Pub Date : 2024-10-21DOI: 10.3390/biomimetics9100648
Yijie Zhang, Yuhang Cai
The high dimensionality of large datasets can severely impact the data mining process. Therefore, feature selection becomes an essential preprocessing stage, aimed at reducing the dimensionality of the dataset by selecting the most informative features while improving classification accuracy. This paper proposes a novel binary Gray Wolf Optimization algorithm to address the feature selection problem in classification tasks. Firstly, the historical optimal position of the search agent helps explore more promising areas. Therefore, by linearly combining the best positions of the search agents, the algorithm's exploration capability is increased, thus enhancing its global development ability. Secondly, the novel quadratic interpolation technique, which integrates population diversity with local exploitation, helps improve both the diversity of the population and the convergence accuracy. Thirdly, chaotic perturbations (small random fluctuations) applied to the convergence factor during the exploration phase further help avoid premature convergence and promote exploration of the search space. Finally, a novel transfer function processes feature information differently at various stages, enabling the algorithm to search and optimize effectively in the binary space, thereby selecting the optimal feature subset. The proposed method employs a k-nearest neighbor classifier and evaluates performance through 10-fold cross-validation across 32 datasets. Experimental results, compared with other advanced algorithms, demonstrate the effectiveness of the proposed algorithm.
大型数据集的高维度会严重影响数据挖掘过程。因此,特征选择成为一个必不可少的预处理阶段,目的是通过选择信息量最大的特征来降低数据集的维度,同时提高分类准确率。本文提出了一种新颖的二元灰狼优化算法来解决分类任务中的特征选择问题。首先,搜索代理的历史最优位置有助于探索更有前景的领域。因此,通过线性组合搜索代理的最佳位置,可以提高算法的探索能力,从而增强其全局开发能力。其次,新颖的二次插值技术将种群多样性与局部开发相结合,有助于提高种群多样性和收敛精度。第三,在探索阶段对收敛因子施加混沌扰动(小的随机波动),有助于避免过早收敛,促进对搜索空间的探索。最后,新颖的转移函数在不同阶段对特征信息进行不同处理,使算法能够在二进制空间中有效搜索和优化,从而选择最佳特征子集。所提出的方法采用了 k 近邻分类器,并通过 32 个数据集的 10 倍交叉验证来评估性能。实验结果与其他先进算法相比,证明了所提算法的有效性。
{"title":"Explorative Binary Gray Wolf Optimizer with Quadratic Interpolation for Feature Selection.","authors":"Yijie Zhang, Yuhang Cai","doi":"10.3390/biomimetics9100648","DOIUrl":"https://doi.org/10.3390/biomimetics9100648","url":null,"abstract":"<p><p>The high dimensionality of large datasets can severely impact the data mining process. Therefore, feature selection becomes an essential preprocessing stage, aimed at reducing the dimensionality of the dataset by selecting the most informative features while improving classification accuracy. This paper proposes a novel binary Gray Wolf Optimization algorithm to address the feature selection problem in classification tasks. Firstly, the historical optimal position of the search agent helps explore more promising areas. Therefore, by linearly combining the best positions of the search agents, the algorithm's exploration capability is increased, thus enhancing its global development ability. Secondly, the novel quadratic interpolation technique, which integrates population diversity with local exploitation, helps improve both the diversity of the population and the convergence accuracy. Thirdly, chaotic perturbations (small random fluctuations) applied to the convergence factor during the exploration phase further help avoid premature convergence and promote exploration of the search space. Finally, a novel transfer function processes feature information differently at various stages, enabling the algorithm to search and optimize effectively in the binary space, thereby selecting the optimal feature subset. The proposed method employs a k-nearest neighbor classifier and evaluates performance through 10-fold cross-validation across 32 datasets. Experimental results, compared with other advanced algorithms, demonstrate the effectiveness of the proposed algorithm.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"9 10","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11505495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142494117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-21DOI: 10.3390/biomimetics9100647
Shaoming Qiu, Jikun Dai, Dongsheng Zhao
The UAV path planning algorithm has many applications in urban environments, where an effective algorithm can enhance the efficiency of UAV tasks. The main concept of UAV path planning is to find the optimal flight path while avoiding collisions. This paper transforms the path planning problem into a multi-constraint optimization problem by considering three costs: path length, turning angle, and collision avoidance. A multi-strategy improved POA algorithm (IPOA) is proposed to address this. Specifically, by incorporating the iterative chaotic mapping method with refracted reverse learning strategy, nonlinear inertia weight factors, the Levy flight mechanism, and adaptive t-distribution variation, the convergence accuracy and speed of the POA algorithm are enhanced. In the CEC2022 test functions, IPOA outperformed other algorithms in 69.4% of cases. In the real map simulation experiment, compared to POA, the path length, turning angle, distance to obstacles, and flight time improved by 8.44%, 5.82%, 4.07%, and 9.36%, respectively. Similarly, compared to MPOA, the improvements were 4.09%, 0.76%, 1.85%, and 4.21%, respectively.
