Research

21 pages, 1783 KiB

Open AccessArticle

Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm

by Amal H. Alharbi, S. K. Towfek, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, Doaa Sami Khafaga, Nima Khodadadi, Laith Abualigah and Mohamed Saber

Biomimetics 2023, 8(3), 313; https://doi.org/10.3390/biomimetics8030313 - 16 Jul 2023

Cited by 10 | Viewed by 1534

Abstract

The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection [...] Read more.

The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection approaches are crucial since COVID-19 has reached a pandemic level. This study’s overarching goal is to use metaheuristic optimization to boost the performance of feature selection and classification methods to identify skin lesions as indicators of monkeypox in the event of a pandemic. Deep learning and transfer learning approaches are used to extract the necessary features. The GoogLeNet network is the deep learning framework used for feature extraction. In addition, a binary implementation of the dipper throated optimization (DTO) algorithm is used for feature selection. The decision tree classifier is then used to label the selected set of features. The decision tree classifier is optimized using the continuous version of the DTO algorithm to improve the classification accuracy. Various evaluation methods are used to compare and contrast the proposed approach and the other competing methods using the following metrics: accuracy, sensitivity, specificity, p-Value, N-Value, and F1-score. Through feature selection and a decision tree classifier, the following results are achieved using the proposed approach; F1-score of 0.92, sensitivity of 0.95, specificity of 0.61, p-Value of 0.89, and N-Value of 0.79. The overall accuracy of the proposed methodology after optimizing the parameters of the decision tree classifier is 94.35%. Furthermore, the analysis of variation (ANOVA) and Wilcoxon signed rank test have been applied to the results to investigate the statistical distinction between the proposed methodology and the alternatives. This comparison verified the uniqueness and importance of the proposed approach to Monkeypox case detection. Full article

(This article belongs to the Special Issue Nature-Inspired Computer Algorithms)

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19 pages, 3415 KiB

Open AccessArticle

Binary Sand Cat Swarm Optimization Algorithm for Wrapper Feature Selection on Biological Data

by Amir Seyyedabbasi

Biomimetics 2023, 8(3), 310; https://doi.org/10.3390/biomimetics8030310 - 14 Jul 2023

Cited by 13 | Viewed by 1531

Abstract

In large datasets, irrelevant, redundant, and noisy attributes are often present. These attributes can have a negative impact on the classification model accuracy. Therefore, feature selection is an effective pre-processing step intended to enhance the classification performance by choosing a small number of [...] Read more.

In large datasets, irrelevant, redundant, and noisy attributes are often present. These attributes can have a negative impact on the classification model accuracy. Therefore, feature selection is an effective pre-processing step intended to enhance the classification performance by choosing a small number of relevant or significant features. It is important to note that due to the NP-hard characteristics of feature selection, the search agent can become trapped in the local optima, which is extremely costly in terms of time and complexity. To solve these problems, an efficient and effective global search method is needed. Sand cat swarm optimization (SCSO) is a newly introduced metaheuristic algorithm that solves global optimization algorithms. Nevertheless, the SCSO algorithm is recommended for continuous problems. bSCSO is a binary version of the SCSO algorithm proposed here for the analysis and solution of discrete problems such as wrapper feature selection in biological data. It was evaluated on ten well-known biological datasets to determine the effectiveness of the bSCSO algorithm. Moreover, the proposed algorithm was compared to four recent binary optimization algorithms to determine which algorithm had better efficiency. A number of findings demonstrated the superiority of the proposed approach both in terms of high prediction accuracy and small feature sizes. Full article

(This article belongs to the Special Issue Nature-Inspired Computer Algorithms)

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18 pages, 11568 KiB

Open AccessArticle

Multi-Criterion Sampling Matting Algorithm via Gaussian Process

by Yuan Yang, Hongshan Gou, Mian Tan, Fujian Feng, Yihui Liang, Yi Xiang, Lin Wang and Han Huang

Biomimetics 2023, 8(3), 301; https://doi.org/10.3390/biomimetics8030301 - 10 Jul 2023

Cited by 2 | Viewed by 1119

Abstract

Natural image matting is an essential technique for image processing that enables various applications, such as image synthesis, video editing, and target tracking. However, the existing image matting methods may fail to produce satisfactory results when computing resources are limited. Sampling-based methods can [...] Read more.

