Algorithms

25 pages, 1770 KiB

Open AccessArticle

An Improved Iterated Greedy Algorithm for Solving Collaborative Helicopter Rescue Routing Problem with Time Window and Limited Survival Time

by Xining Cui, Kaidong Yang, Xiaoqing Wang and Peng Duan

Algorithms 2024, 17(10), 431; https://doi.org/10.3390/a17100431 - 26 Sep 2024

Abstract

Research on helicopter dispatching has received considerable attention, particularly in relation to post-disaster rescue operations. The survival chances of individuals trapped in emergency situations decrease as time passes, making timely helicopter dispatch crucial for successful rescue missions. Therefore, this study investigates a collaborative [...] Read more.

Research on helicopter dispatching has received considerable attention, particularly in relation to post-disaster rescue operations. The survival chances of individuals trapped in emergency situations decrease as time passes, making timely helicopter dispatch crucial for successful rescue missions. Therefore, this study investigates a collaborative helicopter rescue routing problem with time window and limited survival time constraints, solving it using an improved iterative greedy (IIG) algorithm. In the proposed algorithm, a heuristic initialization strategy is designed to generate an efficient and feasible initial solution. Then, a feasible-first destruction-construction strategy is applied to enhance the algorithm’s exploration ability. Next, a problem-specific local search strategy is developed to improve the algorithm’s local search effectiveness. In addition, the simulated annealing (SA) method is integrated as an acceptance criterion to avoid the algorithm from getting trapped in local optima. Finally, to evaluate the efficacy of the proposed IIG, 56 instances were generated based on Solomon instances and used for simulation tests. A comparative analysis was conducted against six efficient algorithms from the existing studies. The experimental results demonstrate that the proposed algorithm performs well in solving the post-disaster rescue helicopter routing problem. Full article

(This article belongs to the Special Issue Recent Algorithms for Operation Research in Logistics, Project Planning and Scheduling)

40 pages, 2708 KiB

Open AccessArticle

Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings

by Markus Eisenbach, Andreas Gebhardt, Dustin Aganian and Horst-Michael Gross

Algorithms 2024, 17(10), 430; https://doi.org/10.3390/a17100430 - 26 Sep 2024

Abstract

In most re-identification approaches, embedding vectors are compared to identify the best match for a given query. However, this comparison does not take into account whether the encoded information in the embedding vectors was extracted reliably from the input images. We propose the [...] Read more.

In most re-identification approaches, embedding vectors are compared to identify the best match for a given query. However, this comparison does not take into account whether the encoded information in the embedding vectors was extracted reliably from the input images. We propose the first attempt that illustrates how all three types of uncertainty, namely model uncertainty (also known as epistemic uncertainty), data uncertainty (also known as aleatoric uncertainty), and distributional uncertainty, can be estimated for embedding vectors. We provide evidence that we do indeed estimate these types of uncertainty, and that each type has its own value for improving re-identification performance. In particular, while the few state-of-the-art approaches that employ uncertainty for re-identification during inference utilize only data uncertainty to improve single-shot re-identification performance, we demonstrate that the estimated model uncertainty vector can be utilized to modify the feature vector. We explore the best method for utilizing the estimated model uncertainty based on the Market-1501 dataset and demonstrate that we are able to further enhance the performance above the already strong baseline UAL. Additionally, we show that the estimated distributional uncertainty resembles the degree to which the current sample is out-of-distribution. To illustrate this, we divide the distractor set of the Market-1501 dataset into four classes, each representing a different degree of out-of-distribution. By computing a score based on the estimated distributional uncertainty vector, we are able to correctly order the four distractor classes and to differentiate them from an in-distribution set to a significant extent. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (2nd Edition))

18 pages, 5670 KiB

Open AccessArticle

Improved U2Net-Based Surface Defect Detection Method for Blister Tablets

by Jianmin Zhou, Jian Huang, Jikang Liu and Jingbo Liu

Algorithms 2024, 17(10), 429; https://doi.org/10.3390/a17100429 - 26 Sep 2024

Abstract

Aiming at the problem that the surface defects of blAister tablets are difficult to detect correctly, this paper proposes a detection method based on the improved U2Net. First, the features extracted from the RSU module of U2Net are enhanced and adjusted using the [...] Read more.

