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Advanced Control of Three-Phase PWM Rectifier Using Interval Type-2 Fuzzy Neural Network Optimized by Modified golden sine algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第10期51卷 933页

作者： Acikgoz, Hakan Coteli, Resul Tanyildizi, Erkan Dandil, Besir Kayisli, Korhan Gaziantep Islam Sci & Technol Univ Dept Elect & Elect Engn Gaziantep Turkiye Firat Univ Dept Energy Syst Engn Elazig Turkiye Firat Univ Dept Software Engn Elazig Turkiye Mustafa Kemal Univ Fac Engn Hatay Turkiye Gazi Univ Dept Elect Elect Engn Ankara Turkiye

Three-phase Pulse-Width Modulated (PWM) rectifiers used between the power grid and the load in applications requiring DC voltage have features such as high efficiency, high power factor, and low harmonics. This paper proposes a hybrid control approach to improve the dynamic performance of three-phase PWM rectifiers under different operating conditions. Operating conditions are considered as step response, internal disturbance, and regenerative operation. First, Interval Type-2 Fuzzy Neural Network (IT2FNN) is designed and then antecedent and consequent parameters of IT2FNN are optimized with Modified golden sine algorithm (GoldSA-II). The dynamic performance of the hybrid controller, named GoldSA-II-IT2FNN, is analyzed for all operating conditions in Matlab/Simulink environment. The simulation studies are realized to evaluate the performance of the proposed controller. In the simulations, settling times of proposed controller are observed as 27.2 ms, and 10.8 ms for step response, respectively. Moreover, recovery times are calculated as being 12 ms to 5.5 ms for internal disturbance, and 7.2 ms to 19 ms for regenerative operation, respectively. The obtained results demonstrate that the proposed controller not only provides better dynamic performance but also improves the stability of PWM rectifier.

关键词： PWM rectifier interval type-2 fuzzy neural network golden sine algorithm voltage-oriented control power quality

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A novel optimization method for solving constrained and unconstrained problems: modified golden sine algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2018年第6期26卷 3287-3304页

作者： Tanyildizi, Erkan Firat Univ Fac Technol Dept Software Engn Elazig Turkey

Recently, the metaheuristic optimization algorithms inspired by nature and different science branches have been powerful solution methods for unconstrained, constrained, and engineering problems. Various metaheuristic optimization algorithms have been proposed and they have been applied to problems in different fields. This paper proposes a novel optimization method based on a modified version of the golden sine algorithm for solving unconstrained, constrained, and engineering problems. The basic idea behind the proposed modified golden sine algorithm (GoldSA-II) depends on finding the optimum solution field in search space by using the decreasing pattern of the sine function and the golden ratio. The performance of the proposed GoldSA-II is evaluated using 19 unconstrained benchmark functions, five constrained optimization test problems, and five real engineering design problems. The results of the proposed GoldSA-II are compared with best-known optimization algorithms using some well-known criteria. The obtained results show that the GoldSA-II converges more accurately to the global solution in many benchmark functions.

关键词： Benchmark testing golden sine algorithm metaheuristic algorithms optimization methods

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An Enhanced Opposition-Based golden-sine Whale Optimization algorithm 7th

An Enhanced Opposition-Based Golden-Sine Whale Optimization ...

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7th International Conference on Cognitive Computing (ICCC) Held as Part of the Services Conference Federation Meeting (SCF)

作者： Lu, Yong Yi, Chao Li, Jiayun Li, Wentao Minzu Univ China Sch Informat Engn Beijing Peoples R China