Natural image matting is an essential technique for image processing that enables various applications, such as image synthesis, video editing, and target tracking. However, the existing image matting methods may fail to produce satisfactory results when computing resources are limited. Sampling-based methods can reduce the dimensionality of the decision space and, therefore, reduce computational resources by employing different sampling strategies. While these approaches reduce computational consumption, they may miss an optimal pixel pair when the number of available high-quality pixel pairs is limited. To address this shortcoming, we propose a novel multi-criterion sampling strategy that avoids missing high-quality pixel pairs by incorporating multi-range pixel pair sampling and a high-quality sample selection method. This strategy is employed to develop a multi-criterion matting algorithm via Gaussian process, which searches for the optimal pixel pair by using the Gaussian process fitting model instead of solving the original pixel pair objective function. The experimental results demonstrate that our proposed algorithm outperformed other methods, even with 1% computing resources, and achieved alpha matte results comparable to those yielded by the state-of-the-art optimization algorithms. Full article

(This article belongs to the Special Issue Nature-Inspired Computer Algorithms)

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19 pages, 866 KiB

Open AccessArticle

Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective

by Imran Mir, Faiza Gul, Suleman Mir, Laith Abualigah, Raed Abu Zitar, Abdelazim G. Hussien, Emad Mahrous Awwad and Mohamed Sharaf

Biomimetics 2023, 8(3), 294; https://doi.org/10.3390/biomimetics8030294 - 07 Jul 2023

Cited by 3 | Viewed by 1299

Abstract

This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm’s efficiency. The architecture, called [...] Read more.

This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm’s efficiency. The architecture, called the Multi-Agent Exploration–Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithm’s average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs. Full article

(This article belongs to the Special Issue Nature-Inspired Computer Algorithms)

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25 pages, 4175 KiB

Open AccessArticle

Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm

by Qingjiao Cao, Liying Wang, Weiguo Zhao, Zhouxiang Yuan, Anran Liu, Yanfeng Gao and Runfeng Ye

Biomimetics 2023, 8(2), 243; https://doi.org/10.3390/biomimetics8020243 - 08 Jun 2023

Cited by 4 | Viewed by 1139

Abstract

To improve the identification accuracy of the vibration states of hydraulic units, an improved artificial rabbits optimization algorithm (IARO) adopting an adaptive weight adjustment strategy is developed for optimizing the support vector machine (SVM) to obtain an identification model, and the vibration signals [...] Read more.

To improve the identification accuracy of the vibration states of hydraulic units, an improved artificial rabbits optimization algorithm (IARO) adopting an adaptive weight adjustment strategy is developed for optimizing the support vector machine (SVM) to obtain an identification model, and the vibration signals with different states are classified and identified. The variational mode decomposition (VMD) method is used to decompose the vibration signals, and the multi-dimensional time-domain feature vectors of the signals are extracted. The IARO algorithm is used to optimize the parameters of the SVM multi-classifier. The multi-dimensional time-domain feature vectors are input into the IARO-SVM model to realize the classification and identification of vibration signal states, and the results are compared with those of the ARO-SVM model, ASO-SVM model, PSO-SVM model and WOA-SVM model. The comparative results show that the average identification accuracy of the IARO-SVM model is higher at 97.78% than its competitors, which is 3.34% higher than the closest ARO-SVM model. Therefore, the IARO-SVM model has higher identification accuracy and better stability, and can accurately identify the vibration states of hydraulic units. The research can provide a theoretical basis for the vibration identification of hydraulic units. Full article

(This article belongs to the Special Issue Nature-Inspired Computer Algorithms)

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27 pages, 3419 KiB

Open AccessArticle

Improved Environmental Stimulus and Biological Competition Tactics Interactive Artificial Ecological Optimization Algorithm for Clustering

by Wenyan Guo, Mingfei Wu, Fang Dai and Yufan Qiang

Biomimetics 2023, 8(2), 242; https://doi.org/10.3390/biomimetics8020242 - 07 Jun 2023

Viewed by 862

Abstract

An interactive artificial ecological optimization algorithm (SIAEO) based on environmental stimulus and a competition mechanism was devised to find the solution to a complex calculation, which can often become bogged down in local optimum because of the sequential execution of consumption and decomposition [...] Read more.

