Cuckoo Search Algorithm: A Review

Sahoo, Mitali Madhusmita; Dash, Rasmita; Dash, Rajashree; Rautray, Rasmita

doi:10.1007/978-981-15-1420-3_124

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…CS algorithm is successfully applied to solve several optimization problems like traveling salesman problem [ 39 ], economic load dispatch [ 40 ], face recognition [ 41 ], loading pattern optimization [ 42 ], data clustering [ 43 ], feature selection [ 44 ], short-term load forecasting [ 45 ], modeling proton exchange membrane fuel cells [ 46 ], electric distribution network reconfiguration [ 47 ], optimal reactive power scheduling [ 48 ], contrast enhancement of gray scale images [ 49 ], estimating peak particle velocity in mine blasting [ 50 ], systems of nonlinear equations [ 51 ], maximizing area coverage in wireless sensor networks [ 52 ], job scheduling [ 53 ], and others reported in [ 35 , 54 , 55 ].…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications

Alyasseri

Alomari

Al-Betar

et al. 2022

Computational Intelligence and Neuroscience

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Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain’s electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86 % using only 24 sensors with AR 20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.

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