An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems

Information Sciences, Journal Year: 2021, Volume and Issue: 585, P. 441 - 453

Published: Nov. 25, 2021

Language: Английский

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A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots

Applied Sciences, Journal Year: 2021, Volume and Issue: 11(23), P. 11202 - 11202

Published: Nov. 25, 2021

With the development of cities, urban congestion is nearly an unavoidable problem for almost every large-scale city. Road planning effective means to alleviate congestion, which a classical non-deterministic polynomial time (NP) hard problem, and has become important research hotspot in recent years. A K-means clustering algorithm iterative analysis that been regarded as solve road problems by scholars past several decades; however, it very difficult determine number clusters sensitively initialize center cluster. In order these problems, novel based on noise developed capture hotspots this paper. The employed randomly enhance attribution data points output results adding judgment automatically obtain given Four unsupervised evaluation indexes, namely, DB, PBM, SC, SSE, are directly used evaluate analyze results, nonparametric Wilcoxon statistical method verify distribution states differences between results. Finally, five taxi GPS datasets from Aracaju (Brazil), San Francisco (USA), Rome (Italy), Chongqing (China), Beijing (China) selected test effectiveness proposed comparing with fuzzy C-means, K-means, plus approaches. compared experiment show can reasonably cluster, demonstrates better performance accurately obtains well effectively capturing hotspots.

Language: Английский

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Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 120297 - 120308

Published: Jan. 1, 2021

In order to improve the diagnosis accuracy and solve weak fault signal of rolling element bearings due long transmission path, a novel method based on variational mode decomposition (VMD) maximum correlation kurtosis deconvolution (MCKD), namely VMD-MCKD-FD is proposed for elements in this paper. VMD-MCKD-FD, vibration decomposed into series Intrinsic Mode Functions (IMFs) by using VMD method. Then number modes with outstanding information determined Kurtosis criterion calculate period T. The periodic component reconstructed enhanced sensitivity MCKD Finally, power spectrum analyzed detail obtain frequency diagnose bearings. simulation actual are selected verify effectiveness experimental results show that can effectively better accuracy.

Language: Английский

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An adaptive differential evolution algorithm based on belief space and generalized opposition-based learning for resource allocation

Applied Soft Computing, Journal Year: 2022, Volume and Issue: 127, P. 109419 - 109419

Published: Aug. 2, 2022

Language: Английский

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A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

Applied Sciences, Journal Year: 2021, Volume and Issue: 11(12), P. 5385 - 5385

Published: June 10, 2021

In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the from weak signals under strong noise. Stochastic resonance (SR) a popular signal processing method, which can process with noise, but traditional SR burdensome in determining its parameters. Therefore, this paper, new advancing coupled multi-stable stochastic two first-order systems, namely CMSR, proposed faults. Firstly, effects of output signal-to-noise ratio (SNR) system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, SNR considered as fitness function seeker optimization algorithm (SOA), adaptively optimize determine using subsampling technique. An method realized, pre-processed input into CMSR bearings Fourier transform. The determined according signal. Finally, actual vibration data induction used prove effectiveness CMSR. comparison results MSR show that obtain higher SNR, more beneficial extract features realize fault detection. At same time, also has practical application value engineering rotating machinery.

Language: Английский

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Adaptive cylinder vector particle swarm optimization with differential evolution for UAV path planning

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 121, P. 105942 - 105942

Published: Feb. 9, 2023

Language: Английский

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Fractional-Order Controller for Course-Keeping of Underactuated Surface Vessels Based on Frequency Domain Specification and Improved Particle Swarm Optimization Algorithm

Applied Sciences, Journal Year: 2022, Volume and Issue: 12(6), P. 3139 - 3139

Published: March 18, 2022

In this paper, a new fractional-order (FO) PIλDµ controller is designed with the desired gain and phase margin for automatic rudder of underactuated surface vessels (USVs). The integral order λ differential μ are introduced in controller, two additional adjustable factors make FO have better accuracy robustness. Simulations carried out comparison ship’s digital PID autopilot. results show that has advantages small overshoot, short adjustment time, precise control. Due to uncertainty model parameters USVs extra parameters, it difficult compute an controller. Secondly, paper proposes novel particle swarm optimization (PSO) algorithm dynamic parameters. By dynamically changing learning factor, particles carefully search their own neighborhoods at early stage prevent them from missing global optimum converging on local optimum, while later evolution, converge optimal solution quickly accurately speed up PSO convergence. Finally, comparative experiments four different controllers under sailing conditions out, based IPSO control, strong anti-disturbance

Language: Английский

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A population state evaluation-based improvement framework for differential evolution

Information Sciences, Journal Year: 2023, Volume and Issue: 629, P. 15 - 38

Published: Feb. 3, 2023

Language: Английский

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Large-scale evolutionary optimization: A review and comparative study

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 85, P. 101466 - 101466

Published: Jan. 10, 2024

Large-scale global optimization (LSGO) problems have widely appeared in various real-world applications. However, their inherent complexity, coupled with the curse of dimensionality, makes them challenging to solve. Continuous efforts been devoted designing computational intelligence-based approaches solve them. This paper offers a comprehensive review latest developments field, focusing on advances both single-objective and multi-objective large-scale evolutionary algorithms over past five years. We systematically categorize these algorithms, discuss distinct features, highlight benchmark test suites essential for performance evaluation. After that, comparative studies are conducted using numerical solutions evaluate state-of-the-art LSGO problems. Finally, we applications LSGO, some challenges, possible future research directions.

Language: Английский

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Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

IEEE Transactions on Evolutionary Computation, Journal Year: 2022, Volume and Issue: 27(4), P. 964 - 979

Published: June 23, 2022

Large-scale optimization problems (LSOPs) are challenging because the algorithm is difficult in balancing too many dimensions and escaping from trapped bottleneck dimensions. To improve solutions, this paper introduces targeted modification to certain values Analogous gene targeting (GT) biotechnology, we experiment on specific genes candidate solution its trait differential evolution (DE). We propose a simple efficient method, called GT-based DE (GTDE), solve LSOPs. In design, developed perform best individual, comprising probabilistically location of dimensions, constructing homologous vector, inserting vector into individual. way, all individual can be modified break provide global guidance for more optimal evolution. Note that GT only performed globally just carried out as operator added standard DE. Experimental studies compare GTDE with some other state-of-the-art large-scale algorithms, including winners CEC2010, CEC2012, CEC2013, CEC2018 competitions optimization. The results show performs better than or at least comparable others solving

Language: Английский

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