{"title":"Path Planning of an Unmanned Aerial Vehicle Based on a Multi-Strategy Improved Pelican Optimization Algorithm.","authors":"Shaoming Qiu, Jikun Dai, Dongsheng Zhao","doi":"10.3390/biomimetics9100647","DOIUrl":"https://doi.org/10.3390/biomimetics9100647","url":null,"abstract":"<p><p>The UAV path planning algorithm has many applications in urban environments, where an effective algorithm can enhance the efficiency of UAV tasks. The main concept of UAV path planning is to find the optimal flight path while avoiding collisions. This paper transforms the path planning problem into a multi-constraint optimization problem by considering three costs: path length, turning angle, and collision avoidance. A multi-strategy improved POA algorithm (IPOA) is proposed to address this. Specifically, by incorporating the iterative chaotic mapping method with refracted reverse learning strategy, nonlinear inertia weight factors, the Levy flight mechanism, and adaptive t-distribution variation, the convergence accuracy and speed of the POA algorithm are enhanced. In the CEC2022 test functions, IPOA outperformed other algorithms in 69.4% of cases. In the real map simulation experiment, compared to POA, the path length, turning angle, distance to obstacles, and flight time improved by 8.44%, 5.82%, 4.07%, and 9.36%, respectively. Similarly, compared to MPOA, the improvements were 4.09%, 0.76%, 1.85%, and 4.21%, respectively.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"9 10","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11505695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142494145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-21DOI: 10.3390/biomimetics9100646
Fengzhen Tang, Junhuai Zhang, Chi Zhang, Lianqing Liu
Spiking neural networks (SNNs), using action potentials (spikes) to represent and transmit information, are more biologically plausible than traditional artificial neural networks. However, most of the existing SNNs require a separate preprocessing step to convert the real-valued input into spikes that are then input to the network for processing. The dissected spike-coding process may result in information loss, leading to degenerated performance. However, the biological neuron system does not perform a separate preprocessing step. Moreover, the nervous system may not have a single pathway with which to respond and process external stimuli but allows multiple circuits to perceive the same stimulus. Inspired by these advantageous aspects of the biological neural system, we propose a self-adaptive encoding spike neural network with parallel architecture. The proposed network integrates the input-encoding process into the spiking neural network architecture via convolutional operations such that the network can accept the real-valued input and automatically transform it into spikes for further processing. Meanwhile, the proposed network contains two identical parallel branches, inspired by the biological nervous system that processes information in both serial and parallel. The experimental results on multiple image classification tasks reveal that the proposed network can obtain competitive performance, suggesting the effectiveness of the proposed architecture.
