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Design on a wireless mechanomyography acquisition equipment and feature selection for lower limb motion recognition

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期77卷 000-000页

作者： Zhang, Hanyang Wang, Xinping Zhang, Yue Cao, Gangsheng Xia, Chunming East China Univ Sci & Technol Dept Mech Engn Shanghai 200237 Peoples R China Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai 201620 Peoples R China

Mechanomyography (MMG) is a kind of biomedical signal with great research value. This paper designed a novel wireless MMG signal acquisition system composed of modular nodes and core board. The modular nodes were worn on different muscle of the lower limbs, collecting movement data of the corresponding part, and transmitted it to the core board wirelessly. The core board was connected to the computer through the USB to achieve the wireless collection and real-time display of MMG signals. In order to improve the real-time performance, this paper adopted four swarm intelligence algorithms (GA, PSO, WOA, SSA) and three improved algorithms (IPSO, IWOA, ISSA) for feature selection to reduce the redundancy of information. In this study, the different effects of different feature selection methods on the recognition of eight types of lower limb movements based on MMG signals were discussed by comparing the classification accuracy, number of iterations and calculation time. The results show that swarm intelligence algorithms have merits in this type of feature selection problem, and GA has the most obvious effect in improving classification accuracy, but it requires more iterations and calculation time, while the classification accuracy of IPSO is close to that of GA, and it has advantages in time consumption.

关键词： Mechanomyography Wireless acquisition Feature selection swarm intelligence algorithms Pattern recognition

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Chameleon swarm Algorithm Assisted Optimization of U-Slot Patch Antenna for Quad-Band Applications

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IEEE ACCESS 2022年 10卷 74152-74163页

作者： Uluslu, Ahmet Istanbul Univ Cerrahpasa Dept Elect & Automat TR-34500 Istanbul Turkey

Existing optimization algorithms are insufficient in the face of problems with difficult measurement functions as well as a large number of design parameters. Therefore, to achieve such challenging applications, the Chameleon swarm algorithm and its variants, which belong to the family of meta-heuristic algorithms that have not been used in any antenna optimization study before, are used together with 3 different objective functions fitted from mathematical models. Then, a U-slot antenna application with 12 different variable design parameters with resonance frequencies of 3.5 GHz, 3.7 GHz, 5.2 GHz and 5.8 GHz is considered as a multidimensional and single-objective optimization problem. In this study, first of all, the success of the algorithm is reinforced by comparing the performance with other commonly used single and multi-objective optimization algorithms. In addition, the results obtained with different population parameters, weight coefficients, objective functions and variant models were compared. All these processes are compared within themselves, and the antenna results of the most successful result are displayed as 3D electromagnetic simulation. The results show that the optimization processes proposed for an antenna designer are a safe, practical and efficient solution for multidimensional and single-objective antenna optimization applications. In addition, any optimization problem with a large number of variable design parameters can undoubtedly be adapted to the Chameleon swarm algorithm.

关键词： Optimization Antennas Microstrip antennas Heuristic algorithms Mathematical models Substrates Patch antennas Quad-band eCSA patch antenna multidimensional optimization optimization techniques chameleon swarm algorithm evolutionary algorithms swarm intelligence algorithms

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Aperture-coupled Bowtie Patch Antenna Design for Autonomous Driving Applications

Aperture-coupled Bowtie Patch Antenna Design for Autonomous ...

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International Workshop on Antenna Technology (iWAT)

作者： Iliadis, Lazaros A. Boursianis, Achilles D. Rekkas, Vasileios P. Zaharis, Zaharias D. Koulouridis, Stavros Goudos, Sotirios K. Aristotle Univ Thessaloniki ELEDIA AUTH Sch Phys Thessaloniki Greece Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki Greece Univ Patras Dept Elect & Comp Engn Patras Greece

ISBN: (纸本)9798350334081

Autonomous driving will reshape transportation networks, offering at the same time many benefits such as safety, reliable vehicle-to-vehicle communication, and extended telecommunications. For a self-driving vehicle to navigate freely and perceive its environment, it is necessary to be equipped with many sensors. Automotive radars and antenna arrays that operate at 76-81 GHz have been proposed as key elements in future autonomous vehicles. In this work, an aperture-coupled bowtie antenna is designed utilizing hunger games search optimization. The proposed element achieves quite satisfactory values of return loss and gain, as well as broadband operation at 76 GHz - 81 GHz.

