An Exploration Technique for the Ant Colony System Optimization Framework

2011 ◽  
Author(s):  
Zhimeng Lv ◽  
Yong Chen
Keyword(s):  
System Optimization ◽  
Ant Colony ◽  
Ant Colony System ◽  
Optimization Framework

scholarly journals Suspicious Activity Recognition Using Proposed Deep L4-Branched-ActionNet with Entropy Coded Ant Colony System Optimization

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Tanzila Saba ◽  
Amjad Rehman ◽  
Rabia Latif ◽  
Suliman Mohamed Fati ◽  
Mudassar Raza ◽  
...  
Keyword(s):  
Activity Recognition ◽  
System Optimization ◽  
Ant Colony ◽  
Ant Colony System ◽  
Suspicious Activity

The ant colony system: optimization for the logistics of marine cargo in the Aegean

2005 ◽  
Vol 8 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Nikolaos Alexandris ◽  
Chrysostomos Fountas ◽  
Aristidis Vlachos
Keyword(s):  
System Optimization ◽  
Ant Colony ◽  
Ant Colony System

scholarly journals Ant Colony System Optimization for Spatiotemporal Modelling of Combined EEG and MEG Data

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 329
Author(s):  
Eugene A. Opoku ◽  
Syed Ejaz Ahmed ◽  
Yin Song ◽  
Farouk S. Nathoo
Keyword(s):  
Interval Estimation ◽  
Real Data ◽  
System Optimization ◽  
Ant Colony ◽  
Ant Colony System ◽  
Nonparametric Bootstrap ◽  
Spatiotemporal Model ◽  
Iterated Conditional Modes ◽  
Data Application ◽  
Improved Performance

Electroencephalography/Magnetoencephalography (EEG/MEG) source localization involves the estimation of neural activity inside the brain volume that underlies the EEG/MEG measures observed at the sensor array. In this paper, we consider a Bayesian finite spatial mixture model for source reconstruction and implement Ant Colony System (ACS) optimization coupled with Iterated Conditional Modes (ICM) for computing estimates of the neural source activity. Our approach is evaluated using simulation studies and a real data application in which we implement a nonparametric bootstrap for interval estimation. We demonstrate improved performance of the ACS-ICM algorithm as compared to existing methodology for the same spatiotemporal model.

scholarly journals Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110192
Author(s):  
Songcan Zhang ◽  
Jiexin Pu ◽  
Yanna Si ◽  
Lifan Sun
Keyword(s):  
Path Planning ◽  
Convergence Rate ◽  
Mobile Robot ◽  
Local Optimization ◽  
Ant Colony ◽  
Ant Colony System ◽  
Geometric Features ◽  
Robot Path Planning ◽  
Computation Efficiency ◽  
Robot Path

Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms.

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