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Relevant bibliographies by topics / Ameliorated ant lion optimization algorithm

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Academic literature on the topic 'Ameliorated ant lion optimization algorithm'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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Journal articles on the topic "Ameliorated ant lion optimization algorithm"

1

Mallala, Balasubbareddy, Dwivedi Divyanshi, Venkata Krishna Murthy Garikamukkala, and Sowjan Kumar Kotte. "Optimal power flow solution with current injection model of generalized interline power flow controller using ameliorated ant lion optimization." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 1060–77. https://doi.org/10.11591/ijece.v13i1.pp1060-1077.

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Optimal power flow (OPF) solutions with generalized interline power flow controller (GIPFC) devices play an imperative role in enhancing the power system’s performance. This paper used a novel ant lion optimization (ALO) algorithm which is amalgamated with Lévy flight operator, and an effectual algorithm is proposed named as, ameliorated ant lion optimization (AALO) algorithm. It is being implemented to solve single objective OPF problem with the latest flexible alternating current transmission system (FACTS) controller named as GIPFC. GIPFC can control a couple of transmission lines concurrently and it also helps to control the sending end voltage. In this paper, current injection modeling of GIPFC is being incorporated in conventional Newton-Raphson (NR) load flow to improve voltage of the buses and focuses on minimizing the considered objectives such as generation fuel cost, emissions, and total power losses by fulfilling equality, in-equality. For optimal allocation of GIPFC, a novel Lehmann-SymanzikZimmermann (LSZ) approach is considered. The proposed algorithm is validated on single benchmark test functions such as Sphere, Rastrigin function then the proposed algorithm with GIPFC has been testified on standard IEEE-30 bus system.

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Balasubbareddy, Mallala, Divyanshi Dwivedi, Garikamukkala Venkata Krishna Murthy, and Kotte Sowjan Kumar. "Optimal power flow solution with current injection model of generalized interline power flow controller using ameliorated ant lion optimization." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 1060. http://dx.doi.org/10.11591/ijece.v13i1.pp1060-1077.

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<span lang="EN-US">Optimal power flow (OPF) solutions with generalized interline power flow controller (GIPFC) devices play an imperative role in enhancing the power system’s performance. This paper used a novel ant lion optimization (ALO) algorithm which is amalgamated with Lévy flight operator, and an effectual algorithm is proposed named as, ameliorated ant lion optimization (AALO) algorithm. It is being implemented to solve single objective OPF problem with the latest flexible alternating current transmission system (FACTS) controller named as GIPFC. GIPFC can control a couple of transmission lines concurrently and it also helps to control the sending end voltage. In this paper, current injection modeling of GIPFC is being incorporated in conventional Newton-Raphson (NR) load flow to improve voltage of the buses and focuses on minimizing the considered objectives such as generation fuel cost, emissions, and total power losses by fulfilling equality, in-equality. For optimal allocation of GIPFC, a novel Lehmann-Symanzik-Zimmermann (LSZ) approach is considered. The proposed algorithm is validated on single benchmark test functions such as Sphere, Rastrigin function then the proposed algorithm with GIPFC has been testified on standard IEEE-30 bus system.</span>

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3

Hassanien, Aboul Ella, and Ramadan Babers. "Metaheuristic Algorithms for Detect Communities in Social Networks: A Comparative Analysis Study." International Journal of Rough Sets and Data Analysis 5, no. 2 (2018): 25–45. http://dx.doi.org/10.4018/ijrsda.2018040102.

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This article presents a comparative analysis between Cuckoo Search Optimization Algorithm, Lion Optimization Algorithm and Ant-Lion Optimization Algorithm. Zachary karate Club, The Bottlenose Dolphin Network, American College Football Network, and Facebook used as benchmark datasets for comparison, the results proved those algorithms can define the structure and detect communities of complex networks with high accuracy and quality based on different method that it used. The Cuckoo Search Optimization Algorithm is the best algorithm compared to Ant-Lion Optimization Algorithm and Lion Optimization Algorithm as it got greatest number of communities, detect communities in used benchmark datasets with average accuracy %69, average modularity %62 and average fitness %60.

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4

Hamouda, Eslam, Sara El-Metwally, and Mayada Tarek. "Ant Lion Optimization algorithm for kidney exchanges." PLOS ONE 13, no. 5 (2018): e0196707. http://dx.doi.org/10.1371/journal.pone.0196707.

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5

Guo, Hao. "Research on Ant Lion Optimization Algorithm for BP Neural Network in Transformer Fault Diagnosis." Journal of Big Data and Computing 2, no. 3 (2024): 1–5. https://doi.org/10.62517/jbdc.202401301.

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Aiming at the problem of low accuracy in transformer fault diagnosis, an Ant Lion Optimization (ALO) algorithm is proposed to optimize the BP neural network for transformer fault diagnosis. By using the ant lion optimization algorithm to optimize the weights and thresholds of the BP neural network, the problem of premature convergence of the BP neural network can be avoided, and the accuracy of the transformer fault diagnosis model can be improved. The BP neural network model optimized by the ant lion optimization algorithm was used for transformer fault diagnosis. To verify the effectiveness of the proposed method, it was compared with the genetic algorithm optimized BP neural network (GA-BP) and the artificial bee colony (ABC-BP) algorithm optimized BP neural network methods. The experimental results showed that the proposed method has higher fault diagnosis accuracy.

