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A vertical and horizontal crossover sine cosine algorithm with pattern search for optimal power flow in power systems

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ENERGY 2023年 271卷

作者： Weng, Xuemeng Xuan, Ping Heidari, Ali Asghar Cai, Zhennao Chen, Huiling Mansour, Romany F. Ragab, Mahmoud Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China Shantou Univ Sch Engn Dept Comp Sci Shantou 515063 Peoples R China New Valley Univ Fac Sci Dept Math El Kharga 72511 Egypt King Abdulaziz Univ Fac Comp & Informat Technol Informat Technol Dept Jeddah 21589 Saudi Arabia King Abdulaziz Univ Ctr Artificial Intelligence Precis Med Jeddah 21589 Saudi Arabia

Most of the energy consumption is now being used to supply human demand for electricity, which has increased the burden of power system planning to some extent, and thus, researchers proposed solutions to the optimal power flow (OPF) problem. In such a background, flexible AC transmission system (FACTS) devices are widely used in modern power systems to alleviate human power demand and improve network congestion problems. This research proposes a better-quality sine cosine algorithm (CPSCA) based on a crossover mechanism and pattern search algorithm for optimizing multiple objectives, such as system generation cost and transmission losses when we have multiple types of FACTS devices in the power system. The proposed method uses sine and cosine theory and evolutionary strategy to continuously sample and update the framework and improve the quality of the solutions. The method uses a class of vertical and horizontal crossover operators and pattern -shifting techniques to find the optimal solution set. In addition, the Weibull probability density function was used in this study to model uncertainty in wind energy, while direct costs, penalty costs, and standby costs were considered in constructing the target model. Various types of FACTS devices, such as thyristor-controlled series compensators, thyristor-controlled phase shifters, and static reactive power compensators, are also inserted into the model as power in the target optimization problem. The proposed algorithm has experimented on an IEEE30 bus test system. Based on the experimental results, CPSCA achieves an optimal total cost of 806.0225 $/h. In terms of power loss of the bus system, CPSCA reduces the total power loss value from 2.042 MW to 1.41 MW compared to the original sine cosine algorithm, which is a 31% improvement and has an optimal power loss value. Therefore, the simulation results demonstrate that the proposed algorithm is an effective technique for optimizing the optimal power flow of the entire

关键词： Optimal power flow FACTS devices sine cosine algorithm Horizontal crossover Vertical crossover Pattern search

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Hybrid sine cosine algorithm with Integrated Roulette Wheel Selection and Opposition-Based Learning for Engineering Optimization Problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 171-171页

作者： Pham, Vu Hong Son Dang, Nghiep Trinh Nguyen Nguyen, Van Nam Vietnam Natl Univ VNU HCM Ho Chi Minh City Univ Technol HCMUT Fac Civil Engn Ho Chi Minh City Vietnam

The sine cosine algorithm (SCA) is widely recognized for its efficacy in solving optimization problems, although it encounters challenges in striking a balance between exploration and exploitation. To improve these limitations, a novel model, termed the novel sine cosine algorithm (nSCA), is introduced. In this advanced model, the roulette wheel selection (RWS) mechanism and opposition-based learning (OBL) techniques are integrated to augment its global optimization capabilities. A meticulous evaluation of nSCA performance has been carried out in comparison with state-of-the-art optimization algorithms, including multi-verse optimizer (MVO), salp swarm algorithm (SSA), moth-flame optimization (MFO), grasshopper optimization algorithm (GOA), and whale optimization algorithm (WOA), in addition to the original SCA. This comparative analysis was conducted across a wide array of 23 classical test functions and 29 CEC2017 benchmark functions, thereby facilitating a comprehensive assessment. Further validation of nSCA utility has been achieved through its deployment in five distinct engineering optimization case studies. Its effectiveness and relevance in addressing real-world optimization issues have thus been emphasized. Across all conducted tests and practical applications, nSCA was found to outperform its competitors consistently, furnishing more effective solutions to both theoretical and applied optimization problems.

