An intelligent algorithm based on neural network for combinatorial optimization problems

Zhang, Cui; Li, Qiang; Wang, Ronglong

doi:10.1109/bmei.2014.7002909

Cited by 2 publications

(4 citation statements)

References 21 publications

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“…In a combinatorial optimization problem, there is a finite or limited number of solutions available in the solution space. Most of the combinatorial optimization problems are considered as a complicated problem [8]. Simulated Annealing (SA) is one of the computational intelligence approaches for providing meaningful and reasonable solutions for combinatorial optimization problems [9] [10] and can be utilized for feature extraction (example; for cybersecurity threat detection).…”

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

“…As per our literature survey, it is found that simulated annealing is usually not utilized as a classifier [11]. However, the SA method is explored a lot for searching optimal solutions to problems such as the travelling salesperson problem [12], color mapping problem [8], traffic routing management problem [13], and clustering of large sets of time series [14].…”

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

“…In order to calculate the Gini index for a set of features/attributes with classes [47], let's assume that ∈ {1,2,3 … … … }, and let be the fraction of the items labelled with class in the set. Accordingly, the set of equation is given by (8).…”

Section: Decision Treesmentioning

confidence: 99%

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A Support Vector Machine Cost Function in Simulated Annealing for Network Intrusion Detection

Chowdhury¹,

Ferens²

2019

Adv. sci. technol. eng. syst. j.

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This paper proposes a computationally intelligent algorithm for extracting relevant features from a training set. An optimal subset of features is extracted from training examples of network intrusion datasets. The Support Vector Machine (SVM) algorithm is used as the cost function within the thermal equilibrium loop of the Simulated Annealing (SA) algorithm. The proposed fusion algorithm uses a combinatorial optimization algorithm (SA) to determine an optimal feature subset for a classifier (SVM) for the classification of normal and abnormal packets (possible intrusion threats) in a network. The proposed methodology is analyzed and validated using two different network intrusion datasets and the performance measures used are; detection accuracy, false positive and false negative rate, Receiver Operation Characteristics (ROC) curve, area under curve value and F1-score. A comparative analysis through empirically determined measures show that the proposed SA-SVM based model outperforms the general SVM and decision tree-based detection schemes based on performance measures such as detection accuracy, false positive and false negative rates, area under curve value and F1-score.

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Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

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A Support Vector Machine Cost Function in Simulated Annealing for Network Intrusion Detection

Chowdhury¹,

Ferens²

2019

Adv. sci. technol. eng. syst. j.

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“…Compared with the large number of single index methods, there are only few literature using multi-indexes approaches to evaluate or rank mutual funds. These little literature can be divided into four typologies: operational research methods such as data envelopment analysis (Basso & Funari, 2001;Charnes et al, 1985;Gouveia et al, 2018); those based on principal component analysis (Pearson, 1901); those based on intelligent analysis such as neural network (Indro, 1999;Li & Qu, 2022) or genetic algorithm (Wang & Li, 2002); those based on multi-criteria decision making (MCDM) method such as simple scoring method, the ideal solution TOPSIS method (Alptekin, 2009;Chang et al, 2010), and other MCDM methods (Alimi et al, 2012;Lee et al, 2009). One of the advantages of these comprehensive methods is that various aspects of fund performance are considered, although many approaches may not easy to operate in practice.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Method for Ranking Mutual Fund Performance

Yuan

2023

Sage Open

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The increasing guidance requirement in choosing mutual funds contribute to the development of an abundant literature on approaches of ranking funds performance. However, all the commonly used single index and multi-indexes ranking methodologies have their own drawbacks and are in fact not suitable for a reasonable and practical mutual funds comprehensive ranking sometimes. More specifically, the single index measures are incomprehensive, controversial, or even ineffective sometimes, while the mostly used multi-indexes methods do not make full use of evaluation information and usually indirect and inflexible in reality. This paper proposes a paired competition based mutual fund multi-indexes comprehensive performance ranking method which could avoid most of the shortcomings of existing approaches with six good characteristics: it is a comprehensive ranking method that reflects different performance aspects; it provides both cardinal and ordinal information for various practical applications; it integrates both individual evaluation information and joint comparison information for obtaining a more accurate and robust ranking scheme; it has a flexible framework to process complicated data situation; it reveals true strength of fund without distortion; it is convenient to operate in practice. In addition, a more reasonable objective weighted method is proposed to deal with the indexes correlation problems.

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An intelligent algorithm based on neural network for combinatorial optimization problems

Cited by 2 publications

References 21 publications

A Support Vector Machine Cost Function in Simulated Annealing for Network Intrusion Detection

A Support Vector Machine Cost Function in Simulated Annealing for Network Intrusion Detection

A Comprehensive Method for Ranking Mutual Fund Performance

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