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6

Liu, Songzi, Mou Lv, and Hongwei Li. "Intelligent Leakage Location of Urban Small Water Supply Network." Journal of Physics: Conference Series 2185, no. 1 (2022): 012041. http://dx.doi.org/10.1088/1742-6596/2185/1/012041.

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Abstract In this paper, the leakage location model of pipe network is established based on EPANET software. Two intelligent swarm optimization algorithms, ant lion optimization algorithm and particle swarm optimization algorithm, are used to solve the model. Taking the industrial water supply network of a coastal city in North China as an example, the operation of the two algorithms is analyzed and compared. The results show that the ant lion optimization algorithm has stronger global optimization ability and higher search efficiency in the problem of leakage location; it also has high application value in practical engineering.

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7

Wang, Wenjing, and Renjun Zhou. "Application of improved ant-lion algorithm for power systems." PLOS ONE 19, no. 12 (2024): e0311563. https://doi.org/10.1371/journal.pone.0311563.

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An improved ant-lion algorithm is proposed to solve the load allocation problem to improve the efficiency of load allocation in the power system. The global search capability and optimization performance of the algorithm have been significantly improved by introducing elite weights and chaotic search mechanisms. The innovation of the research lies in not only optimizing economic goals, but also considering environmental goals, achieving dual optimization of economy and environment. The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. The required number of iterations was significantly better than other algorithms. In the verification of solving economic load dispatch, the improved ant-lion optimizer achieved a total fuel cost reduction of 0.10% -2.39% and 6% in both 3-unit and 6-unit simulations, respectively, compared to the other three algorithms. In the verification of solving environmental and economic load dispatch, considering the valve point effect, this proposed optimization scheme had a total fuel cost of 622.46 $/hr and a total emission of 0.20 tons/h. The total objective function was 1542.54 $/hr, which was an average reduction of 53.55 $/hr compared to the other five algorithms. Therefore, improving the ant-lion optimizer can enhance its optimization performance. The improved ant-lion optimizer has positive application significance in power system load dispatch and can achieve superior load dispatch results.

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8

Hardiansyah, Hardiansyah. "Dynamic economic emission dispatch using ant lion optimization." Bulletin of Electrical Engineering and Informatics 9, no. 1 (2020): 12–20. http://dx.doi.org/10.11591/eei.v9i1.1664.

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This paper aims to propose a new meta-heuristic search algorithm, called Ant Lion Optimization (ALO). The ALO is a newly developed population-based search algorithm inspired hunting mechanism of ant lions. The proposed algorithm is presented to solve the dynamic economic emission dispatch (DEED) problem with considering the generator constraints such as ramp rate limits, valve-point effetcs, prohibited operating zones and transmission loss. The 5-unit generation system for a 24 h time interval has been taken to validate the efficiency of the proposed algorithm. Simulation results clearly show that the proposed method outperforms in terms of solution quality when compared with the other optimization algorithms reported in the literature.

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9

Hardiansyah, Hardiansyah. "Dynamic economic emission dispatch using ant lion optimization." Bulletin of Electrical Engineering and Informatics 9, no. 1 (2020): 12–20. https://doi.org/10.11591/eei.v9i1.1664.

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Abstract:

This paper aims to propose a new meta-heuristic search algorithm, called Ant Lion Optimization (ALO). The ALO is a newly developed population-based search algorithm inspired hunting mechanism of ant lions. The proposed algorithm is presented to solve the dynamic economic emission dispatch (DEED) problem with considering the generator constraints such as ramp rate limits, valve-point effects, prohibited operating zones and transmission loss. The 5-unit generation system for a 24 h time interval has been taken to validate the efficiency of the proposed algorithm. Simulation results clearly show that the proposed method outperforms in terms of solution quality when compared with the other optimization algorithms reported in the literature.

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10

Ali, E. S., S. M. Abd Elazim, and A. Y. Abdelaziz. "Ant Lion Optimization Algorithm for Renewable Distributed Generations." Energy 116 (December 2016): 445–58. http://dx.doi.org/10.1016/j.energy.2016.09.104.

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Book chapters on the topic "Ameliorated ant lion optimization algorithm"

1

Okwu, Modestus O., and Lagouge K. Tartibu. "Ant Lion Optimization Algorithm." In Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61111-8_9.

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Mani, Melika, Omid Bozorg-Haddad, and Xuefeng Chu. "Ant Lion Optimizer (ALO) Algorithm." In Advanced Optimization by Nature-Inspired Algorithms. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5221-7_11.

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Manikanda Selvam, S., and T. Yuvaraj. "Optimal Allocation of Capacitor Using Ant Lion Optimization Algorithm." In Proceedings of International Conference on Data Science and Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5348-3_22.