关键词： Evolutionary algorithm Stochastic optimization sine cosine algorithm Roulette wheel selection Opposition-based learning

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Triangular-based sine cosine algorithm for global search and feature selection

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SCIENTIFIC REPORTS 2025年第1期15卷 1-43页

作者： Liu, Jiacong Bi, Chunguang Chen, Huiling Heidari, Ali Asghar Chen, He Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran

The sine cosine algorithm (SCA) is a popular population-based optimization technique that utilizes sine and cosine functions to navigate complex search spaces. However, its inherent simplicity can hinder optimal exploration-exploitation balance, particularly in high-dimensional problems, leading to slow convergence and reduced precision. To address these issues, we propose a novel triangular optimization (TO) strategy combined with a theft mechanism (TM), resulting in an enhanced algorithm named TTOSCA. We rigorously evaluate TTOSCA against 27 competing algorithms using the IEEE CEC2017 benchmark functions and apply the Wilcoxon signed-rank test to assess performance. Our results indicate that TTOSCA significantly improves precision and convergence speed. Furthermore, we develop a binary variant, BTTOSCA, to validate its effectiveness in discrete spaces through feature selection experiments on 17 datasets from UCI, including medical and high-dimensional gene data. Comparative analysis shows that BTTOSCA excels in both exploration and exploitation, achieving smaller feature subsets without compromising classification accuracy. This positions BTTOSCA as a powerful tool for feature selection in high-dimensional datasets.

关键词： sine cosine algorithm Swarm intelligence Feature selection Global optimization

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Improved sine cosine algorithm for Optimization Problems Based on Self-Adaptive Weight and Social Strategy

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IEEE ACCESS 2023年 11卷 73053-73061页

作者： Chun, Ye Hua, Xu Jiangsu Vocat Coll Informat Technol Internet Things Engn Coll Wuxi 214001 Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214001 Peoples R China

The sine cosine algorithm (SCA) is a well-known optimization technique that utilizes sine and cosine functions to identify optimal solutions. Despite its popularity, the SCA has limitations in terms of low diversity, stagnation in local optima, and difficulty in achieving global optimization, particularly in complex large-scale problems. In response, we propose a novel approach named the Improved Weight and Strategy sine cosine algorithm (IWSCA). The IWSCA achieves this by introducing novel self-adaptive weight and social strategies that enable the algorithm to efficiently search for optimal solutions in complex large-scale problems. The performance of the IWSCA is evaluated with 23 benchmark test functions and the IEEE CEC 2019 benchmark suite, compare it to a state-of-the-art heuristic algorithm and two improved versions of the SCA. Our experimental results demonstrate that the IWSCA outperforms existing methods in terms of convergence precision and robustness.

关键词： sine cosine algorithm self-adaptive weight social strategy complex large-scale problems

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Combined Emission Economic Load Dispatch with Renewable Energy Sources Employing Hybrid Statistical Multiswarm Particle Swarm Optimizer-sine cosine algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第9期51卷 871页

作者： Keswani, Rinki Verma, Harish Kumar Sharma, Shailendra Kumar Shri GS Inst Technol & Sci Indore India

Due to the increase in environmental concerns, it becomes mandatory now to focus on energy management and the planning of resources. An integration of distributed generation resources with conventional sources is gaining popularity. The hybrid modeling of the power system poses a complex and challenging optimization problem without getting trapped in a local optimum. A novel Hybrid Statistical Multiswarm Particle Swarm Optimizer-sine cosine algorithm (HSMPSO-SCA) is developed in this paper, and its performance is evaluated on various test benchmark functions. The real operating conditions such as valve point loading, prohibited zones, ramp rate limits and uncertainties in solar and wind power generation for the IEEE 30 bus system are considered for combined emission economic load dispatch (CEELD). The performance of the HSMPSO-SCA is also analyzed for the solution of CEELD for various standard test systems with interaction of solar and wind plants. The efficacy of the proposed HSMPSO-SCA is found to be much better than various metaheuristics, hybrid and multi-agent algorithms in minimizing cost along with emissions optimally and achieves better accuracy and a faster convergence rate.

