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Статті в журналах з теми "Frog Leaping Algorithm"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Dr.K.Lenin. "REDUCTION OF ACTIVE POWER LOSS BY IMPROVED FROG LEAPING ALGORITHM." International Journal of Research - Granthaalayah 5, no. 9 (2017): 44–51. https://doi.org/10.5281/zenodo.999199.

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Анотація:

This paper presents Improved Frog Leaping (IFL) algorithm for solving optimal reactive power problem. Comprehensive exploration capability of Particle Swarm Optimization (PSO) and good local search ability of Frog Leaping Algorithm (FLA) has been hybridized to solve the reactive power problem and it overcomes the shortcomings of premature convergence. In order to evaluate the validity of the proposed Improved Frog Leaping (IFL) algorithm, it has been tested in Standard IEEE 57,118 bus systems and compared to other standard algorithms. Simulation results show that proposed Improved Frog Leaping

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2

Lenin, K. "REDUCTION OF ACTIVE POWER LOSS BY IMPROVED FROG LEAPING ALGORITHM." International Journal of Research -GRANTHAALAYAH 5, no. 9 (2017): 44–51. http://dx.doi.org/10.29121/granthaalayah.v5.i9.2017.2197.

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Анотація:

This paper presents Improved Frog Leaping (IFL) algorithm for solving optimal reactive power problem. Comprehensive exploration capability of Particle Swarm Optimization (PSO) and good local search ability of Frog Leaping Algorithm (FLA) has been hybridized to solve the reactive power problem and it overcomes the shortcomings of premature convergence. In order to evaluate the validity of the proposed Improved Frog Leaping (IFL) algorithm, it has been tested in Standard IEEE 57,118 bus systems and compared to other standard algorithms. Simulation results show that proposed Improved Frog Leaping

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3

Liu, Liqun, Renyuan Gu, Jiuyuan Huo, and Yubo Zhou. "Origin-Oriented Shuffled Frog Leaping Vehicle Routing Multiobjective Optimization Algorithm." Journal of Database Management 34, no. 3 (2023): 1–24. http://dx.doi.org/10.4018/jdm.321549.

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Анотація:

Shuffled frog leaping algorithm is a biological swarm intelligent optimization algorithm and improved into capacity-limited vehicle routing problem. However, the optimization performance is limited with improvement strategies in major of the improvement algorithm. A novel framework of algorithm is proposed to solve capacity-limited vehicle routing problem, including three modules such as origin oriented shuffled frog leaping algorithm strategy, origin oriented shuffled frog leaping vehicle routing multiobjective optimization algorithm strategy, and output module. The frog individuals gather ne

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4

Dr., Lenin Kanagasabai. "REDUCTION OF REAL POWER LOSS BY IMPROVED SHUFFLED FROG-LEAPING ALGORITHM." International Journal of Research - Granthaalayah 6, no. 10 (2018): 146–57. https://doi.org/10.5281/zenodo.1476667.

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Анотація:

This paper presents Improved Shuffled Frog-Leaping (ISFL) algorithm for solving optimal reactive power problem. A new search-acceleration parameter has been introduced into the formulation of the original shuffled frog leaping (SFL) algorithm to create an adapted form of the shuffled frog algorithm for solving the reactive power problem. The shuffled frog-leaping algorithm draws its formulation from two other search techniques: the local search of the ‘particle swarm optimization’ technique; and the competitiveness mixing of information of the ‘shuffled complex evolution&rsqu

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5

Yang, Huai De, and Yong Li. "An Intrusion Detection Method Based on Shuffle Frog Leaping Algorithm." Advanced Materials Research 1030-1032 (September 2014): 1646–49. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1646.

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This paper presents an intrusion detection model based on shuffled frog leaping algorithm, the model search speed, high accuracy based on shuffled frog leaping algorithm, using the shuffled frog leaping algorithm generates a set of classification rules from the KDD99 data network audit data collection, quality and the use of objective function shuffled frog leaping algorithm to control the production rule, and then application of the rule of dynamically generated to rule based intrusion detection system, achieve the purpose of detection. The experimental results show that, this method of detec

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6

Zhu, Kai Feng, Xue Wang, and Gui Hua Cai. "Research of Distribution Network Reconfiguration Based on SFLGA." Advanced Materials Research 960-961 (June 2014): 1058–61. http://dx.doi.org/10.4028/www.scientific.net/amr.960-961.1058.