ISBN: (纸本)9783031516702;9783031516719

The Whale Optimization algorithm is an ingenious method conceived by researchers, drawing inspiration from the feeding behavior of humpback whales. Characterized by its simple structure, limited parameters, high efficiency, and robust optimization capacity, WOA has been extensively applied across multiple domains to address various challenges. Nonetheless, it has been found that the algorithm demonstrates low global exploration capability, inadequate search precision, and susceptibility to local optima entrapment. Many enhancements have been suggested in the literature, with Opposition-Based Learning emerging as a particularly effective technique for improving the quality of the initial population. In the present study, we put forth the Enhanced Opposition-Based strategy, which integrates supplementary constraints into the existing Opposition-Based Learning framework, generating a more refined initial population. Furthermore, we introduce the golden sine algorithm to modify the optimization approach of WOA, fostering an equilibrium between global exploration and exploitation abilities. In our evaluation, the proposed algorithm is assessed on nine classic benchmark functions with a dimensionality of 500, and compared with the original WOA, An enhanced whale optimization algorithm (eWOA), and the Elite Opposition-Based golden-sine Whale Optimization algorithm (Egolden-SWOA). The results exemplify the superior performance of our proposed algorithm, underscoring its potential application in the optimization of truss structure design problems, the results indicate that ESWOA outperforms other enhanced algorithms, such as eWOA and Egolden-SWOA, in terms of its performance in engineering optimization. This signifies that ESWOA can be effectively applied to engineering optimization problems.

关键词： Whale optimization algorithm Opposition-Based Learning golden sine algorithm engineering optimization

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Enhancing sand cat swarm optimization based on multi-strategy mixing for solving engineering optimization problems

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EVOLUTIONARY INTELLIGENCE 2025年第1期18卷 1-32页

作者： Wang, Wen-chuan Han, Zi-jun Zhang, Zhao Wang, Jun North China Univ Water Resources & Elect Power Coll Water Resources Zhengzhou 450046 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China

The Sand Cat Swarm Optimization (SCSO) algorithm is characterized by slow convergence speed and a tendency to become trapped in local optima. To address these issues, a hybrid multi-strategy variant known as HSCSO is proposed. The algorithm first employs an Elite Opposition-Based Learning strategy, which generates complementary candidate solutions, providing a more diverse set of initial solutions and improving the distribution of individual positions. Subsequently, a greedy strategy is utilized to combine the position update strategy of the golden sine algorithm, which has a fixed search pattern, with the position update strategy of the SCSO, which features random search characteristics. This combination retains the advantage of fast convergence while also providing a diverse set of random candidate solutions. Finally, a Random Inertia Weight is introduced to assist the SCSO algorithm in adaptively adjusting the search step size and optimization direction based on the different phases of the search process, thereby helping the algorithm effectively avoid local optima. The improved algorithm was compared with two classic original algorithms and six different algorithm variants on 25 benchmark functions. The experimental results indicate that HSCSO exhibits faster convergence and higher solution accuracy on most benchmark functions. Furthermore, the performance of HSCSO was evaluated on the CEC2019 and CEC2021 test suites, as well as several engineering optimization problems. The experimental results validate the competitiveness and superiority of the proposed HSCSO algorithm, demonstrating its potential for application in the field of global optimization.

关键词： Sand cat swarm optimization Elite opposition-based learning golden sine algorithm The greedy strategy Random inertia weight

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A local opposition-learning golden-sine grey wolf optimization algorithm for feature selection in data classification

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APPLIED SOFT COMPUTING 2023年 142卷

作者： Li, Zhang Jiangsu Univ Technol Sch Comp Engn Changzhou 213001 Jiangsu Peoples R China

The classification problem is an important research topic in machine learning and data mining. Feature selection can remove irrelevant and redundant features and improve classification accuracy. The traditional Grey Wolf algorithm (GWO) has the defects of low convergence efficiency and easy to falls into local extremes in solving the feature selection process, leading to ineffective removal of irrelevant and redundant features. This paper proposes a binary version of the local opposing learning golden sine grey wolf optimization algorithm (OGGWO). First, the OGGWO algorithm uses local opposing learning mapping to initialize the positions of individual grey wolves to enrich population diversity and improve convergence speed. Secondly, mix the golden sine algorithm and the grey wolf optimization algorithm to control the direction and distance of & alpha;wolves by using the golden mean coefficient to improve the autonomous search ability of individual grey wolves and avoid the algorithm from falling into the local optimum. Finally, the updated grey wolf position is binary converted by pre-setting the threshold value to reduce the feature subset's size and improve the classification effect. To verify the effectiveness of the OGGWO algorithm, 18 international standard datasets were selected, and compare the OGGWO algorithm with the improved Grey Wolf algorithm and the popular metaheuristic algorithm for the fitness value comparison test and the simulation comparison experiment for classification accuracy. The results show that: (1) the OGGWO algorithm has good convergence and high search accuracy on all 18 test data;(2) the improved strategy of the OGGWO algorithm can effectively improve the classification accuracy and reduce the number of selected features compared with the traditional GWO algorithm in the classification accuracy simulation. The experimental results show that the superiority and robustness of the OGGWO algorithm in feature selection are verified.& COPY;

