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

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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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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An Improved Multi-Objective Particle swarm Optimization With TOPSIS and Fuzzy Logic for Optimizing Trapezoidal Labyrinth Weir

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IEEE ACCESS 2021年 9卷 25458-25472页

作者： Mahmoud, Ali Yuan, Xiaohui Kheimi, Marwan Almadani, Mohammad A. Hajilounezhad, Taher Yuan, Yanbin Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China China Three Gorges Univ Hubei Prov Key Lab Operat & Control Cascaded Hydr Yichang 443002 Peoples R China King Abdulaziz Univ Fac Engn Dept Civil & Environm Engn Rabigh Branch Jeddah 21589 Saudi Arabia Univ Missouri Dept Mech & Aerosp Engn Columbia MO 65211 USA Wuhan Univ Technol Sch Resources & Environm Engn Wuhan 430070 Peoples R China

Labyrinth Weir (LW) is a popular control structure that passes a significantly higher flow rate compared to the linear weirs. In order to approach the optimal design of a trapezoidal LW, a multi-objective problem is defined to concurrently minimize the LW consumed concrete volume and maximize its discharge capacity. Simultaneously, a Radial Basis function Neural Networks (RBFNN) is designed and used for estimating LW discharge coefficient (C-d) according to the existing experimental results. An improved multi-objective particle swarm optimization (MOPSO) algorithm named TOPSIS Fuzzy MOPSO (TFMOPSO) is proposed to solve the LW optimization problem. This algorithm utilizes the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the solutions, while a fuzzy inference system is developed to select the algorithm strategy for finding two leaders among the non-dominated solutions. The performance of the proposed TFMOPSO has been tested on the optimization problem of the LW of the Ute dam. The results of TFMOPSO, along with three other state-of-the-art multi-objective algorithms, are explored in terms of hypervolume, coverage, and spacing metrics. It is demonstrated that the TFMOPSO outperforms other algorithms and studies for solving the LW multi-objective optimization problem for the case of Ute dam. Also, RBFNN is found to be one of the most appropriate approaches among studied algorithms in estimating the discharge coefficient of LW, while Pareto optimal solutions from TFMOPSO exhibit a significant improvement compared to the original design of Ute dam LW.

关键词： Optimization Discharges (electric) Dams Shape Particle swarm optimization Mathematical model Genetic algorithms Labyrinth Weir swarm intelligence algorithms cost reduction shape optimization soft computing techniques

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Improved fuzzy clustering with swarm intelligence for medical image analysis 6

Improved fuzzy clustering with swarm intelligence for medica...

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6th International Conference on Signal Processing and Intelligent Systems (ICSPIS)

作者： Gholami, Fateme Sadjad Univ Technol Fac Comp Engn & Informat Technol Mashhad Razavi Khorasan Iran

ISBN: (纸本)9781728186290

One of the challenges in the world today is the existence of a variety of diseases, some of which require the processing of medical images to diagnose and evaluate, such as images of brain tumors. One of the methods of analyzing and evaluating patients related to the brain is magnetic resonance imaging(MRI). Data mining methods such as clustering can be used to analyze magnetic resonance images. Clustering techniques can take the area of brain tumors from brain tissue and use it to diagnose disease. Various clustering methods have been proposed so far, one of which is the fuzzy clustering or FCM method, and it has a high accuracy for clustering and segmentation of brain tissues. Fuzzy clustering is less sensitive to the noise in these images and therefore its segmentation accuracy is somewhat desirable. To improve the performance of FCM clustering, in identifying the edges and borders of tumors, it is necessary to select the optimal clustering centers. The optimal selection of cluster centers increases its accuracy in learning and segmentation. Given that the optimal selection of cluster centers is an optimization method, metaheuristic algorithms can be used for this purpose. In this research, swarm intelligence algorithms have been used to optimally select cluster centers in FCM. The analysis of the proposed method on a set of images of brain magnetic resonance shows that the proposed algorithm has the specificity, sensitivity, and accuracy of 96.87%, 88.36%, and 91.32% in the diagnosis of brain tumors, respectively. The proposed method of hybrid methods, such as the fuzzy method, better detects brain tumors.

关键词： swarm intelligence algorithms image processing magnetic resonance brain brain tumor

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Hierarchical structure-based joint operations algorithm for global optimization

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swarm AND EVOLUTIONARY COMPUTATION 2023年 79卷

作者： Sun, Gaoji Han, Rongqing Deng, Libao Li, Chunlei Yang, Guoqing Zhejiang Normal Univ Coll Econ & Management Jinhua Peoples R China Harbin Inst Technol Sch Informat Sci & Engn Weihai Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin Peoples R China Hebei Univ Sch Management Baoding Peoples R China

Joint operations algorithm (JOA) is a metaheuristic algorithm based on joint operations strategy in military theory, which incorporates three core operations - offensive, defensive and regroup - and has excellent performance in global optimization problems. To enhance the optimization performance of the original JOA, we re-examine the positioning of the three core operations in balancing global exploration and local exploitation, and propose a hierarchical structure-based JOA variant (abbreviated as HSJOA) by adjusting their execution mechanism. In addition, we redesign the three core operations to give full play to their synergy effect. In the new offensive operations, we simplify its specific execution strategy, but introduce an adjustment parameter to retain valuable position information. In the modified defensive operations, we integrate the influence of officer and elite comrades on soldiers to design a Gaussian distribution-based conservative defensive strategy and one Cauchy distribution-based aggressive defensive strategy. In the novel regroup operations, we replace the original random division strategy with a sorting-based division scheme. To evaluate the optimization performance of HSJOA, we conduct comparison experiments using nine excellent algorithms to deal with four real-life optimization problems and thirty test functions from IEEE CEC 2014 testbed. The comparison results show that the overall optimization performance of HSJOA is significantly better than the original JOA, the outstanding variants (TAPSO and GODE) of two well-known algorithms and four recently proposed algorithms (EO, SOA, MPA and WOA), while inferior to two winners of IEEE CEC competition (L-SHADE and EBOwithCMAR), but HSJOA clearly outperforms L-SHADE and EBOwithCMAR in terms of the runtime consumption.

关键词： Joint operations algorithm Metaheuristic algorithms swarm intelligence algorithms Evolutionary algorithms Global optimization

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