Classifying colour differences in dyed fabrics using an improved hunger games search optimised random vector functional link

Zhang, Xiaochun; Zhou, Zhiyu

doi:10.1177/15589250221111508

Cited by 3 publications

(1 citation statement)

References 25 publications

(28 reference statements)

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“…To preliminarily verify the superiority of CHTMDO, seven algorithms, GWO [40], WOA, BA, DA, PSO, IA [41], and basic MDO, are selected for comparison. PSO and BA algorithms are classical optimization algorithms, while DA, GWO, IA, and WOA algorithms are emerging optimization algorithms in recent years, which contain abundant and innovative scientific achievements in the field of meta-heuristic algorithm research and are often used in the comparison process of algorithm performance test [42].…”

Section: Comparison With Basic Wdo and Several Other Algorithmsmentioning

confidence: 99%

Chaotic Wind-Driven Optimization with Hyperbolic Tangent Model and T-Distributed Mutation Strategy

Fang,

Yan,

Zhou

2024

Mathematical Problems in Engineering

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Meta-heuristic algorithms have the advantages of resilience, global optimization capacity, and coding flexibility, making them helpful in tackling difficult optimization issues. The enhanced wind-driven optimization (CHTWDO) that was proposed in this paper coupled the chaotic map approach and the hyperbolic tangent with the T-distribution mutation method. The initial air particles are evenly distributed in the system space through a tent mapping strategy. Meanwhile, the variation probability of the hyperbolic tangent model and the T-distribution variation method are used to improve the comprehensive performance of the algorithm. In this way, the global search accuracy and the ability of avoiding the extreme value of the algorithm can be taken into account. Combining the three strategies, CHTWDO had higher global search accuracy and a stronger ability to jump out of local extremum. Comparing with the eight meta-heuristic algorithms (including WDO) and the single strategy improved WDO on 24 test functions, the experimental results show that CHTWDO with two improved strategies has better convergence precision and faster convergence speed. Statistical tests such as Friedman’s and Wilcoxon’s rank-sum tests are used to determine significant differences between these comparison algorithms. Finally, CHTWDO also obtains the best results on four classical optimization problems in engineering applications, which verifies its practicality and effectiveness.

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Section: Comparison With Basic Wdo and Several Other Algorithmsmentioning

confidence: 99%

Chaotic Wind-Driven Optimization with Hyperbolic Tangent Model and T-Distributed Mutation Strategy

Fang,

Yan,

Zhou

2024

Mathematical Problems in Engineering

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Prediction of Complex Acute Appendicitis Based on HGS-MSVM

Liu

Sun

et al. 2023

IEEE Access

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Intelligent techniques and optimization algorithms in textile colour management: a systematic review of applications and prediction accuracy

Liu,

et al. 2024

Fash Text

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Based on a selection of 101 articles published from 2013 to 2022, this study systematically reviews the application of intelligent techniques and optimization algorithms in textile colour management. Specifically, the study explores how these techniques have been applied to four subfields within textile colour management: colour matching and prediction, colour difference detection and assessment, colour recognition and segmentation, and dye solution concentration and decolourization. Following an introduction to intelligent techniques and optimization algorithms in textile colour management, the study describes the specific applications of these techniques in the field over the past decade. Descriptive statistics are used to analyse trends in the use of these techniques and optimization algorithms, and comparative performances indicate the effectiveness of the techniques and algorithms. The study finds that the primary intelligent techniques used in the field of textile colour management include artificial neural networks (ANN), support vector machines (SVM) such as SVM, LSSVM, LSSVR, SLSSVR, FWSVR, fuzzy logic (FL) and adaptive neuro-fuzzy inference systems (ANFIS), clustering algorithms (e.g., K-means, FCM, X-means algorithms), and extreme learning machines (ELM) such as ELM, OSLEM, KELM, RELM. The main optimization algorithms used include response surface methodology (RSM), genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE). Finally, the study proposes a comparison of the performance of intelligent techniques and optimization algorithms, summarizes the relevant research trends, and suggests future research opportunities and directions, besides stating the limitations of this paper.

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Classifying colour differences in dyed fabrics using an improved hunger games search optimised random vector functional link

Cited by 3 publications

References 25 publications

Chaotic Wind-Driven Optimization with Hyperbolic Tangent Model and T-Distributed Mutation Strategy

Chaotic Wind-Driven Optimization with Hyperbolic Tangent Model and T-Distributed Mutation Strategy

Prediction of Complex Acute Appendicitis Based on HGS-MSVM

Intelligent techniques and optimization algorithms in textile colour management: a systematic review of applications and prediction accuracy

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