Recently, nature inspired algorithms (NIA) have been implemented to various fields of optimization problem such as biomedical engineering, electrical engineering, computer science and etc. The achievement of NIA in so...
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ISBN:
(纸本)9781538663219
Recently, nature inspired algorithms (NIA) have been implemented to various fields of optimization problem such as biomedical engineering, electrical engineering, computer science and etc. The achievement of NIA in solving optimization in these fields becoming the motivation to apply one of the NIA namely greywolf Optimizer (GWO) into overcurrent relay coordination problem. However, the current state of GWO suffers lack of exploration problem. Hence, the improvement of GWO has been proposed in this paper to enhance the exploration of original GWO. The improvement of GWO (IGWO) is implemented in finding the the optimal value of the Time Multiplier Setting (TMS) and Plug Setting (PS) in order to minimize the primary relays' operating time at the near end fault. Comprehensive simulation studies have been performed to demonstrate the reliability and efficiency of the proposed modification technique compared to the original GWO. The generated results have confirmed the proposed IGWO is able to improve the objective function of the overcurrent relay coordination problem.
This paper proposed the adaptive elitism-based immigration to improve the greywolfoptimization performance. The concept of elitism-based immigration is to generated immigrants and replaces it to the worst individual...
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ISBN:
(纸本)9781509052103
This paper proposed the adaptive elitism-based immigration to improve the greywolfoptimization performance. The concept of elitism-based immigration is to generated immigrants and replaces it to the worst individuals in the population. The elite immigrants in our proposed are mutated before replace to the worst individuals and the parameter to control mutation ratio and elite immigrants ratio are adaptive. The performances have been evaluated by using 7 well-known benchmark functions and compared with the traditional greywolf optimizer (GWO) algorithm, particle swarm optimization (PSO) and differential evolution (DE) algorithm. The experimental results showed that the proposed algorithm has ability to solving optimization problems.
this work presents a control technique for Mobile Robot Navigation using augmented reality (AR). This navigation technique is based on optimized Fuzzy Cognitive Map (FCM) and AR's Glyphs. AR's symbols are prov...
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ISBN:
(纸本)9781538657034
this work presents a control technique for Mobile Robot Navigation using augmented reality (AR). This navigation technique is based on optimized Fuzzy Cognitive Map (FCM) and AR's Glyphs. AR's symbols are provided by the overhead camera. The patterns are made up of glyphs and a clear path. Six practical test are manipulated to examine the strength of optimizing FCM by a mobile robot for navigation with AR's symbols. The experiment examined the effectiveness of a grey wolf optimization algorithm (GWOA) in optimizing the FCM. Two practical experiments confirm that AR's Glyphs are an effective symbol for a robot to navigation in an unknown environment. A practical experiment reveals that a robot can use AR to manage its intended movement. Augmented reality, such as the Glyphs and a simplified map, are an effective tool for mobile robots to use in navigation in unknown environments. A prototype system is made to navigate the mobile robot by using AR and FCM.
For complex structures, the solution process of existing cable force optimization methods for low-tower cable-stayed bridges is characterized by a significant number of matrix operations, which require substantial com...
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For complex structures, the solution process of existing cable force optimization methods for low-tower cable-stayed bridges is characterized by a significant number of matrix operations, which require substantial computing power and time. As a result, achieving a more accurate solution becomes exceedingly difficult. To tackle this challenge, we propose a new cable force optimization method that enhances the stress distribution of the cable-stayed cables in the completed state of the bridge. This approach minimizes the need for frequent adjustments to cable forces and alterations to the linear elevation of the beam bridge during construction. In this study, the low-tower cable-stayed bridge of the Lanjiang Bridge serves as the engineering background. By integrating finite element analysis with a multi-objective optimization method, we propose an optimization approach for the real-time correction of cable forces during the construction of long-span low-tower cable-stayed bridges. Within this optimization framework, the cable forces during construction are treated as variable parameters, while the linear elevation of the completed bridge is imposed as a constraint. The improved greywolfalgorithm is integrated with the finite element algorithm, and the key parameters of the support vector machine are optimized using this method, resulting in the optimal parameter combination predicted based on the training samples. The results indicate that after optimizing the support vector machine model using the improved greywolfalgorithm, the cable force distribution of the cable-stayed cables becomes more uniform, with a variance of 19.96. Additionally, the maximum displacement change of the main beam under the influence of the dead load is reduced by 33.48%. This method demonstrates high optimization efficiency and produces favorable outcomes, highlighting its value in calculating cable forces and guiding construction processes during the erection of cable-stayed cables for
Photovoltaic (PV) systems, in conjunction with battery energy storage systems (BESS), have emerged as promising solutions for sustainable energy generation and consumption. However, the performance of these systems ca...