An interactive artificial ecological optimization algorithm (SIAEO) based on environmental stimulus and a competition mechanism was devised to find the solution to a complex calculation, which can often become bogged down in local optimum because of the sequential execution of consumption and decomposition stages in the artificial ecological optimization algorithm. Firstly, the environmental stimulus defined by population diversity makes the population interactively execute the consumption operator and decomposition operator to abate the inhomogeneity of the algorithm. Secondly, the three different types of predation modes in the consumption stage were regarded as three different tasks, and the task execution mode was determined by the maximum cumulative success rate of each individual task execution. Furthermore, the biological competition operator is recommended to modify the regeneration strategy so that the SIAEO algorithm can provide consideration to the exploitation in the exploration stage, break the equal probability execution mode of the AEO, and promote the competition among operators. Finally, the stochastic mean suppression alternation exploitation problem is introduced in the later exploitation process of the algorithm, which can tremendously heighten the SIAEO algorithm to run away the local optimum. A comparison between SIAEO and other improved algorithms is performed on the CEC2017 and CEC2019 test set. Full article

(This article belongs to the Special Issue Nature-Inspired Computer Algorithms)

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13 pages, 426 KiB

Open AccessArticle

Solving the Min-Max Clustered Traveling Salesmen Problem Based on Genetic Algorithm

by Xiaoguang Bao, Guojun Wang, Lei Xu and Zhaocai Wang

Biomimetics 2023, 8(2), 238; https://doi.org/10.3390/biomimetics8020238 - 06 Jun 2023

Cited by 4 | Viewed by 1290

Abstract

The min-max clustered traveling salesmen problem (MMCTSP) is a generalized variant of the classical traveling salesman problem (TSP). In this problem, the vertices of the graph are partitioned into a given number of clusters and we are asked to find a collection of [...] Read more.

The min-max clustered traveling salesmen problem (MMCTSP) is a generalized variant of the classical traveling salesman problem (TSP). In this problem, the vertices of the graph are partitioned into a given number of clusters and we are asked to find a collection of tours to visit all the vertices with the constraint that the vertices of each cluster are visited consecutively. The objective of the problem is to minimize the weight of the maximum weight tour. For this problem, a two-stage solution method based on a genetic algorithm is designed according to the problem characteristics. The first stage is to determine the visiting order of the vertices within each cluster, by abstracting a TSP from the corresponding cluster and applying a genetic algorithm to solve it. The second stage is to determine the assignment of clusters to salesmen and the visiting order of the assigned clusters. In this stage, by representing each cluster as a node and using the result of the first stage and the ideas of greed and random, we define the distances between each two nodes and construct a multiple traveling salesmen problem (MTSP), and then apply a grouping-based genetic algorithm to solve it. Computational experiments indicate that the proposed algorithm can obtain better solution results for various scale instances and shows good solution performance. Full article

(This article belongs to the Special Issue Nature-Inspired Computer Algorithms)

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23 pages, 6353 KiB

Open AccessArticle

An Energy-Saving and Efficient Deployment Strategy for Heterogeneous Wireless Sensor Networks Based on Improved Seagull Optimization Algorithm

by Li Cao, Zihui Wang, Zihao Wang, Xiangkun Wang and Yinggao Yue

Biomimetics 2023, 8(2), 231; https://doi.org/10.3390/biomimetics8020231 - 02 Jun 2023

Cited by 7 | Viewed by 1212

Abstract

The Internet of Things technology provides convenience for data acquisition in environmental monitoring and environmental protection and can also avoid invasive damage caused by traditional data acquisition methods. An adaptive cooperative optimization seagull algorithm for optimal coverage of heterogeneous sensor networks is proposed [...] Read more.

The Internet of Things technology provides convenience for data acquisition in environmental monitoring and environmental protection and can also avoid invasive damage caused by traditional data acquisition methods. An adaptive cooperative optimization seagull algorithm for optimal coverage of heterogeneous sensor networks is proposed in order to address the issue of coverage blind zone and coverage redundancy in the initial random deployment of heterogeneous sensor network nodes in the sensing layer of the Internet of Things. Calculate the individual fitness value according to the total number of nodes, coverage radius, and area edge length, select the initial population, and aim at the maximum coverage rate to determine the position of the current optimal solution. After continuous updating, when the number of iterations is maximum, the global output is output. The optimal solution is the node’s mobile position. A scaling factor is introduced to dynamically adjust the relative displacement between the current seagull individual and the optimal individual, which improves the exploration and development ability of the algorithm. Finally, the optimal seagull individual position is fine-tuned by random opposite learning, leading the whole seagull to move to the correct position in the given search space, improving the ability to jump out of the local optimum, and further increasing the optimization accuracy. The experimental simulation results demonstrate that, compared with the coverage and network energy consumption of the PSO algorithm, the GWO algorithm, and the basic SOA algorithm, the coverage of the PSO-SOA algorithm proposed in this paper is 6.1%, 4.8%, and 1.2% higher than them, respectively, and the energy consumption of the network is reduced by 86.8%, 68.4%, and 52.6%, respectively. The optimal deployment method based on the adaptive cooperative optimization seagull algorithm can improve the network coverage and reduce the network cost, and effectively avoid the coverage blind zone and coverage redundancy in the network. Full article

