Route selection algorithm based on integer operation Ant Colony Optimization

In this paper, a feature selection algorithm based on ant colony optimization (ACO) is presented to construct classification rules for image classification. Most existing ACO-based algorithms use the graph with O(n2) edges. In contrast, the artificial ants in the proposed algorithm FSC-ACO traverse on a feature graph with only O(n) edges. During the process of feature selection, ants construct the classification rules for each class according to the improved pheromone and heuristic functions. FSC-ACO improves the qualities of rules depend on the classification accuracy and the length of rules. The experimental results on both standard and real image data sets show that the proposed algorithm can outperform the other related methods with fewer features in terms of speed, recall and classification accuracy.

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A new and efficient variable selection algorithm based on ant colony optimization. Applications to near infrared spectroscopy/partial least-squares analysis

Analytica Chimica Acta ◽

10.1016/j.aca.2011.04.061 ◽

2011 ◽

Vol 699 (1) ◽

pp. 18-25 ◽

Cited By ~ 70

Author(s):

Franco Allegrini ◽

Alejandro C. Olivieri

Keyword(s):

Infrared Spectroscopy ◽

Variable Selection ◽

Least Squares ◽

Ant Colony Optimization ◽

Partial Least Squares ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Ant Colony ◽

Selection Algorithm ◽

Partial Least Squares Analysis

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A Hybrid Feature Selection Based on Ant Colony Optimization and Probabilistic Neural Networks for Bearing Fault Diagnostics

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.10-12.573 ◽

2007 ◽

Vol 10-12 ◽

pp. 573-577

Author(s):

Y.H. Gai ◽

Gang Yu

Keyword(s):

Neural Networks ◽

Feature Selection ◽

Ant Colony Optimization ◽

Feature Selection Method ◽

Ant Colony ◽

Diagnostic Purpose ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Probabilistic Neural Networks ◽

Bearing Fault

This paper presents a novel hybrid feature selection algorithm based on Ant Colony Optimization (ACO) and Probabilistic Neural Networks (PNN). The wavelet packet transform (WPT) was used to process the bearing vibration signals and to generate vibration signal features. Then the hybrid feature selection algorithm was used to select the most relevant features for diagnostic purpose. Experimental results for bearing fault diagnosis have shown that the proposed hybrid feature selection method has greatly improved the diagnostic performance.

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Ant Colony Optimization Using Common Social Information and Self-Memory

Complexity ◽

10.1155/2021/6610670 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Yoshiki Tamura ◽

Tomoko Sakiyama ◽

Ikuo Arizono

Keyword(s):

Ant Colony Optimization ◽

Ant Colony ◽

Route Selection ◽

Ant System ◽

Rapid Accumulation ◽

Loss Of Diversity ◽

To Come ◽

The Traveling Salesman Problem ◽

Short Time ◽

Selection Of

Ant colony optimization (ACO), which is one of the metaheuristics imitating real ant foraging behavior, is an eﬀective method to ﬁnd a solution for the traveling salesman problem (TSP). The rank-based ant system (ASrank) has been proposed as a developed version of the fundamental model AS of ACO. In the ASrank, since only ant agents that have found one of some excellent solutions are let to regulate the pheromone, the pheromone concentrates on a specific route. As a result, although the ASrank can find a relatively good solution in a short time, it has the disadvantage of being prone falling into a local solution because the pheromone concentrates on a specific route. This problem seems to come from the loss of diversity in route selection according to the rapid accumulation of pheromones to the specific routes. Some ACO models, not just the ASrank, also suffer from this problem of loss of diversity in route selection. It can be considered that the diversity of solutions as well as the selection of solutions is an important factor in the solution system by swarm intelligence such as ACO. In this paper, to solve this problem, we introduce the ant system using individual memories (ASIM) aiming to improve the ability to solve TSP while maintaining the diversity of the behavior of each ant. We apply the existing ACO algorithms and ASIM to some TSP benchmarks and compare the ability to solve TSP.

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