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Photovoltaic (PV) systems, in conjunction with battery energy storage systems (BESS), have emerged as promising solutions for sustainable energy generation and consumption. However, the performance of these systems can be significantly impacted by partial shading conditions, which can lead to power losses and reduced efficiency. This research proposes a novel grey wolf optimization algorithm (GWO) to optimize the operation of PV-BESS systems under partial shading conditions. The GWO, inspired by the hunting behavior of grey wolves, is a robust optimization technique capable of handling complex and nonlinear problems. The proposed approach aims to maximize energy output, minimize power losses, and ensure optimal battery management. By effectively addressing the challenges posed by partial shading, this research contributes to the advancement of PV-BESS systems as reliable and efficient renewable energy solutions. The proposed system, consisting of a PV array, boost converter, MPPT controller, and battery, was evaluated using MATLAB/Simulink under various conditions. The results demonstrate that the NGWO algorithm achieves 99.89 % tracking efficiency under standard conditions and over 99.26 % under PSC, outperforming particle swarm optimization (PSO), genetic algorithm (GA), the conventional GWO, and Perturb & Observe (P&O) methods. Notably, NGWO exhibits faster response times (0.01 s) and reduced power ripples compared to other algorithms, enhancing both energy extraction and battery efficiency. By optimizing state of charge (SOC) control, the NGWO extends battery lifespan, offering a superior solution for PV systems in challenging environments.
The grey wolf optimization algorithm (GWO) is an efficient optimization technology. However, it still has some problems such as immature convergence and stagnation at local optima. In this paper, a strengthened grey w...
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The grey wolf optimization algorithm (GWO) is an efficient optimization technology. However, it still has some problems such as immature convergence and stagnation at local optima. In this paper, a strengthened grey wolf optimization algorithm (SGWO) is proposed based on three strengthening mechanisms: the exponential decreasing convergence factor, the elite reselection strategy in per generation and the Cauchy mutation (CM) operator. Seven variants of SGWO are designed according to different deployment modes of three reinforcement mechanisms. Experiments on thirteen numerical optimization problems are carried out to compare the differences between GWO and SGWOs. The experimental results reveal that SGWOs can significantly improve the search performance of GWO in most tasks. Among them, SGWO7 is the most successful competitor. Furthermore, several optimizers have demonstrated through comparison on engineering design problems that SGWO7 outperforms the vast majority of competitors. Subsequently, MHHO, TLBO, GWO and SGWO7 are used to build automatic machine learning (AutoML) model. The experimental results of the four methods on MNIST dataset further illustrate the advantages of SGWO7 designed in this research.
Facial expressions are an important part of recognizing human emotional messages, hence it has been a focus of pattern recognition research. However, developments in convolutional neural networks and network topologie...
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Moving sensor nodes can mitigate the coverage problem of random deployment in wireless sensor networks. However, the movement of nodes affects the lifetime and integrity of the network. Therefore, both energy saving a...
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Moving sensor nodes can mitigate the coverage problem of random deployment in wireless sensor networks. However, the movement of nodes affects the lifetime and integrity of the network. Therefore, both energy saving and efficient coverage are crucial factors. In this paper, we propose an energy-efficient coverage optimization technique with the help of the multi-Strategy greywolfoptimization (MSGWO) algorithm. This method can reduce energy consumption and improve coverage area by mixing higher-order multinomial sensing models and a sort-driven hybrid opposition-based learning. In addition, node movement and boundary strategies are proposed to help nodes jump out of obstacles when facing obstacle-aware deployments. The MSGWO is validated and compared on several classical test functions, and the results show that the MSGWO performs well. The MSGWO algorithm is applied to optimize the WSN coverage on different obstacle scenarios, the experimental results show that the algorithm helps to increase the network coverage from 84 % to 97.86 %, extends the network lifecycle by 50 %, reduces the cost of node deployment, and the network has good connectivity and scalability.
