A modified firefly algorithm applying on multi-objective radial-based function for blasting

Author(s):  
Abbas Abbaszadeh Shahri ◽  
Mohammad Khorsand Zak ◽  
Hossein Abbaszadeh Shahri
2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.


2021 ◽  
Vol 14 (1) ◽  
pp. 192-202
Author(s):  
Karrar Alwan ◽  
◽  
Ahmed AbuEl-Atta ◽  
Hala Zayed ◽  
◽  
...  

Accurate intrusion detection is necessary to preserve network security. However, developing efficient intrusion detection system is a complex problem due to the nonlinear nature of the intrusion attempts, the unpredictable behaviour of network traffic, and the large number features in the problem space. Hence, selecting the most effective and discriminating feature is highly important. Additionally, eliminating irrelevant features can improve the detection accuracy as well as reduce the learning time of machine learning algorithms. However, feature reduction is an NPhard problem. Therefore, several metaheuristics have been employed to determine the most effective feature subset within reasonable time. In this paper, two intrusion detection models are built based on a modified version of the firefly algorithm to achieve the feature selection task. The first and, the second models have been used for binary and multiclass classification, respectively. The modified firefly algorithm employed a mutation operation to avoid trapping into local optima through enhancing the exploration capabilities of the original firefly. The significance of the selected features is evaluated using a Naïve Bayes classifier over a benchmark standard dataset, which contains different types of attacks. The obtained results revealed the superiority of the modified firefly algorithm against the original firefly algorithm in terms of the classification accuracy and the number of selected features under different scenarios. Additionally, the results assured the superiority of the proposed intrusion detection system against other recently proposed systems in both binary classification and multi-classification scenarios. The proposed system has 96.51% and 96.942% detection accuracy in binary classification and multi-classification, respectively. Moreover, the proposed system reduced the number of attributes from 41 to 9 for binary classification and to 10 for multi-classification.


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