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A novel Hybrid Harris hawk sine cosine optimization algorithm for reactive power optimization problem

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2024年第6期36卷 901-937页

作者： Jiao, Shangbin Wang, Chen Gao, Rui Li, Yuxing Zhang, Qing Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China Xian Univ Technol Shaanxi Key Lab Complex Syst Control & Intelligen Xian Peoples R China Baoji Univ Arts & Sci Sch Elect & Elect Engn Baoji Shaanxi Peoples R China

Reactive power optimisation can effectively reduce active power loss and improve voltage quality, which is a great significance for power system planning. When the reactive power optimisation problem is solved by Harris Hawk optimisation (HHO) algorithm, there are slow convergence and falling into local optima. This is caused by the multiple random parameters in HHO's exploration phase. To solve this problem, the Improved Logistic Chaotic mapping, sine and cosine algorithm (SCA), the dynamic adaptive inertia weights and greedy strategy are introduced;the aim is to speed up convergence, reduce blind spots and improve the search capability. The improved algorithm was tested on the classical 23 benchmark functions;Wilcoxon's signed-rank test and Friedman test were tested, the results show that the improved algorithm can obtain better performance. The improved algorithm is applied to the reactive power optimisation problem in distribution networks with distributed generators (DG). When the reactive power optimisation problem is solved by the improved algorithms HHO, WOA, CSO, CS and PSO, respectively, the improved algorithm can obtain the lowest active power loss. Compared with no optimisation, active power loss is reduced by 33.19%. Finally, the node voltage quality ensures the safe operation of the system.

关键词： Harris hawk optimisation improved logistic chaotic mapping sine and cosine algorithm dynamic adaptive inertia weights reactive power optimisation problem

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A hybrid Aquila Optimizer sine cosine algorithm for Numerical Optimization 23

A hybrid Aquila Optimizer sine cosine Algorithm for Numerica...

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2nd Asia Conference on algorithms, Computing and Machine Learning (CACML)

作者： Chu, Fei Wang, Jiayang Tian, Fulin School of Computer Science and Engineering Central South University China

ISBN: (纸本)9781450399449

To address the shortcomings of the Aquila optimizer algorithm (AO), this paper proposes a novel hybrid Aquila Optimizer sine cosine algorithm(AO-SCA). Firstly, Singer chaotic mapping is used for initialization, so that the initial solution position distribution was more homogeneous, and increased the richness of the population. Secondly, in the exploration phase of AO, the concept of sine and cosine algorithm is integrated and the nonlinear sine learning factor is introduced to balance the local and global digging ability and accelerate the convergence speed. Finally, through the numerical experiment simulation of 8 benchmark functions, the results show that the optimization ability and convergence speed of the proposed algorithm is better.

关键词： Aquila optimizer Singer mapping sine and cosine algorithm swarm intelligence

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Feature Selection in Machine Learning by Hybrid sine cosine Metaheuristics 5th

Feature Selection in Machine Learning by Hybrid Sine Cosine ...

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5th International Conference on Advances in Computing and Data Sciences (ICACDS)

作者： Bacanin, Nebojsa Petrovic, Aleksandar Zivkovic, Miodrag Bezdan, Timea Antonijevic, Milos Singidunum Univ Danijelova 32 Belgrade 11000 Serbia

ISBN: (纸本)9783030814625;9783030814618

Feature selection problem from the domain of machine learning refers to selecting only those features from the high dimensional datasets, that have prominent influence on dependent variable(s). In this way, dataset dimensionallity is reduced and only the riches data is kept, training process of machine learning model becomes more efficient and accuracy is increased. This manuscript proposes a new hybridized version of the sine cosine algorithm adjusting for solving feature selection problem. Hybridization is relatively novel approach for combing and improving metaheuristics optimizer. Notwithstanding that the basic sine cosine algorithm establishes good performance for solving NP hard challenges, based on simulation results, it was concluded that there is still space for improvement in its exploitation process. Original sine cosine algorithm and proposed hybridized implementation were tested on a well-known 21 machine learning datasets retrieved from the UCL repository. Comparative analysis between hybrid sine cosine and original one, as well as with 10 other state-of-the-art metaheuristics was conducted. Established results in terms of classification accuracy and fitness prove the robustness and efficiency of proposed method for solving this type of NP hard challenge.