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The article studies on the application of the shuffled frog leaping algorithm (SFLA) in power distribution network reconfiguration, taking the minimum loss and voltage quality of distribution network as a multi-objective function. The article improves the initial solution generation strategy of traditional genetic algorithm, which ensures the initial solution is feasible solution. Shuffled frog leaping algorithm and genetic algorithms are combined and proposed as shuffled frog leaping genetic algorithm (SFLGA). The algorithm uses the efficient coding strategy based on the basic loop and initia

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7

Feng, Shuo, and Jinqing Jia. "Acceleration sensor placement technique for vibration test in structural health monitoring using microhabitat frog-leaping algorithm." Structural Health Monitoring 17, no. 2 (2017): 169–84. http://dx.doi.org/10.1177/1475921716688372.

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Анотація:

In this article, a microhabitat frog-leaping algorithm is proposed based on original shuffled frog-leaping algorithm and effective independence method to make the algorithm more efficient to optimize the 3-axis acceleration sensor configuration in the vibration test of structural health monitoring. Optimal sensor placement is a vital component of vibration test in structural health monitoring technique. Acceleration sensors should be placed such that all of the important information is collected. The resulting sensor configuration should be optimal such that the testing resources are saved. In

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8

Dalavi, Amol M., Padmakar J. Pawar, and Tejinder Paul Singh. "Tool path planning of hole-making operations in ejector plate of injection mould using modified shuffled frog leaping algorithm." Journal of Computational Design and Engineering 3, no. 3 (2016): 266–73. http://dx.doi.org/10.1016/j.jcde.2016.04.001.

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Abstract Optimization of hole-making operations in manufacturing industry plays a vital role. Tool travel and tool switch planning are the two major issues in hole-making operations. Many industrial applications such as moulds, dies, engine block, automotive parts etc. requires machining of large number of holes. Large number of machining operations like drilling, enlargement or tapping/reaming are required to achieve the final size of individual hole, which gives rise to number of possible sequences to complete hole-making operations on the part depending upon the location of hole and tool se

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9

Kanagasabai, Lenin. "REDUCTION OF REAL POWER LOSS BY IMPROVED SHUFFLED FROG-LEAPING ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 10 (2018): 146–57. http://dx.doi.org/10.29121/granthaalayah.v6.i10.2018.1172.

Повний текст джерела

Анотація:

This paper presents Improved Shuffled Frog-Leaping (ISFL) algorithm for solving optimal reactive power problem. A new search-acceleration parameter has been introduced into the formulation of the original shuffled frog leaping (SFL) algorithm to create an adapted form of the shuffled frog algorithm for solving the reactive power problem. The shuffled frog-leaping algorithm draws its formulation from two other search techniques: the local search of the ‘particle swarm optimization’ technique; and the competitiveness mixing of information of the ‘shuffled complex evolution’ technique. Proposed I

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10

Lenin, Kanagasabai. "Factual Power Loss Diminution by Enhanced Frog Leaping Algorithm." Journal of Applied Science, Engineering, Technology, and Education 3, no. 2 (2020): 114–18. http://dx.doi.org/10.35877/454ri.asci112.

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Анотація:

This paper proposes Enhanced Frog Leaping Algorithm (EFLA) to solve the optimal reactive power problem. Frog leaping algorithm (FLA) replicates the procedure of frogs passing though the wetland and foraging deeds. Set of virtual frogs alienated into numerous groups known as “memeplexes”. Frog’s position’s turn out to be closer in every memeplex after few optimization runs and certainly, this crisis direct to premature convergence. In the proposed Enhanced Frog Leaping Algorithm (EFLA) the most excellent frog information is used to augment the local search in each memeplex and initiate to the e

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11

Wang, Yongxian, Junxia Ma, and Yanrong Zhang. "Application of Improved Frog Leaping Algorithm in Multi objective Optimization of Engineering Project Management." Decision Making: Applications in Management and Engineering 7, no. 1 (2023): 364–79. http://dx.doi.org/10.31181/dmame712024896.

