Binary Ant Colony Research Articles

Discretization method based on binary ant colony and variable precision rough set

Discretization plays an important role in data pre-processing and has been used in fields such as artificial intelligence and data mining. A new discretization algorithm based on binary ant colony and variable precision rough set is proposed in this paper. Binary ant network is first built using candidate breakpoints, then global optimal breakpoints is searched in the network. The fitness function is established by the number of breakpoints and approximation classification accuracy of variable precision rough set. This method is compared with other algorithms using C4.5 classifier on the WEKA (Waikato Environment for Knowledge Analysis) platform and seven UCI data sets. The results indicate that the proposed method performs well.

A Manhattan distance-based binary bat algorithm vs. integer ant colony optimisation for intrusion detection in the audit trails

A Manhattan distance-based binary bat algorithm vs. integer ant colony optimisation for intrusion detection in the audit trails

A Manhattan distance-based binary bat algorithm vs. integer ant colony optimisation for intrusion detection in the audit trails

Intrusion detection system (IDS) is the process of monitoring and analysing security activities occurring in computer or network systems. The detection method can perform either anomaly-based or misuse-based detection. The misuse mechanism aims to detect predefined attack scenarios in the audit trails, whereas the anomaly detection mechanism aims to detect deviations from normal user behaviour. In this paper, we deal with misuse detection. We propose two approaches to solve the NP-hard security audit trail analysis problem. Both rely on the Manhattan distance measure to improve the intrusion detection quality. The first proposed method, named enhanced binary bat algorithm (EBBA), is an improvement of bat algorithm (BA). The second one, named enhanced integer ant colony system (EIACS), is a combination of two metaheuristics: ant colony system (ACS) and simulated annealing (SA). Experiment results indicate that, for large problem size, the performance of EIACS is more significant than EBBA.

Fault diagnosis of rotary kiln using SVM and binary ACO

This paper proposes a novel hybrid algorithm for fault diagnosis of rotary kiln based on a binary ant colony (BACO) and support vec- tor machine (SVM). The algorithm can find a subset selection which is attained through the elimination of the features that produce noise or are strictly correlated with other already selected features. The BACO algorithm can improve classification accuracy with an appropri- ate feature subset and optimal parameters of SVM. The proposed algorithm is easily implemented and because of use of a simple filter in that, its computational complexity is very low. The performance of the proposed algorithm is evaluated through two real Rotary Cement kiln datasets. The results show that our algorithm outperforms existing algorithms.

Binary Ant Colony Optimizationにおける女王蟻戦略

Binary Ant Colony Optimizationにおける女王蟻戦略

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Ｂｉｎａｒｙ Ａｎｔ Ｃｏｌｏｎｙ Ｏｐｔｉｍｉｚａｔｉｏｎにおける女王蟻戦略の有効性の検討