关键词： Machine learning Swarm intelligence NP hardness Feature selection sine and cosine algorithm Optimization

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A Large-Scale Scheduling Method for Multiple Agile Optical Satellites

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Computer Modeling in Engineering & Sciences 2023年第8期136卷 1143-1163页

作者： Zheng Liu Wei Xiong Minghui Xiong Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering UniversityBeijing101416China

This study investigates the scheduling problem ofmultiple agile optical satelliteswith large-scale *** problem is difficult to solve owing to the time-dependent characteristic of agile optical satellites,complex constraints,and considerable solution *** solve the problem,we propose a scheduling method based on an improved sine and cosine algorithm and a task merging *** first establish a scheduling model with task merging constraints and observation action constraints to describe the ***,an improved sine and cosine algorithm is proposed to search for the optimal solution with the maximum profit *** adaptive cosine factor and an adaptive greedy factor are adopted to improve the ***,a taskmerging method with a task reallocation mechanism is developed to improve the scheduling *** results demonstrate the superiority of the proposed algorithm over the comparison algorithms.

关键词： Multiple agile optical satellites scheduling task merging sine and cosine algorithm task reallocation

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Color constancy computation for dyed fabrics via improved marine predators algorithm optimized random vector functional-link network

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COLOR RESEARCH AND APPLICATION 2021年第5期46卷 1066-1078页

作者： Liu, Xiangqi Yang, Donghe Hangzhou Dianzi Univ Coll Mech Engn Informat Engn Sch Hangzhou 311305 Peoples R China Hangzhou Dianzi Univ Sch Mech Engn Hangzhou Peoples R China Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou Peoples R China

Aiming at the color characteristics of dyed fabrics that are easily affected by changes in light, which affects the correctness of the color difference classification of dyed fabrics, this article proposes the color constancy calculation of dyed fabrics based on improved marine predators algorithm optimized random vector functional-link. First, in order to obtain the excellent initial population of the marine predators algorithm, this article uses two update strategies of the sine and cosine algorithm to screen the randomly initialized population of the marine predators algorithm. Then, the MPA algorithm that initializes the population using the sine and cosine algorithm optimizes the input weights and hidden layer bias parameters of random vector functional-link, thereby improving the prediction accuracy of random vector functional-link. Finally, using the image features extracted by the Gray-Edge framework, the sine and cosine algorithm-marine predators algorithm-random vector functional-link model proposed in this article is used to calculate the color constancy of dyed fabrics, to eliminate the influence of illumination changes on the color difference classification of dyed fabrics. Compared with the other eight algorithms, the dyed fabric image restored by the sine and cosine -marine predators -random vector functional-link algorithm proposed in this article is closest to the image under standard illumination, that is, the color constancy evaluation effect of the dyed fabric is the best.

关键词： color constancy computation marine predators algorithm random vector functional‐ link network sine and cosine algorithm

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Hybrid Multi-Strategy Improved Butterfly Optimization algorithm

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APPLIED SCIENCES-BASEL 2024年第24期14卷 11547页

作者： Cao, Panpan Huang, Qingjiu Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 310018 Peoples R China Kogakuin Univ Grad Sch Engn Control Syst Lab Tokyo 1638677 Japan

To address the issues of poor population diversity, low accuracy, and susceptibility to local optima in the Butterfly Optimization algorithm (BOA), an Improved Butterfly Optimization algorithm with multiple strategies (IBOA) is proposed. The algorithm employs SPM mapping and reverse learning methods to initialize the population, enhancing its diversity;utilizes L & eacute;vy flight and trigonometric search strategies to update individual positions during global and local search phases, respectively, expanding the search scope of the algorithm and preventing it from falling into local optima;and finally, it introduces a simulated annealing mechanism to accept worse solutions with a certain probability, enriching the diversity of solutions during the optimization process. Simulation experimental results comparing the IBOA with Particle Swarm Optimization, BOA, and three other improved BOA algorithms on ten benchmark functions demonstrate that the IBOA has improved convergence speed and search accuracy.

