Abstract: The artificial fish swarm algorithm is a new bionic optimization algorithm, which focuses on constructing an optimization model of autonomous animats. Researches on it have been applied in many fields. This paper makes an in-depth study of the artificial fish swarm algorithm model, and tries to optimize and expand it for the use of. 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 novel quantum artificial fish swarm algorithm 29 based on the principles of quantum computing is proposed which can improve the global search ability and the convergence speed of the artificial fish swarm algorithm. Kongcun Zhu et al., 30 used the quantum rotation gate to update the position of the artificial swarm which can enable the AF.
Sustainability Article The Modified Artificial Fish Swarm Algorithm for Least-Cost Planning of a Regional Water Supply Network Problem Yi Liu 1,2, Zhengpeng Tao 1, Jie Yang 1,. and Feng Mao 1 1 Management School, Hangzhou Dianzi University, Hangzhou 310018, China 2 The Research Center of Information Technology & Economic and Social Development, Hangzhou Dianzi. Urban Taxi Intelligent Scheduling Based On Optimized Artificial Fish Swarm Algorithm - SuperXiang/OptimizedAFSA. Urban Taxi Intelligent Scheduling Based On Optimized Artificial Fish Swarm Algorithm - SuperXiang/OptimizedAFSA. Download the GitHub extension for Visual Studio and try again. 6 commits; Files.
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Released:
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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