Aiming at the problem that the surface defects of blAister tablets are difficult to detect correctly, this paper proposes a detection method based on the improved U2Net. First, the features extracted from the RSU module of U2Net are enhanced and adjusted using the large kernel attention mechanism, so that the U2Net model strengthens its ability to extract defective features. Second, a loss function combining the Gaussian Laplace operator and the cross-entropy function is designed to make the model strengthen its ability to detect edge defects on the surface of blister tablets. Finally, thresholds are adaptively determined using the local mean and OTSU(an adaptive threshold segmentation method) method to improve accuracy. The experimental results show that the method proposed in this paper can reach an average accuracy of 99% and an average precision rate of 96.3%; the model test only takes 50 ms per image, which can meet the rapid detection requirements. Minor surface defects can also be accurately detected, which is better than other algorithmic models of the same type, proving the effectiveness of this method. Full article

(This article belongs to the Special Issue Algorithms for Image Processing and Machine Vision)

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40 pages, 4614 KiB

Open AccessArticle

In Silico Optimization of a Fed-Batch Bioreactor for Tryptophan Production Using a Structured Hybrid Model and Several Algorithms Including a Pareto-Optimal Front

by Gheorghe Maria and Daniela Gheorghe

Algorithms 2024, 17(10), 428; https://doi.org/10.3390/a17100428 - 25 Sep 2024

Viewed by 179

Abstract

Hybrid kinetic models, linking structured models of cell (nano-scale) metabolic processes to the dynamics of macroscopic variables of the bioreactor, are proven to lead to more precise predictions of all key-species dynamics under variable operating conditions, being of an exceptional importance in engineering [...] Read more.

Hybrid kinetic models, linking structured models of cell (nano-scale) metabolic processes to the dynamics of macroscopic variables of the bioreactor, are proven to lead to more precise predictions of all key-species dynamics under variable operating conditions, being of an exceptional importance in engineering evaluations to in-silico (math-model-based) determine the optimal operating mode of a fed-batch bioreactor (FBR). Even if such extended dynamic models require more experimental and computational efforts, their use has proven to be advantageous. The approached probative example refers to the simulation of the dynamics of some key species of the central carbon metabolism (CCM) of a modified E. coli cell, linked to the state variables of a FBR used for the tryptophan (TRP) production. By using several optimization algorithms, and an original application of the Pareto-optimal front technique, this paper compares various operating alternatives by using multiple control variables, aiming to maximize TRP production, with minimum substrate consumption. The used E. coli strain was modified to drastically amplify the glucose (GLC) uptake into the cell. Full article

(This article belongs to the Special Issue Dynamic System Modelling from Data: Emerging Algorithms and Applications)

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32 pages, 8006 KiB

Open AccessArticle

Application of Particle Swarm Optimization to a Hybrid H_∞/Sliding Mode Controller Design for the Triple Inverted Pendulum System

by Yamama A. Shafeek and Hazem I. Ali

Algorithms 2024, 17(10), 427; https://doi.org/10.3390/a17100427 - 24 Sep 2024

Viewed by 226

Abstract

The robotics field of engineering has been witnessing rapid advancements and becoming widely engaged in our lives recently. Its application has pervaded various areas that range from household services to agriculture, industry, military, and health care. The humanoid robots are electro–mechanical devices that [...] Read more.

The robotics field of engineering has been witnessing rapid advancements and becoming widely engaged in our lives recently. Its application has pervaded various areas that range from household services to agriculture, industry, military, and health care. The humanoid robots are electro–mechanical devices that are constructed in the semblance of humans and have the ability to sense their environment and take actions accordingly. The control of humanoids is broken down to the following: sensing and perception, path planning, decision making, joint driving, stability and balance. In order to establish and develop control strategies for joint driving, stability and balance, the triple inverted pendulum is used as a benchmark. As the presence of uncertainty is inevitable in this system, the need to develop a robust controller arises. The robustness is often achieved at the expense of performance. Hence, the controller design has to be optimized based on the resultant control system’s performance and the required torque. Particle Swarm Optimization (PSO) is an excellent algorithm in finding global optima, and it can be of great help in automatic tuning of the controller design. This paper presents a hybrid H_∞/sliding mode controller optimized by the PSO algorithm to control the triple inverted pendulum system. The developed control system is tested by applying it to the nominal, perturbed by parameter variation, perturbed by external disturbance, and perturbed by measurement noise system. The average error in all cases is 0.053 deg and the steady controller effort range is from 0.13 to 0.621 N.m with respect to amplitude. The system’s robustness is provided by the hybrid H_∞/sliding mode controller and the system’s performance and efficiency enhancement are provided by optimization. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms in Optimal Design of Engineering Problems)

