A Novel Simple Particle Swarm Optimization Algorithm for Global Optimization

Zhang, Xin; Zou, Dexuan; Shen, Xin

doi:10.3390/math6120287

Cited by 26 publications

(15 citation statements)

References 71 publications

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“…And the computational complexity of GWO is also O(N × (T + TD)) [14]. Furthermore, the computational complexity of PSO is similar to O(T×D) [23]. The computational complexity of GA is O(T×N×L), where L is the length of the genotypes [24].…”

Section: Avoa Improved By Icmicmentioning

confidence: 99%

Wind Turbine Blade Icing Diagnosis Using Convolutional LSTM-GRU With Improved African Vultures Optimization

Wen-he

Zhou

Cheng

et al. 2022

IEEE Open J. Instrum. Meas.

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Wind farms are usually located in plateau mountains and northern coastal areas, bringing a high probability of blade icing. Blade icing even leads to blade cracks and turbine collapse. Traditional methods of blade icing diagnosis increase operating costs and have the potential risk of damaging the original mechanical structure. A data-driven model based on a novel convolutional recurrent neural network is proposed in this paper. The method can effectively extract hidden features for accurate icing diagnosis. The hyperparameters of the proposed model are optimized by the improved African Vultures optimization algorithm (IAVOA). To alleviate the critical data imbalance, the Adaptive Synthetic (ADASYN) is used to oversample the minority classes of icing status. In comparison to the state-of-art classification methods, the proposed method illustrates the outstanding effectiveness in blade icing diagnosis using the sensor data from Supervisory Control and Data Acquisition (SCADA) systems. The effectiveness analysis of variables, ablation study, and sensitivity analysis validate the performance of the proposed method.

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Section: Avoa Improved By Icmicmentioning

confidence: 99%

Wind Turbine Blade Icing Diagnosis Using Convolutional LSTM-GRU With Improved African Vultures Optimization

Wen-he

Zhou

Cheng

et al. 2022

IEEE Open J. Instrum. Meas.

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“…The order of computation of GSA is

O (m^{2})

[49], here

m

indicates the variable number. The PSO's order of computation is

O (m \times n)

[50] with

m

and

n

considered as variables and population size, respectively. The PSOGSA and ABC have high computational complexity as

(O false(m \times n false) + O false(m^{2} false))

and

O (n^{5})

, respectively [48].…”

Section: Theoretical Analysis Of Asa Compared With the Existing Algor...mentioning

confidence: 99%

Advanced squirrel algorithm‐trained neural network for efficient spectrum sensing in cognitive radio‐based air traffic control application

2021

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In the current scenario, there is a drastic increase in air traffic. The air to ground communication plays a crucial role in the air traffic control system. There is a limited spectrum available for aircraft to establish a connection with the Air Traffic Controller (ATC). With air traffic growth, the available spectrum is getting more congested. This paper proposed an Advanced Squirrel Algorithm (ASA)-trained neural network (NN) for efficient spectrum sensing for cognitive radio-based air traffic control applications. ASA is a novel metaheuristic-based training algorithm for an NN. With the proposed algorithm, it is possible to dynamically allocate the unused spectrum for air to ground communication between aircraft and ATC. The quantitative analysis of the proposed ASA-NN-based spectrum sensing is done by comparing it with the existing metaheuristic-based NN training algorithms, namely, particle swarm optimization Gravitational Search Algorithm (PSOGSA), particle swarm optimization (PSO), gravitational search algorithm (GSA), and artificial bee colony (ABC). Simulation-based evaluation shows that the proposed ASA-NN is capable of efficiently detecting the spectrum holes with high convergence rate as compared to PSOGSA-, PSO-, GSA-, and ABC-based algorithms.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The PSO algorithm is derived from animal predation behavior. In this algorithm, the particle swarm is randomly initialised using the fitness function to evaluate the solution, and the optimal solution is found by iteration [28].…”

Section: Pso Improved By Fractional Differentialmentioning

confidence: 99%

A Novel FastSLAM Framework Based on 2D Lidar for Autonomous Mobile Robot

et al. 2020

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The autonomous navigation and environment exploration of mobile robots are carried out on the premise of the ability of environment sensing. Simultaneous localisation and mapping (SLAM) is the key algorithm in perceiving and mapping an environment in real time. FastSLAM has played an increasingly significant role in the SLAM problem. In order to enhance the performance of FastSLAM, a novel framework called IFastSLAM is proposed, based on particle swarm optimisation (PSO). In this framework, an adaptive resampling strategy is proposed that uses the genetic algorithm to increase the diversity of particles, and the principles of fractional differential theory and chaotic optimisation are combined into the algorithm to improve the conventional PSO approach. We observe that the fractional differential approach speeds up the iteration of the algorithm and chaotic optimisation prevents premature convergence. A new idea of a virtual particle is put forward as the global optimisation target for the improved PSO scheme. This approach is more accurate in terms of determining the optimisation target based on the geometric position of the particle, compared to an approach based on the maximum weight value of the particle. The proposed IFastSLAM method is compared with conventional FastSLAM, PSO-FastSLAM, and an adaptive generic FastSLAM algorithm (AGA-FastSLAM). The superiority of IFastSLAM is verified by simulations, experiments with a real-world dataset, and field experiments.

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A Novel Simple Particle Swarm Optimization Algorithm for Global Optimization

Cited by 26 publications

References 71 publications

Wind Turbine Blade Icing Diagnosis Using Convolutional LSTM-GRU With Improved African Vultures Optimization

Wind Turbine Blade Icing Diagnosis Using Convolutional LSTM-GRU With Improved African Vultures Optimization

Advanced squirrel algorithm‐trained neural network for efficient spectrum sensing in cognitive radio‐based air traffic control application

A Novel FastSLAM Framework Based on 2D Lidar for Autonomous Mobile Robot

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