Development of a Convolution-Based Multi-Directional and Parallel Ant Colony Algorithm Considering a Network with Dynamic Topology Changes

Abstract

While network path generation has been one of the representative Non-deterministic Polynomial-time (NP)-hard problems, changes of network topology invalidate the effectiveness of the existing metaheuristic algorithms. This research proposes a new and efficient path generation framework that considers dynamic topology changes in a complex network. In order to overcome this issue, Multi-directional and Parallel Ant Colony Optimization (MPACO) is proposed. Ant agents are divided into several groups and start at different positions in parallel. Then, Gaussian Process Regression (GPR)-based pheromone update method makes the algorithm more efficient. While the proposed MPACO algorithm is more efficient than the existing ACO algorithm, it is limited in a network with topological changes. In order to overcome the issue, the MPACO algorithm is modified to the Convolution MPACO (CMPACO) algorithm. The proposed algorithm uses the pheromone convolution method using a discrete Gaussian distribution. The proposed pheromone updating method enables the generation of a more efficient network path with comparatively less influence from topological network changes. In order to show the effectiveness of CMPACO, numerical networks considering static and dynamic conditions are tested and compared. The proposed CMPACO algorithm is considered a new and efficient parallel metaheuristic method to consider a complex network with topological changes.

Cooperative Multi-Agent Interaction and Evaluation Framework Considering Competitive Networks with Dynamic Topology Changes

“…The agent observes the new state (line 6). Then, receive the exact quantitative evaluation h from the human (line 10), the update of the adaptive human evaluation reward (lines [11][12][13][14][15]. Finally, when the reward for the Q-function is updated and determined, the Q-function is calculated (lines 17 and 18).…”

“…Furthermore, the computational load of networks that optimize paths such as an escape is heavy because it is necessary to derive the learning results by computing problems in real time. Therefore, learning methods that effectively deal with the real-time computation of a problem have been investigated [11][12][13].…”

An Imbalanced Data Handling Framework for Industrial Big Data Using a Gaussian Process Regression-Based Generative Adversarial Network

“…Chen et al [25] Wind prediction for energy efficiency Oh and Lee [26] Estimation of pheromone values based on ant colony optimization Figure 3 depicts the general process of GPR. The latent variable f i is derived from the input value X i with the observed value Y i .…”

“…Jochem et al [20] Automated spectral band analysis Ak et al [21] The time and space prediction of an infectious diseases Luttinen and Ilin [22] Sea level temperature reconstruction using GPR Nguyen and Peters [23] Kinetics model estimation Nguyen, Hu and Spanos [24] Efficient building field formation using an estimation of indoor environment fields Chen et al [25] Wind prediction for energy efficiency Oh and Lee [26] Estimation of pheromone values based on ant colony optimization Figure 3 depicts the general process of GPR. The latent variable is derived from the input value with the observed value .…”

Learning Collision Situation to Convolutional Neural Network Using Collision Grid Map Based on Probability Scheme

“…Recently, a Convolutional Neural Network (CNN) algorithm [11][12][13][14][15][16] using image data for object classification was proposed. In the proposed method [11], the neural network shows the feature of image data using gradient descent for optimizing.…”