关键词： Aperture-coupling bowtie antenna 79 GHz autonomous driving hunger games search swarm intelligence algorithms

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Optimization based on modified swarm intelligence techniques for a stand-alone hybrid photovoltaic/diesel/battery system

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年 51卷

作者： Maleki, Akbar Shahrood Univ Technol Fac Mech Engn Shahrood Iran

Stand-alone hybrid energy system (SHES) based on photovoltaic/diesel/battery, as one of the main energy resources in remote areas, is widely used for power generation. The optimal sizing and configuration of SHES is essential for achieving a cost-effective and reliable power supply. This article presents five improved particle swarm intelligence algorithms to solve the optimal configuration problem of stand-alone hybrid photovoltaic/ diesel/battery systems. The techniques include five improved particle swarm optimization (PSO) algorithms based on the modified particle velocity and particle position to improve of the local and global searches in all iterations. The objective of the optimization is to determine the optimal sizing of the SHES components (photovoltaic (PV) panels, the diesel power generator, and batteries) to compose a cost-effective and reliable scheme. The improved algorithms are compared by original PSO algorithm. The effects of reliability index, loss of load supply probability, on the optimization of the SHES have been investigated by each algorithm over 30 independent runs. The effect of the maximum number of iteration on the optimization of the hybrid system by the best algorithm for different reliability values is examined. The results show the superiority of the PSO-III in terms of fast convergence, simulation time, and accuracy in comparison to other approachs. Finally, a sensitivity analysis of the SHES shows that the value of total net annual cost (TNAC) is increased around 18% due to the decrease of reliability indexes from 10% to 2%. Also, the mean value of TNAC is decreased around 11.9% due to the increase of the number of iteration from 25 to 175.

关键词： Optimal sizing Stand-alone hybrid photovoltaic diesel battery system swarm intelligence algorithms Cost-effective and reliable system

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A New Improved Multiple Behaviors Whale Optimization Algorithm 7

A New Improved Multiple Behaviors Whale Optimization Algorit...

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7th International Conference on Natural Language Processing and Information Retrieval (NLPIR)

作者： Zhang, Yang Kanagawa Univ Dept Comp Sci Yokohama Kanagawa Japan

ISBN: (纸本)9798400709227

The Whale Optimization Algorithm (WOA) has demonstrated superior performance compared with other swarm intelligence algorithms, making it widely used and applied to practical problemsolving. In this paper, an enhanced WOA is proposed, referred to as the Improved Whale Optimization Algorithm (IWOA). The convergence factor of the traditional WOA is replaced with a random trigonometric function, which considers both the historical and current best positions of the whale population during the bubble-encircling search phase, thereby accelerating the global search speed of the whale population. In the random-encircling search phase, a combination of equiprobable random selection and roulette wheel selection is used to select random positions. Additionally, a random perturbation of individuals is introduced in the spiral search phase to prevent premature convergence to local optima. The effectiveness and robustness of the IWOA are evaluated using 4 benchmark function problems, and the results are compared with three other algorithms including particle swarm optimization (PSO). The experimental results demonstrate that the proposed IWOA outperforms other algorithms in terms of convergence speed and accuracy.

关键词： Optimization Problem swarm intelligence algorithms Benchmark Function Problems Whale Optimization Algorithm

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The Piranha Foraging Optimization Algorithm Oriented to Numerical Optimization Problems 7

The Piranha Foraging Optimization Algorithm Oriented to Nume...