关键词： Classification Feature selection Grey wolf optimization algorithm golden sine algorithm Local opposition-learning

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Scheduling of Multisensor for UAV Cluster Based on Harris Hawks Optimization With an Adaptive golden sine Search Mechanism

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IEEE SENSORS JOURNAL 2022年第10期22卷 9621-9635页

作者： Xie, Jiahao Huang, Shucai Wei, Daozhi Zhang, Zhaoyu Air Force Engn Univ Air Def & Antimissile Coll Xian 710051 Shaanxi Peoples R China

Multisensor scheduling, a multi-objective optimization problem, simultaneously aims to find the optimal matching between sensors and targets in a certain period, that is, to generate the optimal multisensor scheduling scheme. Several sensor scheduling literatures proposed multisensor scheduling schemes incorporating various algorithms, but the majority of them dealt with sensors to targets in two dimensions. First, the visual relationship between sensors and UAV cluster targets is investigated, and a three-dimensional correspondence between sensors, targets, and time is designed while dividing the scheduling time. Secondly, the detection advantage of sensors to UAV cluster, sensor load, and sensor handover numbers in the scheduling time interval are summed to create multisensor scheduling objective functions for UAV cluster. Furthermore, for the UAV cluster optimization problem, a hybrid optimization algorithm is proposed, which integrates the golden sine mechanism and adaptive weight factor in Harris hawks optimization algorithm. Finally, extensive experimental and statistical analyses show that the models and algorithms proposed in this paper outperform the existing ones. It has better convergence, does not need additional computational cost, and generates better multisensor scheduling schemes.

关键词： Sensors Clustering algorithms Optimization Scheduling Task analysis Processor scheduling Optimal scheduling Adaptive weight factor golden sine algorithm Harris Hawks optimization algorithm multi-objective optimization multisensor scheduling UAV cluster

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Hybrid golden jackal and golden sine optimizer for tuning PID controllers

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SCIENTIFIC REPORTS 2024年第1期14卷 1-23页

作者： Mou, Kailong Yang, Ming Zhang, Mengjian Wang, Deguang Guizhou Univ Sch Elect Engn Guiyang 550025 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

In the domain of control engineering, effectively tuning the parameters of proportional-integral-derivative (PID) controllers has persistently posed a challenge. This study proposes a hybrid algorithm (HGJGSO) that combines golden jackal optimization (GJO) and golden sine algorithm (Gold-SA) for tuning PID controllers. To accelerate the convergence of GJO, a nonlinear parameter adaptation strategy is incorporated. The improved GJO is combined with Gold-SA, capitalizing on the expedited convergence speed offered by the improved GJO, coupled with the global optimization and precise search capabilities of Gold-SA. HGJGSO maximizes the strengths of two algorithms, facilitating a comprehensive and balanced exploration and exploitation. The effectiveness of HGJGSO is assessed through tuning the PID controllers for three typical systems. The results indicate that HGJGSO surpasses the comparison tuning methods. To evaluate the applicability of HGJGSO, it is used to tune the cascade PID controllers for trajectory tracking in a quadrotor UAV. The results demonstrate the superiority of HGJGSO in addressing practical challenges.

