A Particle Swarm Optimization With Adaptive Learning Weights Tuned by a Multiple-Input Multiple-Output Fuzzy Logic Controller

Xia, Xuewen; Song, Haojie; Zhang, Yinglong; Gui, Ling; Xu, Xing; Li, Kangshun; Li, Yuanxiang

doi:10.1109/tfuzz.2022.3227464

Cited by 12 publications

(3 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method has faster global convergence speed and higher solution accuracy. Xia et al [10] proposed an MFCPSO algorithm to address the shortcomings of fitness based selection, which exhibits promising characteristics in largescale complex functions. However, these evolutionary optimization algorithms also have certain limitations.…”

Section: Introductionmentioning

confidence: 99%

A Feature Selection Method Based on Feature-Label Correlation Information and Self-Adaptive MOPSO

Han,

Li,

Ling

et al. 2024

Neural Process Lett

View full text Add to dashboard Cite

Feature selection can be seen as a multi-objective task, where the goal is to select a subset of features that exhibit minimal correlation among themselves while maximizing their correlation with the target label. Multi-objective particle swarm optimization algorithm (MOPSO) has been extensively utilized for feature selection and has achieved good performance. However, most MOPSO-based feature selection methods are random and lack knowledge guidance in the initialization process, ignoring certain valuable prior information in the feature data, which may lead to the generated initial population being far from the true Pareto front (PF) and influence the population’s rate of convergence. Additionally, MOPSO has a propensity to become stuck in local optima during the later iterations. In this paper, a novel feature selection method (fMOPSO-FS) is proposed. Firstly, with the aim of improving the initial solution quality and fostering the interpretability of the selected features, a novel initialization strategy that incorporates prior information during the initialization process of the particle swarm is proposed. Furthermore, an adaptive hybrid mutation strategy is proposed to avoid the particle swarm from getting stuck in local optima and to further leverage prior information. The experimental results demonstrate the superior performance of the proposed algorithm compared to the comparison algorithms. It yields a superior feature subset on nine UCI benchmark datasets and six gene expression profile datasets.

show abstract

Section: Introductionmentioning

confidence: 99%

A Feature Selection Method Based on Feature-Label Correlation Information and Self-Adaptive MOPSO

Han,

Li,

Ling

et al. 2024

Neural Process Lett

View full text Add to dashboard Cite

show abstract

“…Also, Duan et al [16] proposed a Chaos Adaptive PSO method to enhance the optimization ability of PSO, Fallahi and Taghadosi [17] applies a Quantum-behaved strategy to PSO. Xia et al [18] proposed an adaptive learning weights for PSO PSO-based methods have shown great performance in solving optimization problems. Compared with traditional methods, PSO algorithms posse the advantages of high accuracy, fast computation, and great adaptability.…”

Section: Introductionmentioning

confidence: 99%

A Bare-Bones Particle Swarm Optimization With Crossed Memory for Global Optimization

Guo

Zhou

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The offspring selection strategy is the core of evolutionary algorithms, which directly affects the method's accuracy. Normally, to improve the search accuracy in local areas, the population converges quickly around the optimal individual. However, excessive aggregation can narrow the search range of the population, and thus the population may be trapped by local optima. To overcome this problem, a bare-bones particle swarm optimization with crossed memory (BPSO-CM) is proposed in this work. The BPSO-CM contains a multi-memory storage mechanism (MSM) and an elite offspring selection strategy (EOSS). The MSM enables an extra storage space to extend the search ability of the particle swarm and the EOSS enhances the local minimum escaping ability of the particle swarm. The population is endowed with the ability of enhanced global search through the cooperation of the MSM and the EOSS. To verify the performance of the BPSO-CM, the CEC2017 benchmark functions are used in experiments, five population-based methods are selected in the control group. Finally, experimental results proved that the BPSO-CM can present highly accurate results for global optimization problems.

show abstract

“…Among intelligent optimization algorithms, particle swarm optimization (PSO) has prominent advantages in convergence speed, initially proposed by Kennedy and Eberhart in 1995 as a swarm-intelligence-based optimization technology. In PSO, the optimization particles imitate the collective predation process of birds and allow for parallel and robust calculation processes [31][32][33]. This characteristic enhances the ability to find globally optimal solutions, giving it a computational advantage in analyzing various problems [34].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors

Huang,

Lin,

Cao

et al. 2023

Mathematics

View full text Add to dashboard Cite

Currently, there are few systematic position control parameter optimization methods for planar switched reluctance motors (PSRMs); how to effectively optimize the control parameters of PSRMs is one of the critical issues that needs to be urgently solved. Therefore, a nonlinear dynamic model-based position control parameter optimization method of PSRMs is proposed in this paper. First, to improve the accuracy of the motor dynamics model, a Hammerstein–Wiener model based on the BP neural network input–output nonlinear module is established by combining the linear model and nonlinear model structures so that the nonlinear and linear characteristics of the system are characterized simultaneously. Then, a position control parameter optimization system of PSRMs is developed using the established Hammerstein–Wiener model. In addition, with a self-designed simulated annealing adaptive particle swarm optimization algorithm (SAAPSO), the position control parameter optimization system is performed offline iteratively to obtain the optimal position control parameters. Simulations and experiments are carried out and the corresponding results show that the optimal position control parameters obtained by the proposed method can be directly applied in the actual control system of PSRMs and the control performance is improved effectively using the obtained optimal control parameters.

show abstract

A Particle Swarm Optimization With Adaptive Learning Weights Tuned by a Multiple-Input Multiple-Output Fuzzy Logic Controller

Cited by 12 publications

References 52 publications

A Feature Selection Method Based on Feature-Label Correlation Information and Self-Adaptive MOPSO

A Feature Selection Method Based on Feature-Label Correlation Information and Self-Adaptive MOPSO

A Bare-Bones Particle Swarm Optimization With Crossed Memory for Global Optimization

Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors

Contact Info

Product

Resources

About