Multi-strategy particle swarm and ant colony hybrid optimization for airport taxiway planning problem

Deng, Wu; Zhang, Lirong; Zhou, Xiangbing; Zhou, Yongquan; Sun, Yuzhu; Zhu, Wei; Chen, Huayue; Deng, Wuquan; Chen, Huiling; Zhao, Huimin

doi:10.1016/j.ins.2022.08.115

Cited by 109 publications

(47 citation statements)

References 41 publications

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“…In addition, there are some advanced variant algorithms, such as fruit fly optimizer with multi-population outpost mechanism (MOFOA) (Chen et al, 2020), bat algorithm based on collaborative and dynamic learning of opposite population (CDLOBA) (Yong et al, 2018), hybridizing gray wolf optimization (HGWO) (Zhu et al, 2015), oppositionbased sine cosine algorithm (OBSCA) (Abd Elaziz et al, 2017), Moth-flame optimizer with sine cosine (SMFO) (Chen C. et al, 2021), Cauchy and Gaussian sine cosine algorithm (CGSCA) (Kumar et al, 2017), modified SCA (m_SCA) (Qu et al, 2018), double adaptive random spare reinforced whale optimization algorithm (RDWOA) (Chen et al, 2019), and associative learning-based exploratory whale optimizer (BMWOA) (Heidari et al, 2020). Furthermore, they are already making their impact in many fields, such as train scheduling (Song et al, 2023), image segmentation (Hussien et al, 2022;Yu et al, 2022), feature selection (Liu Y. et al, 2022), complex optimization problem (Deng et al, 2022a), bankruptcy prediction (Zhang et al, 2021), gate resource allocation (Deng et al, 2020;Wu D. et al, 2020), multi-objective problem (Hua et al, 2021;Deng et al, 2022d), expensive optimization problems , robust optimization (He et al, 2019(He et al, , 2020, airport taxiway planning (Deng et al, 2022c), scheduling problems (Gao et al, 2020;Han et al, 2021;Wang G.-G. et al, 2022), medical diagnosis (Chen et al, 2016;Wang et al, 2017), and resource allocation (Deng et al, 2022b).…”

Section: An Overview Of Acormentioning

confidence: 99%

“…Furthermore, they are already making their impact in many fields, such as train scheduling ( Song et al, 2023 ), image segmentation ( Hussien et al, 2022 ; Yu et al, 2022 ), feature selection ( Liu Y. et al, 2022 ), complex optimization problem ( Deng et al, 2022a ), bankruptcy prediction ( Zhang et al, 2021 ), gate resource allocation ( Deng et al, 2020 ; Wu D. et al, 2020 ), multi-objective problem ( Hua et al, 2021 ; Deng et al, 2022d ), expensive optimization problems ( Li J.-Y. et al, 2020 ; Wu S.-H. et al, 2021 ), robust optimization ( He et al, 2019 , 2020 ), airport taxiway planning ( Deng et al, 2022c ), scheduling problems ( Gao et al, 2020 ; Han et al, 2021 ; Wang G.-G. et al, 2022 ), medical diagnosis ( Chen et al, 2016 ; Wang et al, 2017 ), and resource allocation ( Deng et al, 2022b ).…”

Section: The Proposed Gacomentioning

confidence: 99%

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Tuberculous pleural effusion prediction using ant colony optimizer with grade-based search assisted support vector machine

Hou

Pan

et al. 2022

Front. Neuroinform.

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IntroductionAlthough tuberculous pleural effusion (TBPE) is simply an inflammatory response of the pleura caused by tuberculosis infection, it can lead to pleural adhesions and cause sequelae of pleural thickening, which may severely affect the mobility of the chest cavity.MethodsIn this study, we propose bGACO-SVM, a model with good diagnostic power, for the adjunctive diagnosis of TBPE. The model is based on an enhanced continuous ant colony optimization (ACOR) with grade-based search technique (GACO) and support vector machine (SVM) for wrapped feature selection. In GACO, grade-based search greatly improves the convergence performance of the algorithm and the ability to avoid getting trapped in local optimization, which improves the classification capability of bGACO-SVM.ResultsTo test the performance of GACO, this work conducts comparative experiments between GACO and nine basic algorithms and nine state-of-the-art variants as well. Although the proposed GACO does not offer much advantage in terms of time complexity, the experimental results strongly demonstrate the core advantages of GACO. The accuracy of bGACO-predictive SVM was evaluated using existing datasets from the UCI and TBPE datasets.DiscussionIn the TBPE dataset trial, 147 TBPE patients were evaluated using the created bGACO-SVM model, showing that the bGACO-SVM method is an effective technique for accurately predicting TBPE.