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Анотація:

The development of information has promoted the development of various industries, and the development of industries will inevitably lead to intensified competition, including the construction industry. To enhance the competitiveness of construction enterprises in the industry, a multi-objective optimization model for construction project management has been proposed. At the same time, carbon emission was included as one of the optimization objectives in the experiment. This can also align the construction industry with the concept of modern green development. A non-dominated sorting genetic a

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12

Yang, Xiaoying, Wanli Zhang, and Qixiang Song. "An Improved DV-Hop Algorithm Based on Shuffled Frog Leaping Algorithm." International Journal of Online Engineering (iJOE) 11, no. 9 (2015): 17. http://dx.doi.org/10.3991/ijoe.v11i9.5059.

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According to that node localization accuracy is not high in the DV Hop localization algorithm, shuffled frog leaping algorithm with many advantages such as the convergence speed is fast, easy to realize and excellent performance of global optimization and so on is introduced into the design of DV-Hop algorithm. A new DV-Hop algorithm based on shuffled frog leaping algorithm (Shuffled Frog Leaping DV-Hop Algorithm, SF LA DV-Hop) is proposed in this paper. Based on traditional DV-Hop algorithm, the new algorithm used distance of nodes and position information of anchor nodes to establish objecti

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13

Hu, Bibo. "Shuffled frog leaping algorithm based on quantum rotation angle." MATEC Web of Conferences 309 (2020): 03012. http://dx.doi.org/10.1051/matecconf/202030903012.

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Анотація:

In this paper, through the analysis of the artificial intelligence algorithm, shuffled frog leaping algorithm is effectively improved, and the position of the frog is determined by the quantum rotation angle, so as to improve the performance of the algorithm. Compared with the artificial bee colony algorithm and the shuffled frog leaping algorithm, the improved algorithm has a significant improvement in the convergence speed of the algorithm and the ability to jump out of the local area.

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14

Ling, Jeeng Min, and Anh Son Khuong. "Modified Shuffled Frog-Leaping Algorithm on Optimal Planning for a Stand-Alone Photovoltaic System." Applied Mechanics and Materials 145 (December 2011): 574–78. http://dx.doi.org/10.4028/www.scientific.net/amm.145.574.

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Анотація:

Shuffled frog leaping algorithm (SFLA) is a new meta-heuristic evolutionary algorithm with simple algorithm and effective calculation speed. It performs stochastic searching process that mimics natural biological evolution and the social behavior of species. SFLA conducts its formulation from two main methods, the local searching validated by particle swarm optimization and the competitiveness mixing of information implemented by shuffled complex algorithm. A modified shuffled frog leaping algorithm (MSFLA) is investigated that improves the leaping rule by properly extending the leaping step s

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15

Jiang, Jianguo. "An Improved Shuffled Frog Leaping Algorithm." Journal of Information and Computational Science 10, no. 6 (2013): 1665–73. http://dx.doi.org/10.12733/jics20101552.

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16

Jiang, Jianguo. "An Improved Shuffled Frog Leaping Algorithm." Journal of Information and Computational Science 10, no. 14 (2013): 4619–26. http://dx.doi.org/10.12733/jics20102191.

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17

Liu, Chen. "An Adaptive Shuffled Frog Leaping Algorithm." Journal of Information and Computational Science 12, no. 17 (2015): 6621–28. http://dx.doi.org/10.12733/jics20107099.

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18

Li, Zuo Yong, Chun Xue Yu, and Zheng Jian Zhang. "Optimal Algorithm of Shuffled Frog Leaping Based on Immune Evolutionary Particle Swarm Optimization." Advanced Materials Research 268-270 (July 2011): 1188–93. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1188.

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Анотація:

In order to avoid premature convergence and improve the precision of solution using basic shuffled frog leaping algorithm (SFLA), based on immune evolutionary particle swarm optimization, a new shuffled frog leaping algorithm was proposed. The proposed algorithm integrated the global search mechanism in the particle swarm optimization (PSO) into SFLA, so as to search thoroughly near by the space gap of the worst solution, and also integrated the immune evolutionary algorithm into SFLA making immune evolutionary iterative computation to the optimal solution in the sub-swarm, so as to use the in

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19

Ahandani, Morteza Alinia, and Hosein Alavi-Rad. "Hybridizing Shuffled Frog Leaping and Shuffled Complex Evolution Algorithms Using Local Search Methods." International Journal of Applied Evolutionary Computation 5, no. 1 (2014): 30–51. http://dx.doi.org/10.4018/ijaec.2014010103.

