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Multiple hydropower reservoirs operation optimization by adaptive mutation sine cosine algorithm based on neighborhood search and simplex search strategies

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JOURNAL OF HYDROLOGY 2020年 590卷

作者： Feng, Zhong-kai Niu, Wen-jing Liu, Shuai Luo, Bin Miao, Shu-min Liu, Kang Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China ChangJiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China Tsinghua Sichuan Energy Internet Res Inst Chengdu 610200 Peoples R China State Grid Sichuan Elect Power Res Inst Chengdu 610072 Peoples R China Changjiang Schinta Software Technol Ltd Liabil Co Wuhan 430010 Peoples R China

Recently, the multiple hydropower reservoirs operation optimization is attracting rising concerns from researchers and engineers since it can not only improve the utilization efficiency of water resource but also increase the generation benefit of hydropower enterprises. Mathematically, the reservoir operation problem is a typical multistage constrained optimization problem coupled with numerous decision variables and physical constraints. sine cosine algorithm (SCA), a new swarm-based method, has the merits of clear principle and easy implementation but suffers from the premature convergence and falling into local optima. To improve the SCA performance, this paper proposes an adaptive sine cosine algorithm (ASCA) where the elite mutation strategy is used to increase the population diversity, the simplex dynamic search strategy is used to enhance the solution quality, while the neighborhood search strategy is used to improve the convergence rate. The simulations of 25 test functions show that ASCA outperforms several existing methods in both convergence rate and solution quality. The results of a real-world hydropower system in China demonstrate that ASCA betters the SCA method with obvious increase in power generation. Thus, the main contribution of this study is to provide an effective optimizer for multiple hydropower reservoirs operation.

关键词： Reservoir operation sine cosine algorithm Elite mutation strategy Simplex dynamic search strategy Neighborhood search strategy

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A Modified sine cosine algorithm for Solving Optimization Problems

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IEEE ACCESS 2021年 9卷 27434-27450页

作者： Wang, Meng Lu, Guizhen Commun Univ China Sch Informat & Commun Engn Beijing 100024 Peoples R China

The sine cosine algorithm (SCA) is a newly emerging optimization algorithm. It is easy for sine cosine algorithm (SCA) to sink into premature of the algorithm and obtain a slower convergence rate when solving the complicated optimization problems, especially highly ill-posed problems. A novel modified sine cosine algorithm (MSCA) is put forward for solving the optimization problems. To our limited knowledge, the linear searching path and empirical parameter have not been applied to the related improved sine cosine algorithm. The proposed MSCA improves the search path of original SCA by introducing linear searching path and empirical parameter, effectively avoiding sinking into the local optimal. In addition, the proposed algorithm changes the definition of convergence factor. Two kinds of tests, including 23 benchmark functions test and actual engineering problem tests are adopted to prove the performance of the MSCA. In addition, the performance of the proposed MSCA is compared with SCA by using benchmark functions on different dimensional (D = 30, 50,100 and 500). As expected, the result of comparisons show that the proposed MSCA can better avoid the local optima than both the SCA and other population-based algorithms. And MSCA can obtain the faster convergence than SCA on different dimensions.

关键词： Optimization Birds Genetic algorithms Convergence Benchmark testing Stochastic processes Search problems sine cosine algorithm linear searching path nature-inspired algorithm optimization

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A quantum-based sine cosine algorithm for solving general systems of nonlinear equations

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ARTIFICIAL INTELLIGENCE REVIEW 2021年第5期54卷 3939-3990页