(This article belongs to the Special Issue Nature-Inspired Computer Algorithms)

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38 pages, 25569 KiB

Open AccessArticle

An Adaptive Sand Cat Swarm Algorithm Based on Cauchy Mutation and Optimal Neighborhood Disturbance Strategy

by Xing Wang, Qian Liu and Li Zhang

Biomimetics 2023, 8(2), 191; https://doi.org/10.3390/biomimetics8020191 - 04 May 2023

Cited by 7 | Viewed by 1733

Abstract

Sand cat swarm optimization algorithm (SCSO) keeps a potent and straightforward meta-heuristic algorithm derived from the distant sense of hearing of sand cats, which shows excellent performance in some large-scale optimization problems. However, the SCSO still has several disadvantages, including sluggish convergence, lower [...] Read more.

Sand cat swarm optimization algorithm (SCSO) keeps a potent and straightforward meta-heuristic algorithm derived from the distant sense of hearing of sand cats, which shows excellent performance in some large-scale optimization problems. However, the SCSO still has several disadvantages, including sluggish convergence, lower convergence precision, and the tendency to be trapped in the topical optimum. To escape these demerits, an adaptive sand cat swarm optimization algorithm based on Cauchy mutation and optimal neighborhood disturbance strategy (COSCSO) are provided in this study. First and foremost, the introduction of a nonlinear adaptive parameter in favor of scaling up the global search helps to retrieve the global optimum from a colossal search space, preventing it from being caught in a topical optimum. Secondly, the Cauchy mutation operator perturbs the search step, accelerating the convergence speed and improving the search efficiency. Finally, the optimal neighborhood disturbance strategy diversifies the population, broadens the search space, and enhances exploitation. To reveal the performance of COSCSO, it was compared with alternative algorithms in the CEC2017 and CEC2020 competition suites. Furthermore, COSCSO is further deployed to solve six engineering optimization problems. The experimental results reveal that the COSCSO is strongly competitive and capable of being deployed to solve some practical problems. Full article

(This article belongs to the Special Issue Nature-Inspired Computer Algorithms)

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25 pages, 2483 KiB

Open AccessArticle

A Novel Topology Optimization Protocol Based on an Improved Crow Search Algorithm for the Perception Layer of the Internet of Things

by Yang Bai, Li Cao, Binhe Chen, Yaodan Chen and Yinggao Yue

Biomimetics 2023, 8(2), 165; https://doi.org/10.3390/biomimetics8020165 - 19 Apr 2023

Cited by 5 | Viewed by 1049

Abstract

In wireless sensor networks, each sensor node has a finite amount of energy to expend. The clustering method is an efficient way to deal with the imbalance in node energy consumption. A topology optimization technique for wireless sensor networks based on the Cauchy [...] Read more.

In wireless sensor networks, each sensor node has a finite amount of energy to expend. The clustering method is an efficient way to deal with the imbalance in node energy consumption. A topology optimization technique for wireless sensor networks based on the Cauchy variation optimization crow search algorithm (CM-CSA) is suggested to address the issues of rapid energy consumption, short life cycles, and unstable topology in wireless sensor networks. At the same time, a clustering approach for wireless sensor networks based on the enhanced Cauchy mutation crow search algorithm is developed to address the issue of the crow algorithm’s sluggish convergence speed and ease of falling into the local optimum. It utilizes the Cauchy mutation to improve the population’s variety and prevent settling for the local optimum, as well as to broaden the range of variation and the capacity to carry out global searches. When the leader realizes he is being followed, the discriminative probability is introduced to improve the current person’s location update approach. According to the simulation findings, the suggested CM-CSA algorithm decreases the network’s average energy consumption by 66.7%, 50%, and 33.3% and enhances its connectivity performance by 52.9%, 37.6%, and 23.5% when compared to the PSO algorithm, AFSA method, and basic CSA algorithm. Full article

