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Zation algorithms are Genetic Algorithm (GA) 1, Ant Colony Algorithm (ACA) 2, Particle Swarm Optimi- zation (PSO) 3, Artificial Fish-Swarm Algorithm (AFSA) 4, and Shuffled Frog Leaping Algorithm (SFLA) 5. These bionic algorithms have become a research focus in the fields of intelligent optimization. Yazdani, D., Golyari, S., Meybodi, M.R.: A New Hybrid Algorithm for Optimization Based on Artificial Fish Swarm Algorithm and Cellular Learning Automata. In: 5th International Symposium on Telecommunication (IST), Tehran, pp. 932–937 (2010) Google Scholar. The following Matlab project contains the source code and Matlab examples used for swarmfish the artificial fish swarm algorithm. The AFSA includes five steps of operations: (1) behaviour selection, (2) searching behaviour, (3) swarming behaviour, (4) following behaviour and (5) bulletin. Abstract: In order to solve the pilot pollution problem and realize the low complexity pilot allocation in 5G massive multiple-input and multiple-output (MIMO) communication system, we propose an artificial fish swarm algorithm based pilot allocation scheme in this paper. The algorithm is an imitation of the natural fish activities. Through the search of the region related to the largest user. A new knowledge-based Artificial Fish-Swarm optimization algorithm (AFA) with crossover, CAFAC, is proposed to enhance the precision and combat the blindness of searching of the AFA.
A lightweight package for artificial intelligence
Project description
This is the ailearn AI algorithm package. It includes three modules: Swarm, RL and utils.In swarm module, particle swarm algorithm, artificial fish swarm algorithm and firefly algorithm are implemented.The evolution strategy and the commonly used function to be optimized to evaluate the intelligent algorithm are also implemented in this module.The RL module consists of two parts, the TabularRL part and the environment part.The TabularRL part integrates some classical reinforcement learning algorithms, including Q-learning, Q(lambda), Sarsa, Sarsa(lambda), Dyna-Q, etc.The environment part integrates some classic test environments of reinforcement learning, such as the frozen lake problem, cliffwalking problem, gridworld problem, etc.
Update history:2018.4.10 0.1.3 In the first version, particle swarm optimization and artificial fish swarm algorithm are implemented for the first time and integrated into pip for the first time.2018.4.16 0.1.4 The implementation of evolution strategy and evaluation module are added.2018.4.18 0.1.5 Added TabularRL module and Environment module.2018.4.19 0.1.8 The TabularRL module and environment module are integrated into RL module, the related description of the project is added, and the related protocol is updated.2018.4.25 0.1.9 The output information has been changed from Chinese to English, and some known errors have been updated.2019.1.15 0.2.0 The utils module is added, and some common functions are added, including distance measurement, evaluation function, PCA algorithm, mutual conversion between tag value and one hot code, Friedman detection, etc.; the NN module is added, and some common activation function and loss function are added; the swarm module algorithm is updated to make them update faster.2020.5.14 0.2.1 Simplify the code and delete the NN module. Some functions are added, such as t-test, Friedman test and so on. Add RL classic environment windy gridworld environment.
Other updates:1.
Project website:https://pypi.org/project/ailearn/https://github.com/axi345/ailearn/
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Artificial Fish Swarm Algorithm Download Mac Os
- Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: A survey. Computer Networks 38(4), 393–422 (2002)CrossRefGoogle Scholar
- Estrin, D., Culler, D., Pister, K., Sukhatme, G.: Connecting the physical world with pervasive networks. IEEE Pervasive Computing, 59–69 (January-March 2002)Google Scholar
- El-said, S.A., Hassanien, A.E.: Artificial Eye Vision Using Wireless Sensor Networks. In: Wireless Sensor Networks: Theory and Applications. CRC Press, Taylor and Francis Group (January 2013)Google Scholar
- Culler, D., Estrin, D., Strivastava, M.: Overview of Sensor Networks. IEEE Computer Society 37(8), 41–49 (2004)CrossRefGoogle Scholar
- Liao, W., Chang, K., Kedia, S.: An Object Tracking Scheme for Wireless Sensor Networks using Data Mining Mechanism. In: Proceedings of the Network Operations and Management Symposium, Maui, HI, USA, pp. 526–529 (2012)Google Scholar