{"title":"Brain-Inspired Architecture for Spiking Neural Networks.","authors":"Fengzhen Tang, Junhuai Zhang, Chi Zhang, Lianqing Liu","doi":"10.3390/biomimetics9100646","DOIUrl":"https://doi.org/10.3390/biomimetics9100646","url":null,"abstract":"<p><p>Spiking neural networks (SNNs), using action potentials (spikes) to represent and transmit information, are more biologically plausible than traditional artificial neural networks. However, most of the existing SNNs require a separate preprocessing step to convert the real-valued input into spikes that are then input to the network for processing. The dissected spike-coding process may result in information loss, leading to degenerated performance. However, the biological neuron system does not perform a separate preprocessing step. Moreover, the nervous system may not have a single pathway with which to respond and process external stimuli but allows multiple circuits to perceive the same stimulus. Inspired by these advantageous aspects of the biological neural system, we propose a self-adaptive encoding spike neural network with parallel architecture. The proposed network integrates the input-encoding process into the spiking neural network architecture via convolutional operations such that the network can accept the real-valued input and automatically transform it into spikes for further processing. Meanwhile, the proposed network contains two identical parallel branches, inspired by the biological nervous system that processes information in both serial and parallel. The experimental results on multiple image classification tasks reveal that the proposed network can obtain competitive performance, suggesting the effectiveness of the proposed architecture.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"9 10","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11506793/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142494106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-21DOI: 10.3390/biomimetics9100649
Rafael Rojas-Galván, José R García-Martínez, Edson E Cruz-Miguel, José M Álvarez-Alvarado, Juvenal Rodríguez-Resendiz
This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms-grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)-were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution.
{"title":"Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems.","authors":"Rafael Rojas-Galván, José R García-Martínez, Edson E Cruz-Miguel, José M Álvarez-Alvarado, Juvenal Rodríguez-Resendiz","doi":"10.3390/biomimetics9100649","DOIUrl":"https://doi.org/10.3390/biomimetics9100649","url":null,"abstract":"<p><p>This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms-grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)-were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"9 10","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11505836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142494147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-20DOI: 10.3390/biomimetics9100645
Parth B Patel, Sun Latt, Karan Ravi, Mehdi Razavi
Nanomedicine, leveraging the unique properties of nanoparticles, has revolutionized the diagnosis and treatment of neurological diseases. Among various nanotechnological advancements, ultrasound-mediated drug delivery using micro- and nanobubbles offers promising solutions to overcome the blood-brain barrier (BBB), enhancing the precision and efficacy of therapeutic interventions. This review explores the principles, current clinical applications, challenges, and future directions of ultrasound-mediated drug delivery systems in treating stroke, brain tumors, neurodegenerative diseases, and neuroinflammatory disorders. Additionally, ongoing clinical trials and potential advancements in this field are discussed, providing a comprehensive overview of the impact of nanomedicine on neurological diseases.
{"title":"Clinical Applications of Micro/Nanobubble Technology in Neurological Diseases.","authors":"Parth B Patel, Sun Latt, Karan Ravi, Mehdi Razavi","doi":"10.3390/biomimetics9100645","DOIUrl":"https://doi.org/10.3390/biomimetics9100645","url":null,"abstract":"<p><p>Nanomedicine, leveraging the unique properties of nanoparticles, has revolutionized the diagnosis and treatment of neurological diseases. Among various nanotechnological advancements, ultrasound-mediated drug delivery using micro- and nanobubbles offers promising solutions to overcome the blood-brain barrier (BBB), enhancing the precision and efficacy of therapeutic interventions. This review explores the principles, current clinical applications, challenges, and future directions of ultrasound-mediated drug delivery systems in treating stroke, brain tumors, neurodegenerative diseases, and neuroinflammatory disorders. Additionally, ongoing clinical trials and potential advancements in this field are discussed, providing a comprehensive overview of the impact of nanomedicine on neurological diseases.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"9 10","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11506587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142494107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-20DOI: 10.3390/biomimetics9100644
Hyuk Jae Jang, Jaemin Jeon, Joo Ho Yun, Iqbal Shudha Tasnim, Soyeon Han, Heeyoung Lee, Sungguk An, Seungbeom Kang, Dongyeon Kim, Young Min Song
Artificial antireflective nanostructured surfaces, inspired by moth eyes, effectively reduce optical losses at interfaces, offering significant advantages in enhancing optical performance in various optoelectronic applications, including solar cells, light-emitting diodes, and cameras. However, their limited flexibility and low surface hardness constrain their broader use. In this study, we introduce a universal antireflective film by integrating nanostructures on both sides of a thin polycarbonate film. One side was thinly coated with Al2O3 for its high hardness, enhancing surface durability while maintaining flexibility. The opposite side was coated with SiO2 to optimize antireflective properties, making the film suitable for diverse environments (i.e., air, water, and adhesives). This dual-coating strategy resulted in a mechanically robust and flexible antireflective film with superior optical properties in various conditions. We demonstrated the universal capabilities of our antireflective film via optical simulations and experiments with the fabricated film in different environments.