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Анотація:

In this research, a study was carried out to exploit the hybrid schemes combining two classical local search techniques i.e. Nelder–Mead simplex search method and bidirectional random optimization with two meta-heuristic methods i.e. the shuffled frog leaping and the shuffled complex evolution, respectively. In this hybrid methodology, each subset of meta-heuristic algorithms is improved by a hybrid strategy that is combined from evolutionary process of each subset in related algorithm and a local search method. These hybrid algorithms are evaluated on low and high dimensional continuous bench

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20

Tang, Zhe, and Ke Luo. "K-Means Clustering Algorithm Method Based on Shuffled Frog Leaping Algorithm." Advanced Materials Research 989-994 (July 2014): 2245–49. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2245.

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Aiming to resolve the problems of the traditional k-means clustering algorithm such as random selecting of initial clustering centers,the low efficiency of clustering,low in the real,this paper proposed a novel k-means clustering algorithm method based on shuffled frog leaping algorithm.This algorithm combined the advantages of k-means algorithm and shunffled forg leaping algorithm.A chaotic local search was introduced to improve the quality of the initial individual,a new searching strategy was presented to update frog position,that increased the optimization ability of algorithm.According to

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21

Dr., K. Lenin. "DECREASE OF REAL POWER LOSS BY ADAPTED ALGORITHM." International Journal of Research - Granthaalayah 6, no. 8 (2018): 41–50. https://doi.org/10.5281/zenodo.1403795.

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In this paper, Adapted Flower Pollination (AFP) algorithm is proposed to solve the optimal reactive power problem. Flower pollination algorithm has been improved by comprising of the elements of chaos theory, Shuffled frog leaping search and Levy Flight. In the AFP algorithm, the initial population is generated using the circle map, frog leaping local search is performed by each solution and when rand>p, modified Levy flight with integration of inertia weight in global pollination is performed on that particular solution. Proposed AFP algorithm has been tested in standard IEEE 57 bus test s

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22

Tang, Deyu, Jie Zhao, Jin Yang, Zhen Liu, and Yongming Cai. "An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications." Computational Intelligence and Neuroscience 2021 (December 14, 2021): 1–31. http://dx.doi.org/10.1155/2021/8928182.

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Анотація:

Shuffled frog leaping algorithm, a novel heuristic method, is inspired by the foraging behavior of the frog population, which has been designed by the shuffled process and the PSO framework. To increase the convergence speed and effectiveness, the currently improved versions are focused on the local search ability in PSO framework, which limited the development of SFLA. Therefore, we first propose a new scheme based on evolutionary strategy, which is accomplished by quantum evolution and eigenvector evolution. In this scheme, the frog leaping rule based on quantum evolution is achieved by two

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23

Sharma, Shweta, Tarun K. Sharma, Millie Pant, J. Rajpurohit, and B. Naruka. "Centroid Mutation Embedded Shuffled Frog-Leaping Algorithm." Procedia Computer Science 46 (2015): 127–34. http://dx.doi.org/10.1016/j.procs.2015.02.003.

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24

Tang, Deyu, Zhen Liu, Jin Yang, and Jie Zhao. "Memetic frog leaping algorithm for global optimization." Soft Computing 23, no. 21 (2018): 11077–105. http://dx.doi.org/10.1007/s00500-018-3662-3.

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25

Zhao, Jia, and Li Lv. "Two-Phases Learning Shuffled Frog Leaping Algorithm." International Journal of Hybrid Information Technology 8, no. 5 (2015): 195–206. http://dx.doi.org/10.14257/ijhit.2015.8.5.22.

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26

Saud, Suhair, Halife Kodaz, and İsmail Babaoğlu. "Solving Travelling Salesman Problem by Using Optimization Algorithms." KnE Social Sciences 3, no. 1 (2018): 17. http://dx.doi.org/10.18502/kss.v3i1.1394.

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Анотація:

This paper presents the performances of different types of optimization techniques used in artificial intelligence (AI), these are Ant Colony Optimization (ACO), Improved Particle Swarm Optimization with a new operator (IPSO), Shuffled Frog Leaping Algorithms (SFLA) and modified shuffled frog leaping algorithm by using a crossover and mutation operators. They were used to solve the traveling salesman problem (TSP) which is one of the popular and classical route planning problems of research and it is considered as one of the widely known of combinatorial optimization. Combinatorial optimizatio

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27

Wang, Hongbo, Xiaoxiao Zhen, and Xuyan Tu. "SFDE: Shuffled Frog-Leaping Differential Evolution and Its Application on Cognitive Radio Throughput." Wireless Communications and Mobile Computing 2019 (March 4, 2019): 1–18. http://dx.doi.org/10.1155/2019/2965061.