One of the most essential factors in the current study is effectively harvesting the Maximum Power Extraction (MPE) from the Photovoltaic (PV) panel. The primary difficulties in extracting solar power is occurrence of...
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One of the most essential factors in the current study is effectively harvesting the Maximum Power Extraction (MPE) from the Photovoltaic (PV) panel. The primary difficulties in extracting solar power is occurrence of partial shading which causes the panel to significantly increases power loss. These will mainly occur due to when partially shaded solar PV array kept under certain critical conditions for obtaining maximum output power. Many researcher have suggested by connecting bypass diodes in anti-parallel to the PV modules hotspots in the modules can be avoided. Out of all techniques, the proposed Bayesian Fusion Technique (BFT) is a hybrid optimizationalgorithm that combines the greywolfoptimization (GWO) and Flower Pollination algorithm (FPA) techniques to optimize the performance of solar panels in photovoltaic (PV) systems. The combination of GWO and FPA forms an ideal combination that is beneficial for optimizing the performance of PV systems is determined in this work. In this study real 6*6 PV array string and irregular PV array configuration such as central and parallel-series PV string combination of various partial shading pattern is compared and found to be effective for reducing the hotspots problems. The performance of these configuration under different shading patterns have been compared and analyzed with the different parameters like output power, conversion efficiency and tracking efficiency. This article state about the influence of partial darkening and the crucial point that reduce the sensitivity to shading heaviness. For better understanding for reader the MATLAB/Simulink software is used to validate the simulation result with real time data. Overall, this article states the BFT is an efficient and reliable approach to improve the efficiency of PV systems, by combining two optimization techniques like GWO and FPA hybrid algorithm. This article gives clear insight to the researchers for choosing BFTGWO algorithm in order to decrease the c
作者：
Zhang, LiChen, XiaoboJiangsu Univ Technol
Coll Comp Engn Changzhou 213001 Peoples R China Jilin Univ
Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China Peoples Bank China
Changzhou City Ctr Branch Changzhou 213001 Jiangsu Peoples R China
Feature selection is crucial in data preprocessing, especially in medical data analysis. Although the greywolfoptimization (GWO) algorithm has attracted attention because of its simplicity and efficiency, it is pron...
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Feature selection is crucial in data preprocessing, especially in medical data analysis. Although the greywolfoptimization (GWO) algorithm has attracted attention because of its simplicity and efficiency, it is prone to falling into the local optimum when searching fora globally optimal solution when dealing with complex feature selection problems, which restricts its application potential. To solve this problem, this paper proposes the Elite-driven greywolf Optimizer (EDGWO) algorithm. The EDGWO algorithm significantly improves the global search capability of Alpha, Beta, and Delta grey wolves by taking advantage of the social hierarchy of the greywolf population and designing three global exploration operators. The algorithm smoothly transitions from extensive exploration to intensive exploitation by dynamically adjusting the search parameters A. In addition, the introduced stochastic probabilistic search strategy allows omega grey wolves to make a flexible choice between local exploitation and global exploration, effectively avoiding premature convergence during the search process. To evaluate the performance of the EDGWO algorithm, this study compared twenty-two standard benchmark functions of CEC2021 and CEC2022 and twelve cancer microarray datasets. The experimental results show that the EDGWO algorithm demonstrates superior exploration and exploitation capabilities compared to fifteen well-known algorithms, with fast convergence speed and effective circumvention of local optima. Various evaluations have shown that EDGWO achieved the best Friedman rankings in the 10- and 20-dimensional CEC2021 and CEC2022 benchmark functions. In particular, the EDGWO algorithm maintains high convergence speed and high accuracy in feature selection for cancer microarray datasets.
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