A Novel Optimization Technique to Improve Gas Recognition by Electronic Noses Based on the Enhanced Krill Herd Algorithm

Wang, Li; Jia, Pengfei; Huang, Tailai; Duan, Shukai; Jia, Yan; Wang, Lidan

doi:10.3390/s16081275

Cited by 11 publications

(6 citation statements)

References 33 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Authors in [56] used the FA algorithm in the near infrared (NIR) spectroscopy to find the most informative wavelengths. In the subarea of sensors, the researchers in [100] proposed a novel KH algorithm to improve gas recognition by electronic noses. Hyperspectral images are addressed in FS in [43,87].…”

mentioning

confidence: 99%

Swarm Intelligence Algorithms for Feature Selection: A Review

2018

View full text Add to dashboard Cite

The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data. To be able to learn from data, the dimensionality of the data should be reduced first. Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets. Swarm intelligence (SI) has been proved as a technique which can solve NP-hard (Non-deterministic Polynomial time) computational problems. It is gaining popularity in solving different optimization problems and has been used successfully for FS in some applications. With the lack of comprehensive surveys in this field, it was our objective to fill the gap in coverage of SI algorithms for FS. We performed a comprehensive literature review of SI algorithms and provide a detailed overview of 64 different SI algorithms for FS, organized into eight major taxonomic categories. We propose a unified SI framework and use it to explain different approaches to FS. Different methods, techniques, and their settings are explained, which have been used for various FS aspects. The datasets used most frequently for the evaluation of SI algorithms for FS are presented, as well as the most common application areas. The guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors while existing issues and open questions are being discussed. In this manner, using the proposed framework and the provided explanations, one should be able to design an SI approach to be used for a specific FS problem.

show abstract

mentioning

confidence: 99%

Swarm Intelligence Algorithms for Feature Selection: A Review

2018

View full text Add to dashboard Cite

show abstract

“…Similarly, for the detection rate, the proposed LNNLS-KH feature selection algorithm exhibits excellent performance. e average detection rate of (2,3,4,7,8,10,11,17,19,20,21,27,30,33) the LNNLS-KH algorithm is 96.48%, which is 13.47%, 9.32%, 7.02%, and 4.72% higher than the CMPSO, ACO, KH, and IKH feature selection algorithms, respectively.…”

Section: Experimental Results and Discussion Of Nsl-kdd Datasetmentioning

confidence: 91%

“…It integrates the local robust search method with the population-based method and has a good performance in high-dimensional data processing. It is widely used in network path optimization [4], text clustering analysis [5], neural network training [6], multiple continuous optimization [7][8][9], combinatorial optimization [10,11], constraint optimization [12][13][14], and other scenarios [3]. KH algorithm has good exploitation ability, but the exploration ability is not satisfactory, which means that the algorithm is easy to fall into local optimal solution when solving practical problems.…”

Section: Introductionmentioning

confidence: 99%

LNNLS-KH: A Feature Selection Method for Network Intrusion Detection

Xin

Peng

Wei

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

As an important part of intrusion detection, feature selection plays a significant role in improving the performance of intrusion detection. Krill herd (KH) algorithm is an efficient swarm intelligence algorithm with excellent performance in data mining. To solve the problem of low efficiency and high false positive rate in intrusion detection caused by increasing high-dimensional data, an improved krill swarm algorithm based on linear nearest neighbor lasso step (LNNLS-KH) is proposed for feature selection of network intrusion detection. The number of selected features and classification accuracy are introduced into fitness evaluation function of LNNLS-KH algorithm, and the physical diffusion motion of the krill individuals is transformed by a nonlinear method. Meanwhile, the linear nearest neighbor lasso step optimization is performed on the updated krill herd position in order to derive the global optimal solution. Experiments show that the LNNLS-KH algorithm retains 7 features in NSL-KDD dataset and 10.2 features in CICIDS2017 dataset on average, which effectively eliminates redundant features while ensuring high detection accuracy. Compared with the CMPSO, ACO, KH, and IKH algorithms, it reduces features by 44%, 42.86%, 34.88%, and 24.32% in NSL-KDD dataset, and 57.85%, 52.34%, 27.14%, and 25% in CICIDS2017 dataset, respectively. The classification accuracy increased by 10.03% and 5.39%, and the detection rate increased by 8.63% and 5.45%. Time of intrusion detection decreased by 12.41% and 4.03% on average. Furthermore, LNNLS-KH algorithm quickly jumps out of the local optimal solution and shows good performance in the optimal fitness iteration curve, convergence speed, and false positive rate of detection.

show abstract

“…For the first motion, αi represents the direction of its motion and is determined by a target, a local influence, and a repulsive effect. Here N max represents the maximal induced speed, w n dents the inertia weights of the second movement in (0, 1) and is the final movement impacted by other krill [54].…”

Section: Process Involved In Ckha-based Clustering Techniquementioning

confidence: 99%

Improved Metaheuristics-Based Clustering with Multihop Routing Protocol for Underwater Wireless Sensor Networks

Mohan

Neelakandan²,

Alotaibi

et al. 2022

Sensors

117

View full text Add to dashboard Cite

Underwater wireless sensor networks (UWSNs) comprise numerous underwater wireless sensor nodes dispersed in the marine environment, which find applicability in several areas like data collection, navigation, resource investigation, surveillance, and disaster prediction. Because of the usage of restricted battery capacity and the difficulty in replacing or charging the inbuilt batteries, energy efficiency becomes a challenging issue in the design of UWSN. Earlier studies reported that clustering and routing are considered effective ways of attaining energy efficacy in the UWSN. Clustering and routing processes can be treated as nondeterministic polynomial-time (NP) hard optimization problems, and they can be addressed by the use of metaheuristics. This study introduces an improved metaheuristics-based clustering with multihop routing protocol for underwater wireless sensor networks, named the IMCMR-UWSN technique. The major aim of the IMCMR-UWSN technique is to choose cluster heads (CHs) and optimal routes to a destination. The IMCMR-UWSN technique incorporates two major processes, namely the chaotic krill head algorithm (CKHA)-based clustering and self-adaptive glow worm swarm optimization algorithm (SA-GSO)-based multihop routing. The CKHA technique selects CHs and organizes clusters based on different parameters such as residual energy, intra-cluster distance, and inter-cluster distance. Similarly, the SA-GSO algorithm derives a fitness function involving four parameters, namely residual energy, delay, distance, and trust. Utilization of the IMCMR-UWSN technique helps to significantly boost the energy efficiency and lifetime of the UWSN. To ensure the improved performance of the IMCMR-UWSN technique, a series of simulations were carried out, and the comparative results reported the supremacy of the IMCMR-UWSN technique in terms of different measures.

show abstract

A Novel Optimization Technique to Improve Gas Recognition by Electronic Noses Based on the Enhanced Krill Herd Algorithm

Cited by 11 publications

References 33 publications

Swarm Intelligence Algorithms for Feature Selection: A Review

Swarm Intelligence Algorithms for Feature Selection: A Review

LNNLS-KH: A Feature Selection Method for Network Intrusion Detection

Improved Metaheuristics-Based Clustering with Multihop Routing Protocol for Underwater Wireless Sensor Networks

Contact Info

Product

Resources

About