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Differential Evolution (abbreviation for DE) is showing many advantages in solving optimization problems, such as fast convergence, strong robustness, and so on. However, when DE faces a complex target space, the diversity of its population will degenerate in a small scope; even sometimes it is premature to fall into the local minimum. All things contend in beauty in the world; a Shuffled Frog Leaping Algorithm (abbreviation for SFLA) has a strong global ability; unfortunately, its convergence speed is also slow. In order to overcome the shortcoming, this article suggests a Shuffled Frog-leapi

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28

Ma, Yong Chun, and Hua Gang Shao. "Wireless Sensor Network Routing Optimization Based on Improved Shuffled Frog Leaping Algorithm." Applied Mechanics and Materials 681 (October 2014): 253–57. http://dx.doi.org/10.4028/www.scientific.net/amm.681.253.

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Against path optimization problem for wireless sensor network, this paper proposes a path optimization strategy for wireless sensor network based on improved shuffled frog leaping algorithm. The shuffled frog leaping algorithm was used as wireless sensor network path optimization main frame, gauss mutation and opposition-based learning were used to overcome the defects of easily trapping into local optimum and low accuracy computation. Simulation results show that the route optimization mechanism can effectively prolongs the network lifetime,reduces energy consumption, and improves the overall

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29

Feng, Yanhong, Gai-Ge Wang, Qingjiang Feng, and Xiang-Jun Zhao. "An Effective Hybrid Cuckoo Search Algorithm with Improved Shuffled Frog Leaping Algorithm for 0-1 Knapsack Problems." Computational Intelligence and Neuroscience 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/857254.

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Анотація:

An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of Lévy flights and fr

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30

Zhang, Jing Min, and Cong Cong Wu. "Improved Shuffled Frog-Leaping Algorithm and its Application." Applied Mechanics and Materials 155-156 (February 2012): 92–96. http://dx.doi.org/10.4028/www.scientific.net/amm.155-156.92.

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This paper proposes an improved shuffled frog-leaping algorithm, the algorithm improves the subpopulation frog individual optimization way which is not just the worst individual optimization. "Guide optimal" probability and "guide suboptimal" probability are put forward. The experiments results of multiple problems of the TSPLIB show that the algorithm is feasible and effective.

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31

Cui, Yu Juan, Hao Cha, and Bin Tian. "Cultural Shuffled Frog Leaping Algorithm and its Applications for the Radar Network Deployment." Applied Mechanics and Materials 624 (August 2014): 516–19. http://dx.doi.org/10.4028/www.scientific.net/amm.624.516.

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The substance of the deployment of radar network is a multi-parameter optimization problem. This paper presents an objective function to deploy the radar network and a shuffled frog leaping algorithm (SFLA) is proposed to implement the radar network deployment. The proposed cultural shuffled frog leaping algorithm (CSFLA) makes use of mechanism of cultural evolution to update the locations of cultural frogs. Simulation results show that the proposed CSFLA has stronger abilities of exploitation and exploration by designing new leaping equations based on knowledge strategy and information commun

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32

Cui, Yu Juan. "New Method of Optimal Deployment of Radar Network." Applied Mechanics and Materials 716-717 (December 2014): 1043–46. http://dx.doi.org/10.4028/www.scientific.net/amm.716-717.1043.

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Анотація:

In order to improve the detection performance with the limited radar resources in the given defense zone, it is necessary to make reasonable and effective deployment of radar network. According to the principles of radar network deployment, its mathematical model is established at the beginning. And then, on the base of the shuffled frog leaping algorithm (SFLA) principle, a new method-improved shuffled frog leaping algorithm (ISFLA) is introduced through the technique of algorithm hybrid. By integrating updated strategy and chaotic optimization into SFLA, it greatly enhances the local searchi

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33

Xu, Li Qun, and Ling Li. "Inversion Analysis of Seepage Parameters Based on Improved Shuffled Frog Leaping Algorithm." Mathematical Problems in Engineering 2021 (October 12, 2021): 1–11. http://dx.doi.org/10.1155/2021/6536294.

