Relevant bibliographies by topics / Frog Leaping Algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Frog Leaping Algorithm'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Frog Leaping Algorithm"

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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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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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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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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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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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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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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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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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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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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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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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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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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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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.

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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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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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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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Dissertations / Theses on the topic "Frog Leaping Algorithm"

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Chung, Gunhui. "Water Supply System Management Design and Optimization under Uncertainty." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/195506.

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Increasing population, diminishing supplies and variable climatic conditions can cause difficulties in meeting water demands. When this long range water supply plan is developed to cope with future water demand changes, accuracy and reliability are the two most important factors. To develop an accurate model, the water supply system has become more complicated and comprehensive structures. Future uncertainty also has been considered to improve system reliability as well as economic feasibility.In this study, a general large-scale water supply system that is comprised of modular components was
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Lamboia, Fabiany. "Modelo de otimização multiobjetivo baseado em algoritmo Shuffled Frog Leaping para transporte de produtos em redes de dutos." Universidade Tecnológica Federal do Paraná, 2015. http://repositorio.utfpr.edu.br/jspui/handle/1/2029.

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ANP; FINEP; MCT<br>A modelagem de sistemas envolvidos no gerenciamento das operações de uma rede de dutos é um problema de otimização que envolve complexas restrições operacionais. O transporte por meio de dutos mostra-se confiável e econômico, principalmente para grandes volumes. Porém, a elevada taxa de ocupação das redes de distribuição e a quantidade de diferentes produtos que devem ser transportados sob condições operacionais diferenciadas levam a cenários operacionais complexos. Uma melhoria na eficiência do transporte de produtos através de redes de dutos pode ser obtida por uma melhor
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Xu, Fu-Yi, and 徐富翊. "Adaptive Shuffled Frog-Leaping Algorithm for Motion Estimation." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/66178773075295548249.

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碩士<br>國立屏東商業技術學院<br>資訊工程系(所)<br>101<br>In MPEG and H.26X series, Block Matching Algorithm is used for finding the motion vector between frame and frame. The Full Search Algorithm (FS) retrieves the best video quality, but the computation is too complex. In order to maintain video quality and reduce search points, Fast Block Matching Algorithms such as Three-Step Search algorithm (TSS), Diamond Search algorithm (DS), Hexagon-based Search algorithm (HEXS) are proposed. In this paper, Shuffled Frog-Leaping Algorithm (SFLA) is proposed based on the evolution of the new algorithm, the initial searc
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Ku, Cheng-Hua, and 辜振華. "Digital Filter Design Using an Improved Shuffled Frog Leaping Algorithm." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/12083795841166650101.

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碩士<br>樹德科技大學<br>電腦與通訊系碩士班<br>104<br>In this thesis, we employ an improved shuffled frog leaping algorithm (ISLFA) to solve two different digital filter design issues. Shuffled frog leaping algorithm is a meta-heuristic optimization method that mimics a group of frogs for seeking food. It combines the advantages of the Genetic-based Memetic Algorithm (MA) and social behavior-based Particle Swarm Optimization (PSO) algorithm. A new mechanism to the general frog leaping algorithm is developed to enhance the search efficiency of the algorithm. Based on using the developed algorithm, two differ
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Hsu, Chih-Chien, and 許志鍵. "The Application of Shuffled Frog Leaping Algorithm on Water Distribution Network." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/04029841590681189638.

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碩士<br>中興大學<br>土木工程學系所<br>95<br>Heuristic algorithms has developed rapidly in recent ten years and effective application on various kinds of great yardstick , high dispersed and complicated problem , compared with traditional optimum method , it''s a lot of excellent . So heuristic algorithms in order to look for more excellent , research this with shuffle frogs leaping algorithm was that for goal of revising in recent years and enhance this algorithm , test out the parameter set up way with the functional equation in the article, combine the hardy cross method to apply on pipe network system
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Li, Pei-Yu, and 李佩諭. "Application of Shuffled Frog Leaping Algorithm for the Power Dispatch of Mircogrid." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/552jyf.