作者： Rizk-Allah, Rizk M. Menoufia Univ Fac Engn Shibin Al Kawm Egypt

In this paper, a quantum-based sine cosine algorithm, named as Q-SCA, is proposed for solving general systems of nonlinear equations. The Q-SCA hybridizes the sine cosine algorithm (SCA) with quantum local search (QLS) for enhancing the diversity of solutions and preventing local optima entrapment. The essence of the proposed Q-SCA is to speed up the optimum searching operation and to accelerate the convergence characteristic. The proposed Q-SCA works in twofold: firstly, an improved version of SCA based on tuning the search space around the destination solution dynamically, so that the search space is shrunken gradually as the optima are attained. In addition, a new mechanism to update the solutions is introduced using bidirectional equations. Secondly, QLS is incorporated to improve the quality of the obtained solutions by the SCA phase. By this methodology, the proposed Q-SCA can achieve high levels of exploration/exploitation and precise stable convergence to high-quality solutions. The performance of the proposed algorithm is assessed by adopting twelve systems of nonlinear equations and two electrical applications. Furthermore, the proposed Q-SCA algorithm is applied on expensive large-scale problems including CEC 2017 benchmark and realistic optimal power dispatch (OPD) to confirm its scalability. Experimental results affirm that the Q-SCA is performs steadily, and it has a promising overall performance among several compared algorithms.

关键词： sine cosine algorithm Quantum strategy Systems of nonlinear equations Power system applications

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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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Robust 2D and 3D images zero - watermarking using dual Hahn moment invariants and sine cosine algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第18期81卷 25581-25611页

作者： Daoui, Achraf Karmouni, Hicham Sayyouri, Mhamed Qjidaa, Hassan Sidi Mohamed Ben Abdellah Fez Univ Natl Sch Appl Sci Lab Engn Syst & Applicat Fes Morocco Sidi Mohamed Ben Abdellah Fez Univ Fac Sci Lab Elect Signals & Syst Informat Fes Morocco

In this paper, we initially provide significant improvements on the computational aspects of dual Hahn moment invariants (DHMIs) in both 2D and 3D domains. These improvements ensure the numerical stability of DHMIs for large-size images. Then, we propose an efficient method for optimizing the local parameters of dual Hahn polynomials (DHPs) when computing DHMIs using the sine-cosine algorithm (SCA). DHMIs optimized via SCA are used to propose new and robust zero-watermarking scheme applied to both 2D and 3D images. On one hand, the simulation results confirm the efficiency of the proposed computation of 2D and 3D DHMIs regarding the numerical stability and invariability. Indeed, the proposed computation method of 2D DHMIs allows to reach a relative error (RE) of the order approximate to 10(-10) for images of size 1024 x 1024 with an execution time improvement ratio exceeds 70% (ETIR >= 70%), which validates the efficiently of the proposed computation method. On the other hand, the simulation and comparison outcomes clearly demonstrate the robustness of the proposed zero-watermarking scheme against various geometric attacks (translation, rotation, scaling and combined transformations), as well as against other common 2D and 3D image processing attacks (compression, filtering, noise addition, etc.).

关键词： Image reconstruction 2D and 3D image zero-watermarking Discrete orthogonal moments Dual - Hahn moment invariants sine cosine algorithm Numerical stability

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MTV-SCA: multi-trial vector-based sine cosine algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第10期27卷 13471-13515页

作者： Nadimi-Shahraki, Mohammad H. Taghian, Shokooh Javaheri, Danial Sadiq, Ali Safaa Khodadadi, Nima Mirjalili, Seyedali Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad *** Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad *** Iran Torrens Univ Ctr Artificial Intelligence Res & Optimisat Brisbane 4006 Australia Korea Univ Dept Comp Sci & Engn Seoul 02841 South Korea Nottingham Trent Univ Dept Comp Sci Clifton Lane Nottingham NG11 8NS England Univ Miami Dept Civil & Architectural Engn Coral Gables FL 33146 USA