关键词： combined emission economic load dispatch statistical multi swarm particle swarm optimization sine cosine algorithm solar energy wind energy uncertainty

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An Improved Future Search algorithm Based on the sine cosine algorithm for Function Optimization Problems

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IEEE ACCESS 2023年 11卷 30171-30187页

作者： Fan, Yuqi Zhang, Sheng Yang, Huimin Xu, Di Wang, Yaping Harbin Univ Sci & Technol Sch Mech & Power Engn Key Lab Adv Mfg & Intelligent Technol Minist Educ Harbin 150080 Peoples R China

Future search algorithm imitates the person living life. If one person finds that his life is not good, he will try to change his living life, and he will imitate a more successful person. To overcome insufficient performances of the basic Future search algorithm, this paper proposed an improved Future search algorithm based on the sine cosine algorithm (FSASCA). The proposed algorithm uses sine cosine algorithm to loop-progressive find the best solution. The searching method of the sine cosine algorithm can make the feasible solution to be re-positioned around another feasible solutions, which can make the proposed algorithm have a strong exploitation ability. Four coefficient factors are added in the basic FSA, and new update methods are introduced in the searching phase. To verify the searching and optimization performances of the proposed algorithm in this paper, this paper also gives data calculation results, Wilcoxon rank sum test, iteration figures, box plot figures, and searching path figures. Experimental results showed that FSASCA has a better iteration speed, the convergence precision, the solving accuracy, the strong competitive, and the high balance.

关键词： Optimization Mathematical models Optimization methods Convergence Search problems Statistics Sociology Future search algorithm sine cosine algorithm optimization problem function optimization

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A Double Adaptive Random Spare Reinforced sine cosine algorithm

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2267-2289页

作者： Abdelazim G.Hussien Guoxi Liang Huiling Chen Haiping Lin Department of Computer and Information Science Linköing UniversityLinköing4189Sweden Faculty of Science Fayoum UniversityFayoum63514Egypt Department of Information Technology Wenzhou PolytechnicWenzhou325035China Artificial Intelligence Wenzhou UniversityWenzhou325035China College of Information Engineering Hangzhou Vocational&Technical CollegeHangzhou310018China

Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution,so more new techniques and methods are needed to solve such *** algorithms have received a lot of attention in recent years because of their efficient performance and simple *** cosine algorithm(SCA)is a recent Metaheuristic algorithm that is based on two trigonometric functions sine&***,like all other metaheuristic algorithms,SCA has a slow convergence and may fail in sub-optimal *** this study,an enhanced version of SCA named RDSCA is suggested that depends on two techniques:random spare/replacement and double adaptive *** first technique is employed in SCA to speed the convergence whereas the second method is used to enhance exploratory searching *** evaluate RDSCA,30 functions from CEC 2017 and 4 real-world engineering problems are ***,a nonparametric test called Wilcoxon signed-rank is carried out at 5%level to evaluate the significance of the obtained results between RDSCA and the other 5 variants of *** results show that RDSCA has competitive results with other metaheuristics algorithms.

关键词： sine cosine algorithm global optimization swarm intelligence meta-heuristic algorithms

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WSN Clustering Routing algorithm Combining sine cosine algorithm and Levy Mutation

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IEEE ACCESS 2023年 11卷 22654-22663页

作者： Guo, Xiaoling Ye, Yongfei Li, Ling Wu, Renjie Sun, Xinghua Hebei North Univ Sch Informat Sci & Engn Zhangjiakou 075000 Peoples R China

Directed against the disadvantages of relatively short life-cycle and unbalanced energy utilization among nodes in WSN, a clustering routing algorithm combining sine cosine algorithm and Levy mutation is developed. During the cluster head election stage, the amount of cluster heads is dynamically calculated according to the surviving nodes for keeping it at a reasonable value;taking full account of the current energy of nodes, only nodes with high energy can be candidate cluster heads;the fitness function is constructed according to intra-cluster distance, so that the distribution structure within the cluster are relatively uniform;the sine cosine algorithm with improved step size search factor is used for cluster head election, and Levy mutation is introduced to realize the variation of population. The group of individuals with the lowest fitness function value is used as final election scheme for current round. In the data transmission phase, for the sake of avoiding long-distance transmission, the relay node is designed to forward data. The proposed algorithm effectively extends network life-cycle and well equalizes the load of network nodes.