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碩士<br>正修科技大學<br>電機工程研究所<br>106<br>This thesis performs the economic dispatch and security dispatch for smart micro-grids by considering the renewable energy and battery storage system. The operating models including wind power generation, solar power generation, battery storage systems, combined cooling, heating, and power(CCHP), etc., are first determined. Based on all technical constraints, the optimal dispatch model of mixed-power generations is formulated by considering the charging/dis-charging scheduling of battery storage systems. Secondly, this thesis uses the local search of frogs of
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Hong, Tzu-Cheng, and 洪梓誠. "Applying a modified shuffled frog leaping algorithm to solve medical staff scheduling problems." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/34700944215770925895.

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碩士<br>中原大學<br>工業與系統工程研究所<br>104<br>As people keep pursuing for better medical quality, medical demands of people increase in recent years. Due to limited medical staff of hospitals, each medical employee has more and more workload. If a hospital does not assign the appropriate staff to each shift, it will cause to over workload of each staff, leading to decrease medical quality. Therefore, how to arrange a medical staff schedule with high staff satisfaction to comply with government and hospital regulations becomes a significant challange for hospital management. This study developed a modifie
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Cheng, Yi-Chi, and 程衣淇. "A Rainfall-runoff Model Based on the Neural Network Incorporating Shuffled Frog Leaping Algorithm." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/v6gge8.

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碩士<br>國立中興大學<br>土木工程學系所<br>100<br>Taiwan is a subtropical island climate. Average annual rainfall is mostly 2,500mm, this record is 2.6 times of the world which have about 80% concentrate at typhoon season. Rainfall is strong and the steep topography. Damage often happen. Rainfall time of concentration is very short. Rainfall caused indelible disaster. Use rainfall time of concentration predict downstream runoff. Hence, an efficient and high accuracy rainfall-runoff model is very important. In this study, the artificial neural network (ANN) could be building quickly and accurately on the rainf
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Chen, Gu-Yang, and 陳谷暘. "A Comparison of Artificial Bee Colony Algorithm and Shuffled Frog Leaping Algorithm for the Numerical Optimization and 3-D Otsu Threshold Selection." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/03597785312561050125.

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碩士<br>國立屏東大學<br>資訊科學系碩士班<br>103<br>In the first part of this work, two nature inspired algorithms are used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by Shuffled Frog Leaping Algorithm (SFLA). In the second part of this work, two threshold selection methods are proposed to speed up the original 3-D Otsu thresholding for image segmentation based on SFLA and ABCA, respectively. In order to compare the performance of the proposed ABC algorithm with SFLA, five classical benchmark functions are used for te
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Elbehairy, Hatem. "Bridge Management System with Integrated Life Cycle Cost Optimization." Thesis, 2007. http://hdl.handle.net/10012/3017.

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In recent years, infrastructure renewal has been a focus of attention in North America and around the world. Municipal and federal authorities are increasingly recognizing the need for life cycle cost analysis of infrastructure projects in order to facilitate proper prioritization and budgeting of maintenance operations. Several reports have highlighted the need to increase budgets with the goal of overcoming the backlog in maintaining infrastructure facilities. This situation is apparent in the case of bridge networks, which are considered vital links in the road network infrastructure. Becau
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Book chapters on the topic "Frog Leaping Algorithm"

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Badar, Altaf Q. H. "Shuffled Frog Leaping Algorithm." In Evolutionary Optimization Algorithms. CRC Press, 2021. http://dx.doi.org/10.1201/9781003206477-7.

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Zolghadr-Asli, Babak. "Shuffled Frog-Leaping Algorithm." In Computational Intelligence-based Optimization Algorithms. CRC Press, 2023. http://dx.doi.org/10.1201/9781003424765-10.

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Kong, Lingping, Jeng-Shyang Pan, Shu-Chuan Chu, and John F. Roddick. "Directional Shuffled Frog Leaping Algorithm." In Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70730-3_31.

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Kaveh, Ali, and Hossein Yousefpoor. "Chaotic Shuffled Frog Leaping Algorithm." In Chaotic Meta-heuristic Algorithms for Optimal Design of Structures. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-48918-1_10.

