Flowchart of Be-ACO algorithm. 4. Experiment and Analysis 4.1. Simulation Experiment Environment and Parameter Setting The type of experimental computer is HP880G1, ACPI ×64-based PC; processor is Intel(R) Core
Investigation of Code Complexity of an Innovative Algorithm Based on ACO in Weighted Graph Traversing and Compare it to Traditional ACO and Bellman-Ford[J]. Journal of Bioinforxnatics and Intelligent Control, 2013, 2(1): 73-78.Investigation of Code Complexity of an Innovative Algor...
Key words ：feature selection ；ant colony optimization （ACO ）algorithm ；relevance vector machine （RVM ）；flow regime identification 1引言流型识别主要包括特征信号的采集、特征提取和分类器的选择，其中特征提取是流型识别中一个非常重要的环节。针对压差波动信号的非线性和非平稳性特征，目前已有分形、小波包...
Figure 2. Flowchart of the ACO Algorithm. 3.2. DDQN Algorithm The DDQN algorithm is a deep reinforcement learning algorithm based on value functions, which aims to solve the high estimation problem in the traditional DQN algorithm. By introducing the goal network mechanism, the DDQN algorithm fir...
The flowchart of the optical flow algorithm used to extract features is shown in Figure 1. Figure 1. Proposed method. ACO is inspired by the foraging behavior of ants, where they find the shortest path to a food source by laying down pheromones. The key steps in the ACO algorithm are ...
Figure 2. Flowchart of the ACO-BP algorithm. ACO-BP algorithm-specific steps: Step 1: Read the data, initialize the structure of the BP neural network and the parameters of the ant colony optimization algorithm, and set the parameter set 𝐼𝑃𝑖IPi. Step 2: Place each ant on the ...