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7th International Conference on Robotics and Automation Engineering (ICRAE)

作者： Cao, Shuai Qian, Qian Cao, Yongjun Li, Wenwei Zhou, Lei He, Hongan Huang, Weixi Liang, Jianan Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming Yunnan Peoples R China South China Robot Innovat Res Inst Robot Lab Foshan Peoples R China South Glass Technol Co LTD Foshan Peoples R China

ISBN: (纸本)9781665489188

This paper provides an efficient meta-heuristic optimization algorithm for solving continuous optimization problems in the field of numerical and engineering optimization: Piranha Foraging Optimization Algorithm (PFOA). The algorithm is inspired by the flexible and mobile foraging behavior of piranhas, which are classified into three modes of localized group attack, bloodthirsty swarming attack, and scavenging foraging, and two dynamic search behaviors of exploration and exploitation are constructed by simulating the above behaviors. PFOA allows the population to be diverse at different times of the search by means of a non-linear parameter adjustment strategy, a piranha population survival strategy and a reverse escape search strategy. Using visual means to evaluate the optimization efficiency of PFOA, this paper developed experiments on 23 standard benchmark functions and compared the results with 15 well-known meta-heuristics. Experimental results based on statistical methods such as Wilcoxon rank sum test and Friedman test in multiple dimensions (30, 50, 100 and fixed dimensions) show significant differentiation, stable and significant improvement in algorithm performance compared to other compared algorithms. The unique advantages of PFOA in terms of convergence speed and exploration utilization balance can avoid getting trapped in local optimum regions and effectively solve optimization problems with complex search spaces of multiple dimensions.

关键词： swarm intelligence algorithms piranha foraging optimization algorithm meta-heuristic algorithms

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An improved SABO model for solving architectural engineering design problems

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2025年第5期19卷 1374-1405页

作者： Zhang, Bangcheng Song, Jingyuan Li, Bo Hou, Zhaojun Ren, Yuheng Yin, Yungao Hu, Bowen Changchun Inst Technol Coll Jilin Emergency Management Changchun 130012 Peoples R China Changchun Inst Technol Sch Comp Technol & Engn Changchun 130012 Peoples R China Changchun Inst Technol Sch Mech & Elect Engn Changchun 130012 Peoples R China

This study proposes an enhanced version of the Subtraction Average-Based Optimizer (SABO) to address the slow convergence, local optima, and imbalance between exploration and exploitation inherent in the original algorithm. The enhancement incorporates chaotic mapping for uniform population initialization and introduces a sinusoidal function to enhance global search efficiency. The enhanced SABO algorithm is evaluated using 23 benchmark functions from IEEE CEC2005, and it outperforms several state-of-the-art algorithms in terms of convergence speed and accuracy. Compared to existing swarm intelligence algorithms, the enhanced SABO algorithm consistently demonstrates superior performance across all 23 benchmark functions. The practical applicability of the enhanced SABO algorithm is further validated through its application to a real-world engineering design problem. Experimental and statistical analyses confirm that the enhanced SABO algorithm achieves rapid convergence, demonstrates superior optimization capability, and exhibits strong effectiveness in addressing complex multimodal functions. Furthermore, the enhanced SABO algorithm effectively maintains a balance between exploration and exploitation, significantly enhancing solution quality in multi-objective optimization problems.

关键词： Evolutionary Computing Improved SABO Multi-objective optimization SABO swarm intelligence algorithms

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Chaotic Coyote Optimization Algorithm

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JOURNAL OF AMBIENT intelligence AND HUMANIZED COMPUTING 2021年第5期13卷 2807-2827页

作者： Tong, Huawei Zhu, Yun Pierezan, Juliano Xu, Youyun Coelho, Leandro dos Santos Nanjing Univ Posts & Telecommun Nanjing 210003 Peoples R China Gannan Normal Univ Ganzhou 341000 Peoples R China Fed Univ Parana UFPR Curitiba Parana Brazil

Coyote Optimization Algorithm (COA) is classified as both swarm intelligence and evolutionary heuristic algorithms. However, getting trapped in a poor local optimum and the low convergence speed are the weaknesses of COA obviously. Due to these weaknesses, this paper proposes a new algorithm named Chaotic Coyote Optimization Algorithm (CCOA) which focusing on COA equipped with chaotic maps. Through utilising ten well-known benchmark functions, experimental results are recorded in tables and drawn in figures to provide a sharp contrast. The performance of CCOA and COA are discussed, which proves CCOA outperforms COA guaranteeing rapid global convergence rate.

