampa Sinha, Mohit Bajaj, Mohamad Abou Houran und Salah Kamel. „Static and dynamic environmental economic dispatch using tournament selection based ant lion optimization algorithm“. Frontiers in Energy Research 10 (07.09.2022). http://dx.doi.org/10.3389/fenrg.2022.972069.

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The static and dynamic economic dispatch problems are solved by creating an enhanced version of ant lion optimisation (ALO), namely a tournament selection-based ant lion optimisation (TALO) method. The proposed algorithm is presented to solve the combined economic and emission dispatch (CEED) problem with considering the generator constraints such as ramp rate limits, valvepoint effects, prohibited operating zones and transmission loss. The proposed algorithm’s efficiency was tested using a 5-unit generating system in MATLAB R2021a during a 24-hour time span. When compared to previous optimization methods, the suggested TALO reduces the costs of fuel and pollution by 9.01 and 4.7 percent, respectively. Furthermore, statistical analysis supports the suggested TALO optimization superiority over other methods. It is observed that the renewable energy output can be stabilized in the future by combining a hybrid dynamic economic and emission dispatch model with thermal power units, wind turbines, solar and energy storage devices to achieve the balance between operational costs and pollutant emissions.
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19

„Combined economic and emission dispatch using Whale Optimization Algorithm“. ARPN Journal of Engineering and Applied Sciences, 29.02.2024, 2692–707. http://dx.doi.org/10.59018/1223321.

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Power plants give the most to environmental pollution, another important factor nowadays. Power stations must hold carbon credits and follow tight carbon emission restrictions. This is crucial for minimizing global warming and sustaining life. Electric power system planning and operation must meet load demand reliably, cost-effectively, and environmentally. Planners and operators use optimisation tools to attain these goals. In this study, the performance of two new optimisation methods, like the Whale Optimisation Algorithm (WOA), is compared to the performance of two older optimisation methods, like the Moth Flame Optimisation (MFO) and the Ant Lion Optimisation (ALO). When compared to the other two optimisation method, the results from the new optimisation method are better. It is obvious that there are competing goals that must be met. One cannot reasonably expect to achieve both the goal of reducing fuel costs and that of reducing gaseous emissions. In order to aid decision-makers in making the best choices, multi objective optimisation techniques are used to derive trade-off relationships between these incompatible goal functions. In this study, we examine the economic load dispatching issues that arise in the operation of power systems. The objective function of the issue is first analysed as a multi-objective function, with power dispatch and environmental considerations each being addressed as a distinct goal. Both the single- and multi-objective variants are examples of high-dimensional, nonlinear, non-convex constrained optimisation problems. Because of this, employing any optimisation strategy is extremely difficult. Several algorithms, including those that take their cues from nature, have been implemented to help us get as near as possible to optimum solutions tools.
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Mhetre, Santosh Laxman, und Iranna Korachagaon. „Hybrid optimization for optimal positioning and sizing of distributed generators in unbalanced distribution networks“. Energy Harvesting and Systems, 29.11.2022. http://dx.doi.org/10.1515/ehs-2021-0046.

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Abstract The goal of this work is to reduce power loss and improve voltage profile by formulating the optimal DG placement problem as a restricted nonlinear optimisation problem. As a novelty, the proposed hybrid algorithm, referred to as Multifactor Update-based Hybrid Model (MUHM) is constructed by merging the concepts of Lion Algorithm (LA) & Sea Lion Algorithm (Sea Lion Optimization Algorithm (SLnO). The Forward-Backward Sweep (FBSM) Model is used to calculate the power loss. Three test cases are examined for the voltage profile & loss minimization in the feeder team with DGs: “case 1(DG supplying real power alone (P), case 2 (DG supplying reactive power alone (Q) and Case 3 (DG supplying both real and reactive power)”. Application of the suggested method to various IEEE test systems, including IEEE 33, IEEE 123, and IEEE 69, respectively, is used to assess its efficacy. According, the results show that the presented work at loading percentage = 0 is 12, 15, 135, 4.65, and 8 superior to SFF, BBO, BAT, LA and SLnO, respectively.
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Kar, Manoj Kumar, Jogeswara Sabat und Sanjeet Kanungo. „Voltage profile enhancement of an AVR system using Ant Lion Optimization algorithm“. Engineering Research Express, 22.01.2025. https://doi.org/10.1088/2631-8695/adad32.