关键词： PID parameter tuning golden jackal optimization golden sine algorithm Nonlinear parameter adaptation strategy Hybrid algorithm Quadrotor UAV trajectory tracking

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PREDICTION OF SILICON CONTENT IN HOT METAL BASED ON golden sine PARTICLE SWARM OPTIMIZATION AND RANDOM FOREST

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METALURGIJA 2022年第2期61卷 325-328页

作者： Hu, CH. Yang, K. Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan 114051 Peoples R China

Particle Swarm Optimization (PSO) algorithm quickly falls into local optimum, low precision. In this paper, add the golden sine operation to the particle position update. The results show that the improved PSO algorithm has better optimization ability. The main parameters affecting the silicon content in hot metal are selected. Then, calculate the correlation coefficient and significance level between parameters and silicon content in hot metal. Finally, the prediction model of silicon content in hot metal is established based on the Random Forest (RF) optimized by improved PSO. The results show that the hit rate is 87,17 %.

关键词： blast furnace hot metal silicon particle swarm optimization golden sine algorithm random forest

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China's carbon dioxide emission forecast based on improved marine predator algorithm and multi-kernel support vector regression

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第3期30卷 5730-5748页

作者： Qin, Xiwen Zhang, Siqi Dong, Xiaogang Zhan, Yichang Wang, Rui Xu, Dingxin Changchun Univ Technol Sch Math & Stat 2055 Yanan St Changchun 130012 Peoples R China Changchun Univ Technol Grad Sch 2055 Yanan St Changchun 130012 Peoples R China

Global warming has constituted a major global problem. Carbon dioxide emissions from the burning of fossil fuels are the main cause of global warming. Therefore, carbon dioxide emission forecasting has attracted widespread attention. Aiming at the problem of carbon dioxide emissions forecasting, this paper proposes a new hybrid forecasting model of carbon dioxide emissions, which combines the marine predator algorithm (MPA) and multi-kernel support vector regression. For further strengthening the prediction accuracy, a novel variant of MPA is proposed, called EGMPA, which introduces the elite opposition-based learning strategy and the golden sine algorithm into MPA. algorithm test results show that EGMPA can effectively improve the convergence speed and optimization accuracy. The carbon dioxide emission data of China from 1965 to 2020 are taken as the research objects. Root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed model. The proposed multi-kernel support vector regression model is used to forecast China's carbon dioxide emissions during the "14th Five-Year Plan" period. The results show that the proposed model has RMSE of 37.43 Mt, MAE of 30.63 Mt, and MAPE of 0.32%, which significantly improves the prediction accuracy and can accurately and effectively predict China's carbon dioxide emissions. During the "14th Five-Year Plan" period, China's carbon dioxide emissions will continue to show an increasing trend, but the growth rate will slow down significantly.

关键词： Carbon dioxide emissions Multi-kernel support vector regression Marine predator algorithm Elite opposition-based learning strategy golden sine algorithm

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An improved beetle antennae search algorithm and its application in coverage of wireless sensor networks

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SCIENTIFIC REPORTS 2024年第1期14卷 1-21页

作者： Yin, Biao Mo, Liping Min, Wei Li, Shan Yu, Cunwei Jishou Univ Coll Comp Sci & Engn Jishou 416000 Peoples R China Jishou Univ Sch Commun & Elect Engn Jishou 416000 Peoples R China Troop 75841 Changsha 410000 Peoples R China

This manuscript presents ABSAS-CS-GSA, an improved iteration of the beetle antennae search (BAS) algorithm, tailored to address the shortcoming of the BAS with low convergence accuracy and easily falling into local optima. The improvements are structured into three principal components: first, dynamic step-size adjustment predicated on initial population to bolster solution accuracy and expedite convergence;second, position update mechanism integrating golden sine algorithm to diversify search patterns and expedite convergence;and third, population disturbance based on vertical and horizontal cross strategy to avoid falling into local optima. Experimental results on 12 benchmark functions demonstrate ABSAS-CS-GSA's superiority over the standard BAS and its four variants, as well as over two classic swarm intelligence algorithms. The simulation results of the algorithm applied to coverage optimization of wireless sensor networks with 30 nodes and 50 nodes reveal a marked improvement in both optimal and average coverage metrics relative to seven alternative algorithms. The refined algorithm exhibits excellent performance and is well-suited for tackling the coverage issue within wireless sensor networks.

关键词： Beetle antennae search algorithm golden sine algorithm Vertical and horizontal cross strategy Optimization Wireless sensor network

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