Feature selection for fuzzy classifier using the spider monkey algorithm

Hodashinsky, I. A.; Немирович-Данченко, М М; Samsonov, S.

doi:10.17323/1998-0663.2019.2.29.42

Cited by 4 publications

(3 citation statements)

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“…Objective: minimize the total energy consumption along the selected path

. Constraints: connectivity; ensure that the selected path

is connected in the communication graph

energy constraint [ 34 ]. The energy consumption along the path should not exceed the energy available at any node.…”

Section: Wsn Cluster Head Architecturementioning

confidence: 99%

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks

Ullah,

Khan,

Mohy-ud-din

et al. 2024

Sensors

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In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model.

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“…Objective: minimize the total energy consumption along the selected path

. Constraints: connectivity; ensure that the selected path

is connected in the communication graph

energy constraint [ 34 ]. The energy consumption along the path should not exceed the energy available at any node.…”

Section: Wsn Cluster Head Architecturementioning

confidence: 99%

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks

Ullah,

Khan,

Mohy-ud-din

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The proposed algorithm is designed for thinning of concentric circular antenna arrays. In [30], the binary spider monkey algorithm is used to feature selection for a fuzzy classifier.…”

Section: Modified Algebraic Operationsmentioning

confidence: 99%

“…BSSO was compared with other representative methods, wrapper feature selections based on the binary spider monkey algorithm (BSMA) [30], the binary gravitational search algorithm (BSGA) [37], the binary brain storm optimization algorithm (BBSO) [52], the random search algorithm (RS), as well as a feature selection algorithm based on mutual information (IG) [53], and a algorithm without feature selection (All features) on well-known benchmark datasets.…”

Section: Comparison With the Other Approachesmentioning

confidence: 99%

Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification

et al. 2019

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This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric structure of a membership function. Searching for the (sub) optimal subset of features is an NP-hard problem. In this paper, a binary swallow swarm optimization (BSSO) algorithm for feature selection is proposed. To solve the classification problem, we use a fuzzy rule-based classifier. To evaluate the feature selection performance of our method, BSSO is compared to induction without feature selection and some similar algorithms on well-known benchmark datasets. Experimental results show the promising behavior of the proposed method in the optimal selection of features.

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