{"title":"Dual-Coated Antireflective Film for Flexible and Robust Multi-Environmental Optoelectronic Applications.","authors":"Hyuk Jae Jang, Jaemin Jeon, Joo Ho Yun, Iqbal Shudha Tasnim, Soyeon Han, Heeyoung Lee, Sungguk An, Seungbeom Kang, Dongyeon Kim, Young Min Song","doi":"10.3390/biomimetics9100644","DOIUrl":"https://doi.org/10.3390/biomimetics9100644","url":null,"abstract":"<p><p>Artificial antireflective nanostructured surfaces, inspired by moth eyes, effectively reduce optical losses at interfaces, offering significant advantages in enhancing optical performance in various optoelectronic applications, including solar cells, light-emitting diodes, and cameras. However, their limited flexibility and low surface hardness constrain their broader use. In this study, we introduce a universal antireflective film by integrating nanostructures on both sides of a thin polycarbonate film. One side was thinly coated with Al<sub>2</sub>O<sub>3</sub> for its high hardness, enhancing surface durability while maintaining flexibility. The opposite side was coated with SiO<sub>2</sub> to optimize antireflective properties, making the film suitable for diverse environments (i.e., air, water, and adhesives). This dual-coating strategy resulted in a mechanically robust and flexible antireflective film with superior optical properties in various conditions. We demonstrated the universal capabilities of our antireflective film via optical simulations and experiments with the fabricated film in different environments.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"9 10","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11506741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142494112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-19DOI: 10.3390/biomimetics9100643
Yu-Hui Zhang, Zi-Jia Wang
In this paper, we present a two-phase multimodal optimization model designed to efficiently and accurately identify multiple optima. The first phase employs a population-based search algorithm to locate potential optima, while the second phase introduces a novel peak identification (PI) procedure to filter out non-optimal solutions, ensuring that each identified solution represents a distinct optimum. This approach not only enhances the effectiveness of multimodal optimization but also addresses the issue of redundant solutions prevalent in existing algorithms. We propose two PI algorithms: HVPI, which uses a hill-valley approach to distinguish between optima, without requiring prior knowledge of niche radii; and HVPIC, which integrates HVPI with bisecting K-means clustering to reduce the number of fitness evaluations (FEs). The performance of these algorithms was evaluated using the F-measure, a comprehensive metric that accounts for both the accuracy and redundancy in the solution set. Extensive experiments on a suite of benchmark functions and engineering problems demonstrated that our proposed algorithms achieved a high precision and recall, significantly outperforming traditional methods.