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4

Gulati, Devansh, Mehul Gupta, Dinesh Kumar Saini, and Punit Gupta. "Neural Inspired Ant Lion Algorithm for Resource Optimization in Cloud." In Sustainable Smart Cities. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08815-5_12.

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Tyagi, Tushar, Amit Kumar Singh, Himanshu Sharma, and Rintu Khanna. "Harmonic Minimization in Multilevel Inverters Using Ant Lion Optimization Algorithm." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8892-8_43.

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6

Kumari, Ruchika, and Rakesh Kumar. "Vehicular Ant Lion Optimization Algorithm (VALOA) for Urban Traffic Management." In Innovations in Computer Science and Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4543-0_12.

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Majhi, Santosh Kumar, and Shubhra Biswal. "A Hybrid Clustering Algorithm Based on Kmeans and Ant Lion Optimization." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1498-8_56.

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8

Gupta, Naman, Rishabh Jain, Deepak Gupta, Ashish Khanna, and Aditya Khamparia. "Modified Ant Lion Optimization Algorithm for Improved Diagnosis of Thyroid Disease." In Cognitive Informatics and Soft Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1451-7_61.

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9

Guha, Dipayan, Provas Kumar Roy, and Subrata Banerjee. "Ant Lion Optimization: A Novel Algorithm Applied to Load Frequency Control Problem in Power System." In Operations Research and Optimization. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7814-9_15.

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10

Preetha, P. S., and Ashok Kusagur. "Implementation of Ant-Lion Optimization Algorithm in Energy Management Problem and Comparison." In Learning and Analytics in Intelligent Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24318-0_55.

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Conference papers on the topic "Ameliorated ant lion optimization algorithm"

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Dzulkalnine, Mohamad Faiz, Khyrina Airin Fariza Abu Samah, and Sulaiman Mahzan. "Medical Data Imputation by Hybrid Optimized Fuzzy C-Means and Ant Lion Optimization Algorithm." In 2024 IEEE 22nd Student Conference on Research and Development (SCOReD). IEEE, 2024. https://doi.org/10.1109/scored64708.2024.10872640.

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Guan, Yunchong, Wei Zhi Sturdy So, Su Peng, et al. "Enhancing Multi-UAV Path Planning Efficiency with Security through Integration of Priority-Based Ant Lion Optimization Algorithm." In 2024 Sixth International Conference on Next Generation Data-driven Networks (NGDN). IEEE, 2024. http://dx.doi.org/10.1109/ngdn61651.2024.10744075.

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3

Yu, Jie, Chenghuai Hong, Jian Yang, JianHui Meng, and Kai Gao. "An Optimization Method for Capacity Configuration of Island Wind-Hydrogen System Based on Improved Ant Lion Algorithm." In 2025 8th International Conference on Energy, Electrical and Power Engineering (CEEPE). IEEE, 2025. https://doi.org/10.1109/ceepe64987.2025.11034119.

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4

Ganesh, Narayanan, Aravind Shankar R, Srinivasan Arindham, and Arvind Venkat Ramanan. "Comparative Analysis of the Black-winged Kite Algorithm (BWKA) with Ant Lion Optimizer and Grey Wolf Optimizer using Benchmark Functions for Global Optimization." In 2025 AI-Driven Smart Healthcare for Society 5.0. IEEE, 2025. https://doi.org/10.1109/ieeeconf64992.2025.10962946.

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5

Davendra, Donald, Magdalena Bialic-Davendra, and Magdalena Metlicka. "Chaotic Ant Lion Optimization Algorithm." In 2022 IEEE Workshop on Complexity in Engineering (COMPENG). IEEE, 2022. http://dx.doi.org/10.1109/compeng50184.2022.9905467.

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6

Shen, Haicheng, and Qiang Liu. "An Improved Ant Lion Optimization Algorithm and Its Application*." In 2022 IEEE International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2022. http://dx.doi.org/10.1109/icnsc55942.2022.10004110.

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Luo, Yuke. "Multi-objective optimization of microgrid based on improved ant lion optimization algorithm." In 2023 2nd International Conference on Smart Grids and Energy Systems (SGES). IEEE, 2023. http://dx.doi.org/10.1109/sges59720.2023.10367022.

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Ye, Zhiwei, Yuanzhi Tang, Wei Liu, et al. "Learning Parameters in Deep Belief Networks Through Ant Lion Optimization Algorithm." In 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2019. http://dx.doi.org/10.1109/idaacs.2019.8924288.

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Kexin, Zhao, Ku Shuo, Han Bo, Wang Jie, and Zhou Rui. "An ant lion optimization algorithm with random fractal adaptive search strategy." In 2017 3rd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2017. http://dx.doi.org/10.1109/compcomm.2017.8322929.

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10

Sakthi Gokul Rajan, C., and K. Ravi. "Optimal placement and sizing of DSTATCOM using Ant lion optimization algorithm." In 2019 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC). IEEE, 2019. http://dx.doi.org/10.1109/iccpeic45300.2019.9082382.

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