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Section: An Overview Of Acormentioning

confidence: 99%

Section: The Proposed Gacomentioning

confidence: 99%

Tuberculous pleural effusion prediction using ant colony optimizer with grade-based search assisted support vector machine

Hou

Pan

et al. 2022

Front. Neuroinform.

Self Cite

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“…Therefore, based on an analysis of previous literature, we believe that focused improvement of the original algorithm is a viable option. These methods have excellent performance in dealing with some optimization problems, such as economic emission dispatch problem ( Dong et al, 2021 ), image segmentation ( Hussien et al, 2022 ; Yu et al, 2022b ), feature selection ( Hu J. et al, 2022 ; Liu et al, 2022 ), robust optimization ( He et al, 2019 , 2020 ), scheduling problems ( Gao et al, 2020 ; Han et al, 2021 ; Wang G. G. et al, 2022 ), multi-objective problem ( Hua et al, 2021 ; Deng et al, 2022d ), plant disease recognition ( Yu et al, 2022a ), complex optimization problem ( Deng et al, 2022b ), train scheduling ( Song et al, 2023 ), resource allocation ( Deng et al, 2022a ), airport taxiway planning ( Deng et al, 2022c ), optimization of machine learning model ( ling Chen et al, 2014 ), medical diagnosis ( Chen et al, 2016 ; Wang et al, 2017 ), and solar cell parameter identification ( Ye et al, 2021 ). But there are still some issues to be considered in these methods.…”

Section: Related Workmentioning

confidence: 99%

Multi-threshold image segmentation for melanoma based on Kapur’s entropy using enhanced ant colony optimization

Xiao

Ye²,

Zhao³

et al. 2022

Front. Neuroinform.

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Melanoma is a malignant tumor formed by the cancerous transformation of melanocytes, and its medical images contain much information. However, the percentage of the critical information in the image is small, and the noise is non-uniformly distributed. We propose a new multi-threshold image segmentation model based on the two-dimensional histogram approach to the above problem. We present an enhanced ant colony optimization for continuous domains (EACOR) in the proposed model based on the soft besiege and chase strategies. Further, EACOR is combined with two-dimensional Kapur’s entropy to search for the optimal thresholds. An experiment on the IEEE CEC2014 benchmark function was conducted to measure the reliable global search capability of the EACOR algorithm in the proposed model. Moreover, we have also conducted several sets of experiments to test the validity of the image segmentation model proposed in this paper. The experimental results show that the segmented images from the proposed model outperform the comparison method in several evaluation metrics. Ultimately, the model proposed in this paper can provide high-quality samples for subsequent analysis of melanoma pathology images.

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“…[ 28 ] processes an ensemble classification algorithm is operated to train a multi-label classifier in a new feature space. Based on [ 29 ], ref. [ 30 ] proposed a three-phase large-scale optimizer.…”

Section: Related Workmentioning

confidence: 99%

Attention-Based Scene Text Detection on Dual Feature Fusion

Silamu

Wang

et al. 2022

Sensors

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The segmentation-based scene text detection algorithm has advantages in scene text detection scenarios with arbitrary shape and extreme aspect ratio, depending on its pixel-level description and fine post-processing. However, the insufficient use of semantic and spatial information in the network limits the classification and positioning capabilities of the network. Existing scene text detection methods have the problem of losing important feature information in the process of extracting features from each network layer. To solve this problem, the Attention-based Dual Feature Fusion Model (ADFM) is proposed. The Bi-directional Feature Fusion Pyramid Module (BFM) first adds stronger semantic information to the higher-resolution feature maps through a top-down process and then reduces the aliasing effects generated by the previous process through a bottom-up process to enhance the representation of multi-scale text semantic information. Meanwhile, a position-sensitive Spatial Attention Module (SAM) is introduced in the intermediate process of two-stage feature fusion. It focuses on the one feature map with the highest resolution and strongest semantic features generated in the top-down process and weighs the spatial position weight by the relevance of text features, thus improving the sensitivity of the text detection network to text regions. The effectiveness of each module of ADFM was verified by ablation experiments and the model was compared with recent scene text detection methods on several publicly available datasets.

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Multi-strategy particle swarm and ant colony hybrid optimization for airport taxiway planning problem

Cited by 109 publications

References 41 publications

Tuberculous pleural effusion prediction using ant colony optimizer with grade-based search assisted support vector machine

Tuberculous pleural effusion prediction using ant colony optimizer with grade-based search assisted support vector machine

Multi-threshold image segmentation for melanoma based on Kapur’s entropy using enhanced ant colony optimization

Attention-Based Scene Text Detection on Dual Feature Fusion

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