{"title":"Peak Identification in Evolutionary Multimodal Optimization: Model, Algorithms, and Metrics.","authors":"Yu-Hui Zhang, Zi-Jia Wang","doi":"10.3390/biomimetics9100643","DOIUrl":"https://doi.org/10.3390/biomimetics9100643","url":null,"abstract":"<p><p>In this paper, we present a two-phase multimodal optimization model designed to efficiently and accurately identify multiple optima. The first phase employs a population-based search algorithm to locate potential optima, while the second phase introduces a novel peak identification (PI) procedure to filter out non-optimal solutions, ensuring that each identified solution represents a distinct optimum. This approach not only enhances the effectiveness of multimodal optimization but also addresses the issue of redundant solutions prevalent in existing algorithms. We propose two PI algorithms: HVPI, which uses a hill-valley approach to distinguish between optima, without requiring prior knowledge of niche radii; and HVPIC, which integrates HVPI with bisecting K-means clustering to reduce the number of fitness evaluations (FEs). The performance of these algorithms was evaluated using the F-measure, a comprehensive metric that accounts for both the accuracy and redundancy in the solution set. Extensive experiments on a suite of benchmark functions and engineering problems demonstrated that our proposed algorithms achieved a high precision and recall, significantly outperforming traditional methods.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"9 10","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11505590/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142494146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.3390/biomimetics9100634
Hamid Furkan Suluova, Duc Truong Pham
Based on bee foraging behaviour, the Bees Algorithm (BA) is an optimisation metaheuristic algorithm which has found many applications in both the continuous and combinatorial domains. The original version of the Bees Algorithm has six user-selected parameters: the number of scout bees, the number of high-performing bees, the number of top-performing or "elite" bees, the number of forager bees following the elite bees, the number of forager bees recruited by the other high-performing bees, and the neighbourhood size. These parameters must be chosen with due care, as their values can impact the algorithm's performance, particularly when the problem is complex. However, determining the optimum values for those parameters can be time-consuming for users who are not familiar with the algorithm. This paper presents BA1, a Bees Algorithm with just one parameter. BA1 eliminates the need to specify the numbers of high-performing and elite bees and other associated parameters. Instead, it uses incremental k-means clustering to divide the scout bees into groups. By reducing the required number of parameters, BA1 simplifies the tuning process and increases efficiency. BA1 has been evaluated on 23 benchmark functions in the continuous domain, followed by 12 problems from the TSPLIB in the combinatorial domain. The results show good performance against popular nature-inspired optimisation algorithms on the problems tested.
{"title":"A New Single-Parameter Bees Algorithm.","authors":"Hamid Furkan Suluova, Duc Truong Pham","doi":"10.3390/biomimetics9100634","DOIUrl":"https://doi.org/10.3390/biomimetics9100634","url":null,"abstract":"<p><p>Based on bee foraging behaviour, the Bees Algorithm (BA) is an optimisation metaheuristic algorithm which has found many applications in both the continuous and combinatorial domains. The original version of the Bees Algorithm has six user-selected parameters: the number of scout bees, the number of high-performing bees, the number of top-performing or \"elite\" bees, the number of forager bees following the elite bees, the number of forager bees recruited by the other high-performing bees, and the neighbourhood size. These parameters must be chosen with due care, as their values can impact the algorithm's performance, particularly when the problem is complex. However, determining the optimum values for those parameters can be time-consuming for users who are not familiar with the algorithm. This paper presents BA<sub>1</sub>, a Bees Algorithm with just one parameter. BA<sub>1</sub> eliminates the need to specify the numbers of high-performing and elite bees and other associated parameters. Instead, it uses incremental k-means clustering to divide the scout bees into groups. By reducing the required number of parameters, BA<sub>1</sub> simplifies the tuning process and increases efficiency. BA<sub>1</sub> has been evaluated on 23 benchmark functions in the continuous domain, followed by 12 problems from the TSPLIB in the combinatorial domain. The results show good performance against popular nature-inspired optimisation algorithms on the problems tested.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"9 10","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11505725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142494089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.3390/biomimetics9100642
Mattew A Olawumi, Francis T Omigbodun, Bankole I Oladapo
This research aims to enhance the surface quality, mechanical properties, and biocompatibility of PEEK (polyether-ether-ketone) biomimetic dental implants through laser polishing. The objective is to improve osseointegration and implant durability by reducing surface roughness, increasing hydrophilicity, and enhancing mechanical strength. The methodology involved fabricating PEEK implants via FDM and applying laser polishing. The significant findings showed a 66.7% reduction in surface roughness, Ra reduced from 2.4 µm to 0.8 µm, and a 25.3% improvement in hydrophilicity, water contact angle decreased from 87° to 65°. Mechanical tests revealed a 6.3% increase in tensile strength (96 MPa to 102 MPa) and a 50% improvement in fatigue resistance (100,000 to 150,000 cycles). The strength analysis result showed a 10% increase in stiffness storage modulus from 1400 MPa to 1500 MPa. Error analysis showed a standard deviation of ±3% across all tests. In conclusion, laser polishing significantly improves the surface, mechanical, and biological performance of PEEK implants, making it a promising approach for advancing biomimetic dental implant technology.