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Abstract This paper addresses voltage regulation, which is a critical issue in power systems. The automatic voltage regulator (AVR) plays a crucial role in maintaining voltage regulation within permissible limits The AVR system's dynamic behaviour is improved by applying the Proportional (KP) Integral (KI) Derivative (KD) (PID) controller. The primary challenges of using a PID controller in AVR include difficulties in tuning parameters to handle nonlinearities, load disturbances, and system dynamics, leading to potential instability, overshoot, or slow response. The controller parameters are tuned with Ant Lion Optimisation (ALO) algorithm and Integral Time Absolute Error (ITAE) as an objective function. Also, the proposed system has the ability to improve the transient response of AVR by decreasing the maximum overshoot, settling time, rise time, and peak time magnitudes of the generator terminal voltage with eliminating the steady state error. Once the suggested method had produced the best values for the three gains i.e., KP, KI, and KD of the PID controller, a transient response analysis is examined and compared with some of the existing techniques such as Bat Algorithm (BA), Grey Wolf Optimisation (GWO) and Biogeography Based Optimisation (BBO) algorithm to highlight the novelty of the proposed ALO optimized PID controller. Transient response analysis, root locus analysis, and bode analysis are used to assess the AVR system's stability. Comparisons to three well-known optimization techniques corroborate the findings. The results show that this new suggested method performs exceptionally well even when there are significant variations in power system parameters and load uncertainties.
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22

Negi, Pankaj, Yash Pal und Leena G. „Performance enhancement of grid‐connected photovoltaic systems using Ant Lion optimisation and Genetic Algorithm‐based optimisation techniques“. Cognitive Computation and Systems, 16.03.2022. http://dx.doi.org/10.1049/ccs2.12058.

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23

Binbusayyis, Adel, Abed Alanazi, Shtwai Alsubai, Areej Alasiry, Mehrez Marzougui, Abdullah Alqahtani, Mohemmed Sha und Muhammad Aslam. „A secured cloud‐medical data sharing with A‐BRSA and Salp ‐Ant Lion Optimisation Algorithm“. CAAI Transactions on Intelligence Technology, 17.05.2024. http://dx.doi.org/10.1049/cit2.12305.

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AbstractSharing medical data among healthcare providers, researchers, and patients is crucial for efficient healthcare services. Cloud‐assisted smart healthcare (s‐healthcare) systems have made it easier to store EHRs effectively. However, the traditional encryption algorithms used to secure this data can be vulnerable to attacks if the encryption key is compromised, posing a security threat. A secured cloud‐based medical data‐sharing system is proposed using a hybrid encryption model called A‐BRSA, which combines attribute‐based encryption (ABE) and B‐RSA encryption. The system utilises the Salp‐Ant Lion Optimisation Algorithm for optimal key selection. The encrypted data is stored in the cloud and transmitted to the recipient, where it is decrypted using A‐BRSA‐based decryption. The study measures turnaround time, encryption time, decryption time, and restoration efficiency to evaluate the system's performance. The results demonstrate the effectiveness of the A‐BRSA model in ensuring secure medical data sharing in cloud‐based s‐healthcare systems.
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Yan, Renwu, Yihan Lin, Ning Yu und Yi Wu. „A low‐carbon economic dispatch model for electricity market with wind power based on improved ant‐lion optimisation algorithm“. CAAI Transactions on Intelligence Technology, 29.09.2022. http://dx.doi.org/10.1049/cit2.12138.

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25

Subramaniam, Anuvelavan, Sureshkumar Chelladurai, Stanly Kumar Ande und Sathiyandrakumar Srinivasan. „Securing IoT network with hybrid evolutionary lion intrusion detection system: a composite motion optimisation algorithm for feature selection and ensemble classification“. Journal of Experimental & Theoretical Artificial Intelligence, 18.07.2024, 1–23. http://dx.doi.org/10.1080/0952813x.2024.2342858.

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26

Patel, Namrata Jiten, und Ashish Jadhav. „Design of an efficient dynamic context‐based privacy policy deployment model via dual bioinspired Q learning optimisations“. IET Cyber-Physical Systems: Theory & Applications, 27.06.2024. http://dx.doi.org/10.1049/cps2.12100.

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AbstractA novel context‐based privacy policy deployment model enhanced with bioinspired Q‐learning optimisations is presented. The model addresses the challenge of maintaining privacy while ensuring data integrity and usability in various settings. Leveraging datasets including Adult (Census Income), Yelp, UC Irvine Machine Learning, and Movie Lens, the authors evaluate the model's performance against state‐of‐the‐art techniques, such as GEF AL, Deep Forest, and Robust Continual Learning. The approach employs Firefly Optimiser (FFO) and Ant Lion Optimiser (ALO) algorithms to dynamically adjust privacy parameters and handle large datasets efficiently. Additionally, Q‐learning enables intelligent decision‐making and rapid adaptation to changing data and network conditions and scenarios. Evaluation results demonstrate that the model consistently outperforms reference techniques across multiple metrics, including privacy levels, scalability, fidelity, and sensitivity management. By reducing reputational harm, minimising delays, and enhancing network quality, the model offers robust privacy protection without sacrificing data utility. Overall, a dynamic context‐based privacy policy deployment approach, enhanced with bioinspired Q‐learning optimisations, presents a significant advancement in privacy preservation methods. The combination of ALO, FFO, and Q‐learning techniques offers a practical solution to evolving data privacy challenges and enhances flexibility in various use case scenarios.
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