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.
Highlights
The route generation in a complicated network is a representative Non-deterministic Polynomial-time (NP)-Complete problem requiring a non-deterministic solution time with the polynomial increase of problem complexity
In order to verify the validity of the proposed Convolution MPACO (CMPACO) algorithm, numerical experiments are conducted on the dynamic changes of network topology, where during the learning process, a portion of the network changes
The results show that the proposed CMPACO has better performances than other algorithms in a network environment with variable topology
Summary
The route generation in a complicated network is a representative NP-Complete problem requiring a non-deterministic solution time with the polynomial increase of problem complexity It has a number of applications, such as escape route production, and transport optimization. In the studies mentioned above, all agents used a unidirectional direction-based strategy [1,2,3,4,5,6,7] to produce routes from the start point to the terminal point, in the escape from a maze or large network. In order to overcome this issue, this research study uses a new and efficient strategy, the “Multi-directional and Parallel Ant Colony Optimization (MPACO)” algorithm, to start agents at any number of points simultaneously, not from the start point to the terminal point.
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