关键词： Clustering algorithms Routing Wireless sensor networks Relays Heuristic algorithms Data communication Voting Clustering algorithm energy balance life cycle Levy mutation sine cosine algorithm WSN

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MOAEOSCA: an enhanced multi-objective hybrid artificial ecosystem-based optimization with sine cosine algorithm for feature selection in botnet detection in IoT

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第9期82卷 13369-13399页

作者： Hosseini, Fatemeh Gharehchopogh, Farhad Soleimanian Masdari, Mohammad Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran

The number of Internet of Things (IoT) devices overgrows, and this technology dominates. The importance of IoT security and the growing need to devise intrusion detection systems (IDSs) to detect all types of attacks. The limited sources on the IoT. They have led researchers to explore and provide new and efficient solutions to create Botnet Detection in IoT systems. These systems use data features to detect network traffic status and thus detect malicious behavior. Also, data set features indicate the type of network traffic. Many features in the problem space and network behaviour unpredictability make IDSs the main challenge in establishing security in computer networks. Many unnecessary features have also made feature selection an essential aspect of attack detection systems. This paper developed a multi-objective MOAEOSCA algorithm hybridizing Artificial Ecosystem-based Optimization (AEO) algorithms and the sine cosine algorithm (SCA) for botnet detection in IoT. By accurately identifying the weaknesses of the MOAEOSCA algorithm, it has been tried to cover the weaknesses to a large extent and to reach a robust algorithm. We promoted the proposed algorithm using Bitwise operations, Disruption operator, and Opposition-based learning (OBL) mechanisms. Ten standard datasets in the UCI repository were examined to evaluate the proposed algorithm's performance in solving the feature selection problem to detect a botnet. Simulation findings indicated that the proposed algorithm had an acceptable accuracy in Botnet Detection in the IoT, outperforming other methods. According to the experiments carried out in this paper, the MOAEOSCA algorithm has shown that nine data sets out of ten data sets in the feature selection problem performed better than other optimization algorithms. But in all seven botnet data sets, performance has shown better than different optimization algorithms.

关键词： Multi-objective Artificial ecosystem-based optimization sine cosine algorithm Feature selection Botnet detection in IoT

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Statistical learning algorithms for dendritic neuron model artificial neural network based on sine cosine algorithm

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INFORMATION SCIENCES 2023年 629卷 398-412页

作者： Gul, Hasan Huseyin Egrioglu, Erol Bas, Eren Giresun Univ Fac Arts & Sci Dept Stat Forecast Res Lab TR-28200 Giresun Turkiye

Training of dendritic neuron model artificial neural networks is generally achieved by using nonlinear least square methods. The distribution of random error terms is ignored in training algorithms although error terms are random variables. Maximum likelihood estimators can be obtained for dendritic neuron model artificial neural networks by using some indefinite sym-metric probability distributions. In this study, statistical learning algorithms are proposed for dendritic neuron model artificial neural networks. Maximum likelihood estimators for dendritic neuron model artificial neural networks are obtained by using Normal, Cauchy, Logistic, Gumbel and Laplace distributions. The sine cosine algorithm is used for maximization of the likelihood function under error terms that have Normal, Cauchy, Logistic, Gumbel and Laplace distributions. The proposed learning algorithms are applied to Istanbul Stock Exchange time series data sets. At the end of the analysis of application results, the performance of the proposed method is statis-tically better than well-known deep and shallow artificial neural networks.

关键词： Dendritic neuron model Maximum likelihood estimators sine cosine algorithm Artificial neural networks Forecasting

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