- Ye, W., Heidemann, J., Estrin, D.: An energy-efficient MAC protocol for wireless sensor networks. In: Proceedings of the 21st Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 3, pp. 1567–1576 (2002)Google Scholar
- Wood, A., Stankovic, J., Virone, G., Selavo, L., Zhimin, H., Qiuhua, C., Thao, D., Yafeng, W., Lei, F., Stoleru, R.: Context-Aware wireless sensor networks for assisted living and residential monitoring. Network 22, 26–33 (2008)Google Scholar
- Fathy, M.E., Hussein, A.S., Tolba, M.F.: Fundamental matrix estimation: a study of error criteria. Pattern Recognition Letters 32(2), 383–391 (2011)CrossRefGoogle Scholar
- Siew, Z.W., Wong, C.H., Chin, C.S., Kiring, A., Teo, K.T.K.: Cluster Heads Distribution of Wireless Sensor Networks via Adaptive Particle Swarm Optimization. In: Fourth International Conference on Computational Intelligence, Communication Systems and Networks, pp. 78–83 (2012)Google Scholar
- Li, L.X., Shao, Z.J., Qian, J.X.: An Optimizing Method Based on Autonomous Animate: Fish Swarm Algorithm. In: Proceeding of System Engineering Theory and Practice, pp. 32–38 (2002)Google Scholar
- Xiao, L.: A Clustering Algorithm Based on Artificial Fish school. In: 2nd International Conference on Computer Engineering and Technology, Chengdu, pp. 766–769 (2010)Google Scholar
- Yazdani, D., Golyari, S., Meybodi, M.R.: A New Hybrid Algorithm for Optimization Based on Artificial Fish Swarm Algorithm and Cellular Learning Automata. In: 5th International Symposium on Telecommunication (IST), Tehran, pp. 932–937 (2010)Google Scholar
- Luo, Y., Zhang, J., Li, X.: The Optimization of PID Controller Parameters Based on Artificial Fish Swarm Algorithm. In: IEEE International Conference on Automation and Logistics, Jinan, pp. 1058–1062 (2007)Google Scholar
- Zhang, M., Shao, C., Li, M., Sun, J.: Mining Classification Rule with Artificial Fish Swarm. In: 6th World Congress on Intelligent Control and Automation, Dalian, pp. 5877–5881 (2006)Google Scholar
- Li, C.X., Ying, Z., JunTao, S., Qing, S.J.: Method of Image Segmentation Based on Fuzzy C-means Clustering Algorithm and Artificial Fish Swarm Algorithm. In: International Conference on Intelligent Computing and Integrated Systems (ICISS), Guilin (2010)Google Scholar
- Neshat, M., Adeli, A., Sepidnam, G., Sargolzaei, M., Toosi, A.N.: A Review of Artificial Fish Swarm Optimization Methods and Applications. International Journal on Smart Sensing and Intelligent Systems 5(1) (2012)Google Scholar
- Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless sensor networks. In: The Proceeding of the Hawaii International Conference System Sciences, Hawaii (January 2000)Google Scholar
- Manjeshwar, A., Agrawal, D.: TEEN: a Routing Protocol for Enhanced Efficient in Wireless Sensor Networks. In: Proceedings of the 15th International Parallel and Distributed Processing Symposium, San Francisco, pp. 2009–2015 (April 2001)Google Scholar
- Manjeshwar, A., Agrawal, D.P.: APTEEN: a hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In: Proceedings of the 2nd International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing, Ft. Lauderdale, FL (April 2002)Google Scholar
- Atakan, B., Akan, O.B., Tugcu, T.: Bio-inspired Communications in Wireless. In: Guide to Wireless Sensor Networks, ch. 26, pp. 659–687. Springer-Verlag London Limited (2009)Google Scholar
- Batra, N., Jain, A., Dhiman, S.: An Optimized Energy Efficient Routing Algorithm For Wireless Sensor Network. International Journal of Innovative Technology & Creative Engineering 1(5) (2011) ISSN: 2045-8711Google Scholar
- Krings, A.W., Sam Ma, Z.: Bio-Inspired Computing and Communication in Wireless Ad Hoc and Sensor Networks. Ad Hoc Networks 7(4), 742–755 (2009)CrossRefGoogle Scholar
- Selvakennedy, S., Sinnappan, S., Shang, Y.: A biologically-inspired clustering protocol for wireless sensor networks. Computer Communications 30, 2786–2801 (2007)CrossRefGoogle Scholar
- Juan, L., Chen, S., Chao, Z.: Ant System Based Anycast Routing in Wireless Sensor Networks. In: International Conference on Wireless Communications, Networking and Mobile Computing, pp. 2420–2423 (2007)Google Scholar
- Wang, C., Lin, Q.: Swarm intelligence optimization based routing algorithm for Wireless Sensor Networks. In: Proceedings of International Conference on Neural Networks and Signal Processing, pp. 136–141 (2008)Google Scholar