{"title":"Improved Biocompatibility in Laser-Polished Implants.","authors":"Mattew A Olawumi, Francis T Omigbodun, Bankole I Oladapo","doi":"10.3390/biomimetics9100642","DOIUrl":"https://doi.org/10.3390/biomimetics9100642","url":null,"abstract":"<p><p>This research aims to enhance the surface quality, mechanical properties, and biocompatibility of PEEK (polyether-ether-ketone) biomimetic dental implants through laser polishing. The objective is to improve osseointegration and implant durability by reducing surface roughness, increasing hydrophilicity, and enhancing mechanical strength. The methodology involved fabricating PEEK implants via FDM and applying laser polishing. The significant findings showed a 66.7% reduction in surface roughness, Ra reduced from 2.4 µm to 0.8 µm, and a 25.3% improvement in hydrophilicity, water contact angle decreased from 87° to 65°. Mechanical tests revealed a 6.3% increase in tensile strength (96 MPa to 102 MPa) and a 50% improvement in fatigue resistance (100,000 to 150,000 cycles). The strength analysis result showed a 10% increase in stiffness storage modulus from 1400 MPa to 1500 MPa. Error analysis showed a standard deviation of ±3% across all tests. In conclusion, laser polishing significantly improves the surface, mechanical, and biological performance of PEEK implants, making it a promising approach for advancing biomimetic dental implant technology.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"9 10","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11505702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142494126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.3390/biomimetics9100637
Yuanqiong Chen, Zhijie Liu, Yujia Meng, Jianfeng Li
Glaucoma represents a significant global contributor to blindness. Accurately segmenting the optic disc (OD) and optic cup (OC) to obtain precise CDR is essential for effective screening. However, existing convolutional neural network (CNN)-based segmentation techniques are often limited by high computational demands and long inference times. This paper proposes an efficient end-to-end method for OD and OC segmentation, utilizing the lightweight MobileNetv3 network as the core feature-extraction module. Our approach combines boundary branches with adversarial learning, to achieve multi-label segmentation of the OD and OC. We validated our proposed approach across three public available datasets: Drishti-GS, RIM-ONE-r3, and REFUGE. The outcomes reveal that the Dice coefficients for the segmentation of OD and OC within these datasets are 0.974/0.900, 0.966/0.875, and 0.962/0.880, respectively. Additionally, our method substantially lowers computational complexity and inference time, thereby enabling efficient and precise segmentation of the optic disc and optic cup.
{"title":"Lightweight Optic Disc and Optic Cup Segmentation Based on MobileNetv3 Convolutional Neural Network.","authors":"Yuanqiong Chen, Zhijie Liu, Yujia Meng, Jianfeng Li","doi":"10.3390/biomimetics9100637","DOIUrl":"https://doi.org/10.3390/biomimetics9100637","url":null,"abstract":"<p><p>Glaucoma represents a significant global contributor to blindness. Accurately segmenting the optic disc (OD) and optic cup (OC) to obtain precise CDR is essential for effective screening. However, existing convolutional neural network (CNN)-based segmentation techniques are often limited by high computational demands and long inference times. This paper proposes an efficient end-to-end method for OD and OC segmentation, utilizing the lightweight MobileNetv3 network as the core feature-extraction module. Our approach combines boundary branches with adversarial learning, to achieve multi-label segmentation of the OD and OC. We validated our proposed approach across three public available datasets: Drishti-GS, RIM-ONE-r3, and REFUGE. The outcomes reveal that the Dice coefficients for the segmentation of OD and OC within these datasets are 0.974/0.900, 0.966/0.875, and 0.962/0.880, respectively. Additionally, our method substantially lowers computational complexity and inference time, thereby enabling efficient and precise segmentation of the optic disc and optic cup.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"9 10","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11506706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142494131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}