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Анотація:

Aiming at overcoming the defects such as slow searching speed and easily trapping into local extremum at anaphase of the shuffled frog leaping algorithm (SFLA), based on the Evolutionary Exploration strategy, a more effective shuffled frog leaping algorithm, Improved Shuffled Frog Leaping Algorithm (ISFLA), which can be applied to the inverse analysis of seepage parameters to dams, is proposed. With the introduction of the threshold value selection in the local search of the original initial population to improve the best frogs in memeplex, the improved algorithm overcomes the shortcomings of

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34

Khosravian, Mohsen, Mohammad Ebrahim Pourzarandi, and Jalal Haghighat Monfared. "Identification and Analysis of Supply Chain and Supplier Risks Using Frog-Leaping and Genetic Algorithms (A Case Study in the Automotive Industry)." International Journal of Innovation Management and Organizational Behavior 4, no. 1 (2024): 82–90. http://dx.doi.org/10.61838/kman.ijimob.4.1.10.

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Анотація:

Objective: The increasing complexity of the production process, the use of machines with advanced and modern capabilities, and the growing demand for products compel industry owners to maximize their capabilities at minimal costs towards production, reduce the risk of product manufacturing, and also improve the quality of their manufactured products to access broader markets. One of the most important factors in achieving this goal is the assessment of risk in product manufacturing. Given the financial difficulties in the automotive industry, supply chain management is of high importance becau

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35

Sun, Changlong, Zhengzong Wang, Dongwan Lu, et al. "An Energy Efficient and Reliable Multipath Transmission Strategy for Mobile Wireless Sensor Networks." Computational Intelligence and Neuroscience 2022 (August 9, 2022): 1–16. http://dx.doi.org/10.1155/2022/8083804.

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Multipath data transmission is a key problem that needs to be solved urgently in wireless sensor networks. In this paper, sensor node failure, link failure, energy exhaustion, and external interference affect the stability and reliability of network data transmission. A multipath transmission strategy for wireless sensor networks based on improved shuffled frog leaping algorithm is proposed. A mathematical model of multipath transmission in wireless sensor networks is established. In the shuffled frog leaping algorithm, combined with the transition probability in the particle swarm optimizatio

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36

Alhenak, Lubna, and Manar Hosny. "Genetic–Frog-leaping Algorithm for Text Document Clustering." Computers, Materials & Continua 61, no. 3 (2019): 1045–74. http://dx.doi.org/10.32604/cmc.2019.08355.

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37

Luo, Jian-ping, Xia Li, and Min-rong Chen. "Improved Shuffled Frog Leaping Algorithm for Solving CVRP." Journal of Electronics & Information Technology 33, no. 2 (2011): 429–34. http://dx.doi.org/10.3724/sp.j.1146.2010.00328.

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Lin, Juan, and Yiwen Zhong. "Accelerated Shuffled Frog-leaping Algorithm with Gaussian Mutation." Information Technology Journal 12, no. 23 (2013): 7391–95. http://dx.doi.org/10.3923/itj.2013.7391.7395.

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Sharifipour, Milad, Ali Nakhaee, Reza Yousefzadeh, and Mojtaba Gohari. "Well placement optimization using shuffled frog leaping algorithm." Computational Geosciences 25, no. 6 (2021): 1939–56. http://dx.doi.org/10.1007/s10596-021-10094-7.

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Shao Mingsheng, 邵明省, and 王其华 Wang Qihua. "Blurred Image Restoration Based on Frog Leaping Algorithm." Laser & Optoelectronics Progress 49, no. 2 (2012): 021003. http://dx.doi.org/10.3788/lop49.021003.

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ZHAO, Peng-jun. "Shuffled frog leaping algorithm based on differential disturbance." Journal of Computer Applications 30, no. 10 (2010): 2575–77. http://dx.doi.org/10.3724/sp.j.1087.2010.02575.

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Zhao, Jia, Min Hu, Hui Sun, and Li Lv. "Shuffled frog leaping algorithm based on enhanced learning." International Journal of Intelligent Systems Technologies and Applications 15, no. 1 (2016): 63. http://dx.doi.org/10.1504/ijista.2016.076099.

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Sharma, Tarun Kumar, and Millie Pant. "Opposition based learning ingrained shuffled frog-leaping algorithm." Journal of Computational Science 21 (July 2017): 307–15. http://dx.doi.org/10.1016/j.jocs.2017.02.008.

