Multi-objective symbiotic organism search algorithm for optimal feature selection in brain computer interfaces

Baysal, Yesim A.; Ketenci, Seniha; Altaş, İsmail H.

doi:10.1016/j.eswa.2020.113907

Cited by 17 publications

(2 citation statements)

References 66 publications

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“…Each organism in the ecosystem has a fitness value assigned to it, which shows the degree of adaptation to the desired goal. Initially SOS was proposed to be applied to numerical optimization and engineering design problem only but later have been applied to some area for solving optimization problems like the work of [118] in cloud computing, [119] for optimal feature selection in brain computer interface and so on. To be specific, we observed that SOS is not applied to any combinatorial t-way strategy for optimization.…”

Section: Swarm-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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Metaheuristic algorithm is a very important area of research that continuously improve in solving optimization problems. Nature-inspired is one of the classifications of metaheuristic algorithm that are becoming more popular among researchers for the last decades. Nature-inspired metaheuristic algorithms contributes significantly to tackling many standing complex problems (such as combinatorial tway testing problem) and achieving optimal results. One challenge in this area is combinatorial explosion problem which always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation is selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-time hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization whilst others are not adopted. This research paper outlines hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021) such as Coronavirus Optimization Algorithm, Ebola Optimization Algorithm, Harmony Search, Tiki-Taka Algorithm, and so on. The purpose of this review is to revisit and carry out up-to-date review on these distinguished algorithms with their respective current state of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics has a simple structure to be adopted in different areas for becoming a more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters setting to achieve an optimal solution.

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Section: Swarm-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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“…In this case, multi-objective evolutionary algorithms (MOEAs), which can balance multiple optimization objectives at the same time, have been introduced to solve channel selection problems in recent years. Some classic MOEAs, such as multi-objective evolutionary algorithm based on decomposition (MOEA/D), multi-objective particle swarm optimization (MOPSO), and non-dominated sorting genetic algorithm (NSGA-II) have been successfully applied for channel selection in the task of single modality based BCIs (Al-Qazzaz et al, 2019 ; Nandy et al, 2019 ; Baysal et al, 2021 ; Li et al, 2022 ). Few of the existing multi-objective channel selection algorithms consider problem domain-related knowledge in the design of key operators.…”

Section: Introductionmentioning

confidence: 99%

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems

Liu,

2023

Front. Neurosci.

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BackgroundFor non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem.MethodsThe multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space.ResultsThe proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels.ConclusionThe formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications.

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An Improved Arithmetic Optimization Algorithm with Differential Evolution and Chaotic Local Search

Abdulsalami

Elaziz

Haj

et al. 2023

Advances in Intelligent Systems and Computing

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Multi-objective symbiotic organism search algorithm for optimal feature selection in brain computer interfaces

Cited by 17 publications

References 66 publications

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems

An Improved Arithmetic Optimization Algorithm with Differential Evolution and Chaotic Local Search

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