An improved particle swarm optimization algorithm with adaptive weighted delay velocity

Xu, Lin; Song, Baoye; Cao, Maoyong

doi:10.1080/21642583.2021.1891153

Cited by 62 publications

(28 citation statements)

References 36 publications

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“…The experiment results on two MI datasets illustrate that the proposed method can improve the classification performance compared with the original CSP method. The further studies could seek to find other algorithms and learning mechanisms to optimize the revised objective function of CSP in order to enhance the feature extraction in BCI [46,47,48].…”

Section: Discussionmentioning

confidence: 99%

Deep Common Spatial Pattern based Motor Imagery Classification with Improved Objective Function

Yang

Huang

2022

IJNDI

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Survey/review study Deep Common Spatial Pattern Based Motor Imagery Classification with Improved Objective Function Nanxi Yu 1,2, Rui Yang 1, and Mengjie Huang 1,* 1 School of Electrical Engineering, Electronics & Computer Science, University of Liverpool, Liverpool, L69 3BX, United Kingdom 2 Department of Biostatistics, Graduate School of Arts and Sciences, Yale University, New Haven, CT 06511, United States * Correspondence: Mengjie.Huang@liverpool.ac.uk Received: 12 October 2022 Accepted: 28 November 2022 Published: 22 December 2022 Abstract: Common spatial pattern (CSP) technique has been very popular in terms of electroencephalogram (EEG) features extraction in motor imagery (MI)-based brain-computer interface (BCI). Through the simultaneous diagonalization of the covariance matrices, CSP intends to transform data into another mapping with data of different categories having maximal differences in their measures of dispersion. This paper shows the objective function realized by original CSP method could be inaccurate by regularizing the estimated spatial covariance matrix from EEG data by trace, leading to some flaws in the features to be extracted. In order to deal with this problem, a novel deep CSP (DCSP) model with optimal objective function is proposed in this paper. The benefits of the proposed DCSP method over original CSP method are verified with experiments on two EEG based MI datasets where the classification accuracy is effectively improved.

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Section: Discussionmentioning

confidence: 99%

Deep Common Spatial Pattern based Motor Imagery Classification with Improved Objective Function

Yang

Huang

2022

IJNDI

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“…To verify the superiority of the HGSPSO algorithm in solving complex problems, this paper compares it with the original PSO [24] and four other improved PSO algorithms, SAPSO [25], GLEPSO [27], UCPSO [28], and PSOAWDV [29], for experiments. The comparison parameters are set as shown in Table 1.…”

Section: A Comparison With Other Algorithmsmentioning

confidence: 99%

A Multi-Mechanism Particle Swarm Optimization Algorithm Combining Hunger Games Search and Simulated Annealing

et al. 2022

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Particle Swarm Optimization (PSO) algorithm is a meta-heuristic algorithm inspired by the foraging behavior of birds, which has received a lot of attention from many scholars because of its simple principle and fast convergence rate. However, the traditional particle update mechanism limits the performance of the algorithm and makes it easy to fall into local extremums, leading to a reduced convergence rate at a later stage. In this paper, we propose a Multi-Mechanism Particle Swarm Optimization (HGSPSO) algorithm. The algorithm optimizes the position update formula of the particles by the Hunger Game Search (HGS) algorithm to accelerate the convergence speed at the later stage of the algorithm, and then the Simulated Annealing (SA) algorithm is introduced to dynamically update the inertia weights to balance the exploration and utilization of the algorithm to help the particles jump out of the local extrema.In addition, the double variational restrictions strategy is used to simultaneously restrict the velocity and position of the particles to avoid particle transgressions. We tested the proposed algorithm with five compare algorithms on 20 benchmark functions in 30, 50, 100, and 1000 dimensions using Eclipse Kepler Release software. The experimental results show that HGSPSO shows significant superiority in all four evaluation metrics and five assessment schemes.INDEX TERMS Hunger game search, metaheuristic algorithm, particle swarm optimization, swarm intelligence.

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“…However, these intelligent algorithms suffer from imbalance in energy consumption and fail in handling the balance between local and global search during the process of CH selection. Moreover, the nonuniform distribution of CHs introduces maximized drain of energy at certain regions resulting in the problem of energy hole 15 . Further, heavily loaded CHs in the network perish faster than the under‐loaded CHs as the mean distance between CH and BS can be included into the fitness during the selection of all CHs closer to BS.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the nonuniform distribution of CHs introduces maximized drain of energy at certain regions resulting in the problem of energy hole. 15 Further, heavily loaded CHs in the network perish faster than the under-loaded CHs as the mean distance between CH and BS can be included into the fitness during the selection of all CHs closer to BS.…”

Section: Introductionmentioning

confidence: 99%

A modified whale‐dragonfly algorithm and self‐adaptive cuckoo search‐based clustering strategy for augmenting network lifetime in wireless sensor networks

Pratha

Asanambigai

Mugunthan³

2023

Int J Communication

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Summary Designing an energy efficient and durable wireless sensor networks (WSNs) is a key challenge as it personifies potential and reactive functionalities in harsh antagonistic environment at which wired system deployment is completely infeasible. Majority of the clustering mechanisms contributed to the literature concentrated on augmenting network lifetime and energy stability. However, energy consumption incurred by cluster heads (CHs) are high and thereby results in minimized network lifetime and frequent CHs selection. In this paper, a modified whale‐dragonfly optimization algorithm and self‐adaptive cuckoo search‐based clustering strategy (MWIDOA‐SACS) is proposed for sustaining energy stability and augment network lifetime. In specific, MWIDOA‐SACS is included for exploiting the fitness values that aids in determining two optimal nodes that are selected as optimal CH and cluster router (CR) nodes in the network. In MWIDOA, the search conduct of dragon flies is completely updated through whale optimization algorithm (WOA) for preventing load balancing at CHs. It minimized the overhead of CH by adopting CHs and CR for collecting information from cluster members and transmitting the aggregated data from CHs to the base station (BS). It included self‐adaptive cuckoo search (SACS) for achieving sink mobility using radius, energy stability, received signal strength, and throughput for achieving optimal data transmission process after partitioning the network into unequal clusters. Simulation experiments of the proposed MWIDOA‐SACS confirmed better performance in terms of total residual energy by 21.28% and network lifetime by 26.32%, compared to the competitive CH selection strategies.

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An improved particle swarm optimization algorithm with adaptive weighted delay velocity

Cited by 62 publications

References 36 publications

Deep Common Spatial Pattern based Motor Imagery Classification with Improved Objective Function

Deep Common Spatial Pattern based Motor Imagery Classification with Improved Objective Function

A Multi-Mechanism Particle Swarm Optimization Algorithm Combining Hunger Games Search and Simulated Annealing

A modified whale‐dragonfly algorithm and self‐adaptive cuckoo search‐based clustering strategy for augmenting network lifetime in wireless sensor networks

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