The sine cosine algorithm (SCA) is a metaheuristic algorithm that employs the characteristics of sine and cosine trigonometric functions. SCA's deficiencies include a tendency to get trapped in local optima, exploration-exploitation imbalance, and poor accuracy, which limit its effectiveness in solving complex optimization problems. To address these limitations, a multi-trial vector-based sine cosine algorithm (MTV-SCA) is proposed in this study. In MTV-SCA, a sufficient number of search strategies incorporating three control parameters are adapted through a multi-trial vector (MTV) approach to achieve specific objectives during the search process. The major contribution of this study is employing four distinct search strategies, each adapted to preserve the equilibrium between exploration and exploitation and avoid premature convergence during optimization. The strategies utilize different sinusoidal and cosinusoidal parameters to improve the algorithm's performance. The effectiveness of MTV-SCA was evaluated using benchmark functions of CEC 2018 and compared to state-of-the-art, well-established, CEC 2017 winner algorithms and recent optimization algorithms. The results demonstrate that the MTV-SCA outperforms the traditional SCA and other optimization algorithms in terms of convergence speed, accuracy, and the capability to avoid premature convergence. Moreover, the Friedman and Wilcoxon signed-rank tests were employed to statistically analyze the experimental results, validating that the MTV-SCA significantly surpasses other comparative algorithms. The real-world applicability of this algorithm is also demonstrated by optimizing six non-convex constrained optimization problems in engineering design. The experimental results indicate that MTV-SCA can effectively handle complex optimization challenges.

关键词： Engineering optimization problems Metaheuristic algorithms Numerical optimization Optimization algorithms sine cosine algorithm

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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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A modified sine cosine algorithm for accurate global optimization of numerical functions and multiple hydropower reservoirs operation

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KNOWLEDGE-BASED SYSTEMS 2020年 208卷

作者： Feng, Zhong-kai Liu, Shuai Niu, Wen-jing Li, Bao-jian Wang, Wen-chuan Luo, Bin Miao, Shu-min Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China ChangJiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China North China Univ Water Resources & Elect Power Sch Water Conservancy Zhengzhou 450046 Peoples R China Tsinghua Sichuan Energy Internet Res Inst Chengdu 610200 Peoples R China State Grid Sichuan Elect Power Res Inst Chengdu 610072 Peoples R China

sine cosine algorithm (SCA) is an emerging meta-heuristic method for the complicated global optimization problems, but still suffers from the premature convergence problem due to the loss of swarm diversity. To improve the SCA performance, this paper develops a modified sine cosine algorithm coupled with three improvement strategies, where the quasi-opposition learning strategy is used to balance global exploration and local exploitation;the random weighting agent produced by multiple leader solutions is integrated into the agent's evolution equation to improve the convergence rate;the adaptive mutation strategy is designed to increase the swarm diversity. The proposed method is compared with several famous evolutionary methods on 12 classical test functions, 24 CEC2005 composite functions and 30 CEC2017 benchmark functions. The results show that the proposed method outperforms several control methods in both solution quality and convergence rate. Then, the long-term operation optimization of multiple hydropower reservoirs in China is chosen to testify the engineering practicality of the developed method. The simulation results indicate that in different scenarios, the proposed method can produce satisfying scheduling schemes with better objective values compared with several existing evolutionary methods. Hence, a novel optimizer is provided to handle the complicated engineering optimization problem. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Engineering optimization Hydropower system operation optimization sine cosine algorithm Elite-guide evolutionary strategy Quasi-opposition learning Adaptive mutation Numerical optimization

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A new interactive sine cosine algorithm for loading margin stability improvement under contingency

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ELECTRICAL ENGINEERING 2018年第2期100卷 913-933页

作者： Mahdad, Belkacem Srairi, K. Univ Biskra Dept Elect Engn Biskra Algeria

In this paper, a new method called sine cosine algorithm is adapted in coordination with an interactive process to improve the power system security considering loading margin stability and faults at specified important branches. In this study, the loading margin stability is optimized in coordination with total cost, total power loss, total voltage deviation and voltage stability index. In order to locate the best loading margin stability, an initial global database containing suboptimized control variables is generated based on two indices named global and local critical reactive margin security related to generating units. The optimized loading margin stability is improved in coordination with the availability of reactive power of different shunt FACTS devices installed at particular locations. The robustness of the proposed planning strategy is validated on a small test system, the IEEE 30-Bus and to a large test system, the IEEE 118-Bus. Optimized results found confirmed clearly the improvement of loading margin stability at critical situations such as faults at specified branches.

关键词： Loading margin stability Security OPF Contingency Metaheuristic methods sine cosine algorithm Shunt FACTS Critical reactive margin stability

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