关键词： Coyote Optimization Algorithm Chaos theory swarm intelligence algorithms Optimization

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Multilevel thresholding of images with improved Otsu thresholding by black widow optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第18期80卷 28217-28243页

作者： Al-Rahlawee, Anfal Thaer Hussein Rahebi, Javad Altinbas Univ Dept Elect & Comp Engn Istanbul Turkey

One of the most important methods of image processing is image thresholding, which is based on image histogram analysis. These methods analyze the image histogram diagram and try to present optimal values for the image thresholds so that the image regions can be distinguished by these thresholds. Thresholding is a popular method in image processing and is used in most research related to image segmentation due to its accuracy and efficiency. Multi-level thresholding, such as the Otsu method, is one of the most common methods of thresholding image processing. These methods have high computational complexity despite their accuracy and efficiency. When the number of thresholds used increases, these methods lose their efficiency due to increased complexity and execution time. One of the ways to find thresholds in the Otsu threshold method is to use metaheuristic algorithms such as the Black Widow Spider Optimization Algorithm. These algorithms can find the appropriate thresholds for the image at the logical time. In the proposed method, each threshold is a component or one dimension of a solution of the Black Widow Spider Optimization Algorithm, and an attempt is made to calculate the optimal threshold value without high complexity by this algorithm. Experiments on several standard images show that the proposed algorithm finds better thresholds than the particle swarm optimization algorithm, the firefly algorithm, the genetic algorithm, and the gray wolf optimization algorithm. The analysis shows that the proposed method in the PSNR index has a better value in 83.33% of the experiments than other algorithms and also in 80% of the experiments the proposed method has a better SSIM index than these methods. Analysis of the proposed algorithm on several pertussis images also shows that the proposed method has a good ability to threshold medical images such as brain tumors and optic disc detection in human retinal images.

关键词： Thresholding Otsu swarm intelligence algorithms Black widow optimization algorithm

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Integrating Grasshopper Optimization Algorithm with Local Search for Solving Data Clustering Problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL intelligence SYSTEMS 2021年第1期14卷 783-793页

作者： El-Shorbagy, M. A. Ayoub, A. Y. Prince Sattam Bin Abdulaziz Univ Coll Sci & Humanities Al Kharj Dept Math Al Kharj 11942 Saudi Arabia Menoufia Univ Fac Engn Shebin Kom Dept Basic Engn Sci Shibin Al Kawm Egypt

This paper proposes a hybrid approach for solving data clustering problems. This hybrid approach used one of the swarm intelligence algorithms (SIAs): grasshopper optimization algorithm (GOA) due to its robustness and effectiveness in solving optimization problems. in addition, a local search (LS) strategy is applied to enhance the solution quality and access to optimal data clustering. The proposed algorithm is divided into two stages, the first of which aims to use GOA to prevent getting trapped in local minima and to find an approximate solution. While the second stage aims by LS to increase LS performance and obtain the best optimal solution. In other words, the proposed algorithm combines the exploitation capability of GOA and the discovery capability of LS, and integrates the merits of both GOA and LS. In addition, 7 well-known datasets that commonly used in several studies are used to validate the proposed technique. The results of the proposed methodology are compared to previous studies;where statistical analysis, for the various algorithms, indicated the superiority of the proposed methodology over other algorithms and its ability to solve this type of problem. (C) 2021 The Authors. Published by Atlantis Press B.V.

关键词： Data clustering problems grasshopper optimization algorithm local search optimization swarm intelligence algorithms

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