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Sharma, Shweta, Tarun Kumar Sharma, Millie Pant, Jitendra Rajpurohit, and Bhagyashri Naruka. "Accelerated Shuffled Frog-Leaping Algorithm." In Advances in Intelligent Systems and Computing. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2220-0_15.

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Naruka, Bhagyashri, Tarun Kumar Sharma, Millie Pant, Shweta Sharma, and Jitendra Rajpurohit. "Differential Shuffled Frog-leaping Algorithm." In Advances in Intelligent Systems and Computing. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2220-0_20.

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Sharma, Pragya, Nirmala Sharma, and Harish Sharma. "Locally Informed Shuffled Frog Leaping Algorithm." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3322-3_13.

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Sahu, Padma Charan, Sunita Panda, Siba Prasada Panigrahi, and K. Rarvathi. "RBFNN Equalizer Using Shuffled Frog-Leaping Algorithm." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5687-1_49.

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Rajpurohit, Jitendra, Tarun Kumar Sharma, and Atulya K. Nagar. "Shuffled Frog Leaping Algorithm with Adaptive Exploration." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0448-3_49.

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Xu, Honglong, Gang Liu, Minhua Lu, and Rui Mao. "A Least Random Shuffled Frog-Leaping Algorithm." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54924-3_39.

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Conference papers on the topic "Frog Leaping Algorithm"

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He, Zhuangli, Lei Zhang, Bo Chen, Rui Qv, Yunji Ye, and Min Li. "Warehouse task scheduling based on improved frog leaping algorithm." In 2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE). IEEE, 2024. https://doi.org/10.1109/icemce64157.2024.10862864.

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Yao, Wang, Zhong Chen, Zhonglin Xu, and Yongkang Wu. "The Improved Frog Leaping Algorithm for Photovoltaic Array Parameter Identification." In 2024 5th International Conference on Clean Energy and Electric Power Engineering (ICCEPE). IEEE, 2024. https://doi.org/10.1109/iccepe62686.2024.10931423.

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Sharma, Pragya, Nirmala Sharma, and Harish Sharma. "Elitism based Shuffled Frog Leaping algorithm." In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2016. http://dx.doi.org/10.1109/icacci.2016.7732142.

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Naruka, Bhagyashri, Tarun K. Sharma, Millie Pant, Jitendra Rajpurohit, and Shweta Sharma. "Two-phase shuffled frog-leaping algorithm." In 2014 3rd International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions). IEEE, 2014. http://dx.doi.org/10.1109/icrito.2014.7014716.

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Wang, Lianguo, and Yaxing Gong. "A fast shuffled frog leaping algorithm." In 2013 9th International Conference on Natural Computation (ICNC). IEEE, 2013. http://dx.doi.org/10.1109/icnc.2013.6818003.

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Wang, Lianguo, and Yaxing Gong. "Quantum Binary Shuffled Frog Leaping Algorithm." In 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC). IEEE, 2013. http://dx.doi.org/10.1109/imccc.2013.366.

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Luo, Xue-hui, Ye Yang, and Xia Li. "Solving TSP with Shuffled Frog-Leaping Algorithm." In 2008 Eighth International Conference on Intelligent Systems Design and Applications. IEEE, 2008. http://dx.doi.org/10.1109/isda.2008.346.

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Sharma, Pragya, Nirmala Sharma, and Harish Sharma. "Binomial crossover embedded shuffled frog leaping algorithm." In 2016 International Conference on Computing, Communication and Automation (ICCCA). IEEE, 2016. http://dx.doi.org/10.1109/ccaa.2016.7813737.

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Li, Xin, Xu Qian, Junlin Jiang, and Ziqiang Wang. "Shuffled Frog Leaping Algorithm for Materialized Views Selection." In 2010 Second International Workshop on Education Technology and Computer Science. IEEE, 2010. http://dx.doi.org/10.1109/etcs.2010.480.

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Bhaduri, Antariksha. "A Clonal Selection Based Shuffled Frog Leaping Algorithm." In 2009 IEEE International Advance Computing Conference (IACC 2009). IEEE, 2009. http://dx.doi.org/10.1109/iadcc.2009.4808993.

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