(This article belongs to the Special Issue Nature-Inspired Computer Algorithms)

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20 pages, 2266 KiB

Open AccessArticle

Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm

by Amel Ali Alhussan, Marwa M. Eid, S. K. Towfek and Doaa Sami Khafaga

Biomimetics 2023, 8(2), 163; https://doi.org/10.3390/biomimetics8020163 - 17 Apr 2023

Cited by 2 | Viewed by 1566

Abstract

According to the American Cancer Society, breast cancer is the second largest cause of mortality among women after lung cancer. Women’s death rates can be decreased if breast cancer is diagnosed and treated early. Due to the lengthy duration of manual breast cancer [...] Read more.

According to the American Cancer Society, breast cancer is the second largest cause of mortality among women after lung cancer. Women’s death rates can be decreased if breast cancer is diagnosed and treated early. Due to the lengthy duration of manual breast cancer diagnosis, an automated approach is necessary for early cancer identification. This research proposes a novel framework integrating metaheuristic optimization with deep learning and feature selection for robustly classifying breast cancer from ultrasound images. The structure of the proposed methodology consists of five stages, namely, data augmentation to improve the learning of convolutional neural network (CNN) models, transfer learning using GoogleNet deep network for feature extraction, selection of the best set of features using a novel optimization algorithm based on a hybrid of dipper throated and particle swarm optimization algorithms, and classification of the selected features using CNN optimized using the proposed optimization algorithm. To prove the effectiveness of the proposed approach, a set of experiments were conducted on a breast cancer dataset, freely available on Kaggle, to evaluate the performance of the proposed feature selection method and the performance of the optimized CNN. In addition, statistical tests were established to study the stability and difference of the proposed approach compared to state-of-the-art approaches. The achieved results confirmed the superiority of the proposed approach with a classification accuracy of 98.1%, which is better than the other approaches considered in the conducted experiments. Full article

(This article belongs to the Special Issue Nature-Inspired Computer Algorithms)

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40 pages, 34960 KiB

Open AccessArticle

Multistrategy-Boosted Carnivorous Plant Algorithm: Performance Analysis and Application in Engineering Designs

by Min Peng, Wenlong Jing, Jianwei Yang and Gang Hu

Biomimetics 2023, 8(2), 162; https://doi.org/10.3390/biomimetics8020162 - 17 Apr 2023

Cited by 2 | Viewed by 1055

Abstract

Many pivotal and knotty engineering problems in practical applications boil down to optimization problems, which are difficult to resolve using traditional mathematical optimization methods. Metaheuristics are efficient algorithms for solving complex optimization problems while keeping computational costs reasonable. The carnivorous plant algorithm (CPA) [...] Read more.

Many pivotal and knotty engineering problems in practical applications boil down to optimization problems, which are difficult to resolve using traditional mathematical optimization methods. Metaheuristics are efficient algorithms for solving complex optimization problems while keeping computational costs reasonable. The carnivorous plant algorithm (CPA) is a newly proposed metaheuristic algorithm, inspired by its foraging strategies of attraction, capture, digestion, and reproduction. However, the CPA is not without its shortcomings. In this paper, an enhanced multistrategy carnivorous plant algorithm called the UCDCPA is developed. In the proposed framework, a good point set, Cauchy mutation, and differential evolution are introduced to increase the algorithm’s calculation precision and convergence speed as well as heighten the diversity of the population and avoid becoming trapped in local optima. The superiority and practicability of the UCDCPA are illustrated by comparing its experimental results with several algorithms against the CEC2014 and CEC2017 benchmark functions, and five engineering designs. Additionally, the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank-sum tests. The findings show that these introduced strategies provide some improvements in the performance of the CPA, and the accuracy and stability of the optimization results provided by the proposed UCDCPA are competitive against all algorithms. To conclude, the proposed UCDCPA offers a good alternative to solving optimization issues. Full article

(This article belongs to the Special Issue Nature-Inspired Computer Algorithms)

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15 pages, 2142 KiB

Open AccessArticle

Biology-Informed Recurrent Neural Network for Pandemic Prediction Using Multimodal Data

by Zhiwei Ding, Feng Sha, Yi Zhang and Zhouwang Yang

Biomimetics 2023, 8(2), 158; https://doi.org/10.3390/biomimetics8020158 - 14 Apr 2023

Viewed by 1661

Abstract

In the biomedical field, the time interval from infection to medical diagnosis is a random variable that obeys the log-normal distribution in general. Inspired by this biological law, we propose a novel back-projection infected–susceptible–infected-based long short-term memory (BPISI-LSTM) neural network for pandemic prediction. [...] Read more.

