A dynamic locality multi-objective salp swarm algorithm for feature selection

Aljarah, Ibrahim; Habib, Maria; Faris, Hossam; Al-Madi, Nailah; Heidari, Ali Asghar; Mafarja, Majdi; Elaziz, Mohamed E. Abd; Mirjalili, Seyedali

doi:10.1016/j.cie.2020.106628

Cited by 83 publications

(27 citation statements)

References 75 publications

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“…The GWORS was compared to well-known FS methods, and it obtained competitive performance. Tubishat et al [ 35 ] developed an FS based on a dynamic salp swarm algorithm (SSA). The SSA was developed based on two methods.…”

Section: Related Workmentioning

confidence: 99%

An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection

Ibrahim

Abualigah

Ewees

et al. 2021

Entropy

Self Cite

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With the widespread use of intelligent information systems, a massive amount of data with lots of irrelevant, noisy, and redundant features are collected; moreover, many features should be handled. Therefore, introducing an efficient feature selection (FS) approach becomes a challenging aim. In the recent decade, various artificial methods and swarm models inspired by biological and social systems have been proposed to solve different problems, including FS. Thus, in this paper, an innovative approach is proposed based on a hybrid integration between two intelligent algorithms, Electric fish optimization (EFO) and the arithmetic optimization algorithm (AOA), to boost the exploration stage of EFO to process the high dimensional FS problems with a remarkable convergence speed. The proposed EFOAOA is examined with eighteen datasets for different real-life applications. The EFOAOA results are compared with a set of recent state-of-the-art optimizers using a set of statistical metrics and the Friedman test. The comparisons show the positive impact of integrating the AOA operator in the EFO, as the proposed EFOAOA can identify the most important features with high accuracy and efficiency. Compared to the other FS methods whereas, it got the lowest features number and the highest accuracy in 50% and 67% of the datasets, respectively.

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Section: Related Workmentioning

confidence: 99%

An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection

Ibrahim

Abualigah

Ewees

et al. 2021

Entropy

Self Cite

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“…During the movement, the water is sulked by the body mass, which is later used to push the body mass forward. To systematically show the salp chains, the population is divided into two assemblies: Pioneer and adherents 107,108 . The first portion of the populace, that is, the salp tends at the leading end of the chain whereas the remainder of the populace tends to follow the rest salps.…”

Section: Meta‐heuristic Optimization Algorithmsmentioning

confidence: 99%

A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems

Arfeen

Sheikh

Azam

et al. 2020

Concurrency and Computation

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Microgrids have drawn substantial consideration due to high quality and reliable mix sources of electricity. This paper articulates the implication of innovative algorithms for cognitive microgrid. It perceived the algorithms that are backed by artificial intelligence (AI) are quite efficient due to the precision, convergence speed, and less computation time as compared to the conventional heuristic methods. Solar PV/Battery grid‐connected MG is modeled to achieve optimum size, supreme power quality, reduced fluctuations in voltage and frequency, reduced settling time, eliminate short transient currents, seamless power, least annual cost and high reliability as an objective function under wavering weather condition and dynamic load changes. Four broad categorizations of metaheuristic algorithms, that is, evolutionary, swarm intelligence, physics, and human intelligence‐based algorithms are well elaborated in this study. The optimal solution to the fitness function by using a hybrid optimization method also directed in the study. This paper gives deep insight to readers working in the area.

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“…FS plays a meaningful role in maximizing the performance of a ML model considering redundant and irrelevant attributes that degenerate the learning process performance and increase its complexity. FS is an optimization problem that can be expressed as a multiobjective optimization problem and is an NP-hard optimization problem since it has several (n) features, producing an ample search space of size (2 n ) of various permutations of the features [7], [8]. In particular, numerous search methods can be utilized to detect the optimal subset of features.…”

Section: Introductionmentioning

confidence: 99%

“…The second is to apply a random search method by exploring the domain randomly, which has some drawbacks and limitations. There is a chance of stagnation issues, including a very high time complexity [8]. One way to address the drawbacks and gaps of previous FS methods that have been proposed by researchers is to use meta-heuristic paradigms.…”

Section: Introductionmentioning

confidence: 99%

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The Influence of Salp Swarm Algorithm-Based Feature Selection on Network Anomaly Intrusion Detection

Alsaleh

BinSaeedan

2021

IEEE Access

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Network security plays a critical role in our lives based on the threats and attacks to which we are exposed and that increase daily; these attacks result in the need to develop different protection methods and techniques. Network intrusion detection systems (NIDSs) are a way to detect several malicious network attacks. Many researchers have focused on developing NIDSs based on machine learning (ML) approaches to detect variants of attacks. ML approaches can automatically discover the essential variances between normal and abnormal data by analysing the features of a large dataset. Indeed, many features are extracted without discrimination, increasing the computational complexity. By applying a feature selection method, a subset of features is selected from the whole feature set with the aim of improving the performance of MLbased detection methods. The salp swarm algorithm (SSA) is a nature-based optimization algorithm that has demonstrated efficiency in minimizing processing challenges to perform optimization for feature selection problems. This research investigates the impact of the SSA on improving ML-based network anomaly detection using different ML classifiers, including XGBoost and Naïve Bayes algorithms. Experiments were conducted on standard datasets for comparison purposes; two datasets are used explicitly for network intrusion attacks: UNSW-NB15 and NSL-KDD. The experimental results show that the proposed method performs better in improving anomaly NIDSs in terms of the f-measure, recall, detection rate, and false alarm rate on both datasets. It outperforms the state-of-the-art techniques recently proposed in the literature.

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A dynamic locality multi-objective salp swarm algorithm for feature selection

Cited by 83 publications

References 75 publications

An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection

An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection

A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems

The Influence of Salp Swarm Algorithm-Based Feature Selection on Network Anomaly Intrusion Detection

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