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19 pages, 3821 KiB

Open AccessArticle

Pedestrian Re-Identification Based on Weakly Supervised Multi-Feature Fusion

by Changming Qin, Zhiwen Wang, Linghui Zhang, Qichang Peng, Guixing Lin and Guanlin Lu

Algorithms 2024, 17(10), 426; https://doi.org/10.3390/a17100426 - 24 Sep 2024

Viewed by 236

Abstract

This article proposes a weakly supervised multi-feature fusion pedestrian re-identification method, which introduces a multi-feature fusion mechanism to extract feature information from different layers into the same feature space and fuse them into the deep and shallow joint features. The goal is to [...] Read more.

This article proposes a weakly supervised multi-feature fusion pedestrian re-identification method, which introduces a multi-feature fusion mechanism to extract feature information from different layers into the same feature space and fuse them into the deep and shallow joint features. The goal is to fully utilize the rich information in the image and improve the performance and robustness of the pedestrian re-identification model. Secondly, by matching the target character with unprocessed surveillance videos, one only needs to know that the identity of a person appears in the video, without annotating the identity of a person in any of the frames of the video during the training process. This simplifies the annotation of training images by replacing accurate annotations with broad annotations; that is, it puts the pedestrian identities that appeared in the video in one package and assigns a video-level label to each package. This greatly reduces the annotation work and transforms this weakly supervised pedestrian re-identification challenge into a multi-instance and multi-label learning problem. The experimental results show that the method proposed in this paper is effective and can significantly improve mAP. Full article

(This article belongs to the Section Evolutionary Algorithms and Machine Learning)

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19 pages, 3337 KiB

Open AccessArticle

Detecting Fake Accounts on Instagram Using Machine Learning and Hybrid Optimization Algorithms

by Pegah Azami and Kalpdrum Passi

Algorithms 2024, 17(10), 425; https://doi.org/10.3390/a17100425 - 24 Sep 2024

Viewed by 305

Abstract

In this paper, we propose a hybrid method for detecting fake accounts on Instagram by using the Binary Grey Wolf Optimization (BGWO) and Particle Swarm Optimization (PSO) algorithms. By combining these two algorithms, we aim to leverage their complementary strengths and enhance the [...] Read more.

In this paper, we propose a hybrid method for detecting fake accounts on Instagram by using the Binary Grey Wolf Optimization (BGWO) and Particle Swarm Optimization (PSO) algorithms. By combining these two algorithms, we aim to leverage their complementary strengths and enhance the overall optimization performance. We evaluate the proposed hybrid method using four classifiers: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). The dataset for the experiments contains 65,329 Instagram accounts. We extract features from each account, including profile information, posting behavior, and engagement metrics. The Binary Grey Wolf and Particle Swarm Optimizations, when combined to form a hybrid method (BGWOPSO), improved the performance in accurately detecting fake accounts on Instagram. Full article

(This article belongs to the Special Issue Hybrid Intelligent Algorithms)

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17 pages, 5801 KiB

Open AccessArticle

Structural Reliability Analysis Using Stochastic Finite Element Method Based on Krylov Subspace

by Jianyun Huang, Qiuwei Yang, Hongfei Cao and Jiwei Ma

Algorithms 2024, 17(10), 424; https://doi.org/10.3390/a17100424 - 24 Sep 2024

Viewed by 258

Abstract

The stochastic finite element method is an important tool for structural reliability analysis. In order to improve the calculation efficiency, a stochastic finite element method based on the Krylov subspace is proposed for the static reliability analysis of structures. The first step of [...] Read more.

The stochastic finite element method is an important tool for structural reliability analysis. In order to improve the calculation efficiency, a stochastic finite element method based on the Krylov subspace is proposed for the static reliability analysis of structures. The first step of the proposed method is to preprocess the static response equation considering randomness to reduce the condition number of the coefficient matrix. The second step of the proposed method is to construct a Krylov subspace based on the preprocessed static response equation. Then, the static displacement of random sampling is expressed as a linear combination of subspace basis vectors to achieve the purpose of a fast solution. Finally, statistics and failure probability are calculated according to the static response obtained from thousands of random samples. Three numerical examples are given to compare the proposed method with the stochastic finite element method based on the Neumann series. The results show that the stochastic finite element method based on the Krylov subspace is more accurate and efficient than the stochastic finite element method based on the Neumann series. Full article

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Algorithms, Volume 17, Issue 10 (October 2024) – 8 articles

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