In the biomedical field, the time interval from infection to medical diagnosis is a random variable that obeys the log-normal distribution in general. Inspired by this biological law, we propose a novel back-projection infected–susceptible–infected-based long short-term memory (BPISI-LSTM) neural network for pandemic prediction. The multimodal data, including disease-related data and migration information, are used to model the impact of social contact on disease transmission. The proposed model not only predicts the number of confirmed cases, but also estimates the number of infected cases. We evaluate the proposed model on the COVID-19 datasets from India, Austria, and Indonesia. In terms of predicting the number of confirmed cases, our model outperforms the latest epidemiological modeling methods, such as vSIR, and intelligent algorithms, such as LSTM, for both short-term and long-term predictions, which shows the superiority of bio-inspired intelligent algorithms. In general, the use of mobility information improves the prediction accuracy of the model. Moreover, the number of infected cases in these three countries is also estimated, which is an unobservable but crucial indicator for the control of the pandemic. Full article

(This article belongs to the Special Issue Nature-Inspired Computer Algorithms)

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18 pages, 7163 KiB

Open AccessArticle

Optimization Strategy of Rolling Mill Hydraulic Roll Gap Control System Based on Improved Particle Swarm PID Algorithm

by Ying Yu, Ruifeng Zeng, Yuezhao Xue and Xiaoguo Zhao

Biomimetics 2023, 8(2), 143; https://doi.org/10.3390/biomimetics8020143 - 31 Mar 2023

Cited by 1 | Viewed by 2290

Abstract

Medium and heavy plates are important strategic materials, which are widely used in many fields, such as large ships, weapons and armor, large bridges, and super high-rise buildings. However, the traditional control technology cannot meet the high-precision control requirements of the roll gap [...] Read more.

Medium and heavy plates are important strategic materials, which are widely used in many fields, such as large ships, weapons and armor, large bridges, and super high-rise buildings. However, the traditional control technology cannot meet the high-precision control requirements of the roll gap of the thick plate mill, resulting in errors in the thickness of the medium and heavy plate, thereby reducing the quality of the product. In response to this problem, this paper takes the 5500 mm thick plate production line as the research background, and establishes the model of the rolling mill plate thickness automatic control system, using the Ziegler–Nichol response curve method (Z-N), particle swarm optimization (PSO) algorithm and linear weight particle swarm optimization (LWPSO) algorithm, respectively, optimizes the parameter setting of the PID controller of the system, and uses OPC UA communication technology to realize the online semi-physical simulation of Siemens S7-1500 series PLC (Siemens, Munich, Germany) and MATLAB R2018b (The MathWorks, Natick, Massachusetts, United States). Comparative studies show that when the same roll gap displacement step signal is given, the overshoot of the system response using the LWPSO algorithm is reduced by 14.26% and 10.18% compared with the Z-N algorithm and the PSO algorithm, and the peak time is advanced by 0.31 s and 0.05 s. The stabilization time is reduced by 3.71 s and 4.31 s, which effectively improves the control accuracy and speed of the system and has stronger anti-interference ability. It has certain engineering reference and application value. Full article

(This article belongs to the Special Issue Nature-Inspired Computer Algorithms)

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21 pages, 5729 KiB

Open AccessArticle

Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

by Khizer Mehmood, Naveed Ishtiaq Chaudhary, Zeshan Aslam Khan, Khalid Mehmood Cheema and Muhammad Asif Zahoor Raja

Biomimetics 2023, 8(2), 141; https://doi.org/10.3390/biomimetics8020141 - 30 Mar 2023

Cited by 15 | Viewed by 1601

Abstract

In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based [...] Read more.

In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels. The comparative performance analyses with standard counterparts indicate the worth of the ICGWO for ARX model identification, while the statistical analyses endorse the efficacy of the proposed chaotic scheme in terms of accuracy, robustness, and reliability. Full article

(This article belongs to the Special Issue Nature-Inspired Computer Algorithms)

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