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Hasanien, Hany M. "Shuffled Frog Leaping Algorithm for Photovoltaic Model Identification." IEEE Transactions on Sustainable Energy 6, no. 2 (2015): 509–15. http://dx.doi.org/10.1109/tste.2015.2389858.

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Amiri, Babak, Mohammad Fathian, and Ali Maroosi. "Application of shuffled frog-leaping algorithm on clustering." International Journal of Advanced Manufacturing Technology 45, no. 1-2 (2009): 199–209. http://dx.doi.org/10.1007/s00170-009-1958-2.

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Lenin, K. "DECREASE OF REAL POWER LOSS BY ADAPTED ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 8 (2018): 41–50. http://dx.doi.org/10.29121/granthaalayah.v6.i8.2018.1260.

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In this paper, Adapted Flower Pollination (AFP) algorithm is proposed to solve the optimal reactive power problem. Flower pollination algorithm has been improved by comprising of the elements of chaos theory, Shuffled frog leaping search and Levy Flight. In the AFP algorithm, the initial population is generated using the circle map, frog leaping local search is performed by each solution and when rand>p, modified Levy flight with integration of inertia weight in global pollination is performed on that particular solution. Proposed AFP algorithm has been tested in standard IEEE 57 bus test s

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Aghdam, K. M., I. Mirzaee, N. Pourmahmood, and M. P. Aghababa. "Design of Water Distribution Networks via a Novel Fractional Succedaneum Shuffled Frog Leaping Method." Journal of Mechanics 31, no. 4 (2015): 369–80. http://dx.doi.org/10.1017/jmech.2014.94.

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AbstractIt is known that the design problem of water distribution networks (WDNs) is an NP-hard combinatorial problem which cannot be easily solved using traditional mathematical numerical methods. On the other hand, fractional calculus, which is a generalization of the classical integer-order calculus, involves some interesting features such as having memory of past events. Based on the concept of fractional calculus, this paper proposes the use of a new version of heuristic shuffled frog leaping (SFL) algorithm for solving the optimal design problem of WDNs. In order to increase the converge

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Mosa, Afrah U., and Waleed A. Mahmoud Al-Jawher. "Image Fusion Algorithm using Grey Wolf optimization with Shuffled Frog Leaping Algorithm." International Journal of Innovative Computing 13, no. 1-2 (2023): 1–5. http://dx.doi.org/10.11113/ijic.v13n1-2.412.

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Data fusion is a “formal framework in which are expressed the means and tools for the alliance of data originating from different sources.” It aims at obtaining information of greater quality; the exact definition of 'greater quality will depend upon the application. It is a famous technique in digital image processing and is very important in medical image representation for clinical diagnosis. Previously many researchers used many meta-heuristic optimization techniques in image fusion, but the problem of local optimization restricted their searching flow to find optimum search results. In th

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B., Pavithra, and Dr Niranjananmurthy M. "Linear Regression Feature and Frog Leaping Algorithm based Web Page Recommendation." International Journal of Innovative Technology and Exploring Engineering 12, no. 1 (2022): 32–37. http://dx.doi.org/10.35940/ijitee.a9381.1212122.

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Website content and services attract surfers to visit page. Random visitor or first time visitor need more user suggestion for increasing the retaining of user. This work has worked in field of web page prediction as per user previous visits. Web mining logs and content features were further processed to extract the linear regression feature from the work. Extracted features were used for the page prediction in testing phase. Frog leaping genetic algorithm was used for the population generation and possible page prediction. Experiment was done on real dataset extracted from projecttunnel.com w

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Pavithra., B., and Niranjananmurthy M. Dr. "Linear Regression Feature and Frog Leaping Algorithm based Web Page Recommendation." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 12, no. 1 (2022): 32–37. https://doi.org/10.35940/ijitee.A9381.1212122.

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Анотація:

<strong>Abstract: </strong>Website content and services attract surfers to visit page. Random visitor or first time visitor need more user suggestion for increasing the retaining of user. This work has worked in field of web page prediction as per user previous visits. Web mining logs and content features were further processed to extract the linear regression feature from the work. Extracted features were used for the page prediction in testing phase. Frog leaping genetic algorithm was used for the population generation and possible page prediction. Experiment was done on real dataset extract

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