关键词： butterfly optimization algorithm SPM mapping reverse learning L & eacute vy flight sine and cosine algorithm simulated annealing algorithm

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Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2021年第5期17卷

作者： Yue, Yinggao You, Hairong Wang, Shuxin Cao, Li Wenzhou Univ Oujiang Coll Wenzhou 325035 Zhejiang Peoples R China Hubei Univ Arts & Sci Comp Sch Xiangyang Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang Peoples R China

Aiming at the problems of node redundancy and network cost increase in heterogeneous wireless sensor networks, this article proposes an improved whale optimization algorithm coverage optimization method. First, establish a mathematical model that balances node utilization, coverage, and energy consumption. Second, use the sine-cosine algorithm to improve the whale optimization algorithm and change the convergence factor of the original algorithm. The linear decrease is changed to the nonlinear decrease of the cosine form, which balances the global search and local search capabilities, and adds the inertial weight of the synchronous cosine form to improve the optimization accuracy and speed up the search speed. The improved whale optimization algorithm solves the heterogeneous wireless sensor network coverage optimization model and obtains the optimal coverage scheme. Simulation experiments show that the proposed method can effectively improve the network coverage effect, as well as the utilization rate of nodes, and reduce network cost consumption.

关键词： Heterogeneous wireless sensor networks whale optimization algorithm coverage optimization sine and cosine algorithm energy balance

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A quasi-reflection based SC-PSO for ship path planning with grounding avoidance

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OCEAN ENGINEERING 2022年 247卷 110772-110772页

作者： Xue, Han Jimei Univ Coll Nav Xiamen 361021 Fujian Peoples R China

To improve the performance of particle swarm optimization (PSO), PSO is further improved by being combined with the sine cosine algorithm (SCA), which maintains a balance in exploration and exploitation. Furthermore, to speed up searching for the optimal path with grounding avoidance, a quasi-reflection-based sine cosine particle swarm optimization (SC-PSO) is designed. Its convergence is then proven. In the early stage, it can roam around the whole space and enhance the global search ability. In the later stage of the optimization process, the convergence ability of global optimization is strengthened. The quasi-reflection operation is introduced into the algorithm to improve the diversity of solutions. A new fitness function is developed to transform the path planning into an optimization problem, combined with the optimization standards and constraints of path length, grounding threat, collision avoidance with water targets and path smoothness, so as to realize the safe and efficient operation of ships. For grounding avoidance, the distance of the closest point of pass (DCPP), the distance of close-quarters situation of ground (DCQG), the time of close-quarters situation of ground (TCQG) and the distance from the ship to the shallows (DIS) are considered. To verify the shallow and collision avoidance of the actual ship operation, the terrain fluctuation data with the highest resolution built from global and regional data sets is used. EETOPO1 is a 1 arc-minute global relief model of Earth's surface that integrates land topography and ocean bathymetry. 3D seabed terrain and 3D collision avoidance are used for grounding avoidance in ship track planning. The results show that, compared with SCA and PSO, the new algorithm has the fastest convergence speed and the highest calculation accuracy.

关键词： Path planning Particle swarm optimization sine and cosine algorithm Ship grounding

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Optimal Integration of D-STATCOMs in Radial and Meshed Distribution Networks Using a MATLAB-GAMS Interface

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algorithmS 2023年第3期16卷 138-138页

作者： Barreto-Parra, German Francisco Cortes-Caicedo, Brandon Montoya, Oscar Danilo Univ Distrital Francisco Jose De Caldas Fac Ingn Grp Compatibil & Interferencia Electromagnet GCEM Bogota 110231 Colombia Inst Tecnol Metropolitano Fac Ingn Dept Mecatron & Electromecan Medellin 050036 Colombia

This paper proposes an interconnection of the MATLAB and GAMS software interfaces, which were designed based on a master-slave methodology, to solve the mixed-integer nonlinear programming (MINLP) model problem associated with the problem regarding the optimal location and sizing of static distribution compensators (D-STATCOMs) in meshed and radial distribution networks, considering the problem of optimal reactive power flow compensation and the fact that the networks have commercial, industrial, and residential loads for a daily operation scenario. The objective of this study is to reduce the annual investment and operating costs associated with energy losses and the installation costs of D-STATCOMs. This objective function is based on the classical energy budget and the capacity constraints of the device. In the master stage, MATLAB software is used to program a discrete version of the sine-cosine algorithm (DSCA), which determines the locations where the D-STATCOMs will be installed. In the slave stage, using the BONMIN solver of the GAMS software and the known locations of the D-STATCOMs, the MINLP model representing the problem under study is solved to find the value of the objective function and the nominal power of the D-STATCOMs. To validate the effectiveness of the proposed master-slave optimizer, the 33-node IEEE test system with both radial and meshed topologies is used. With this test system, numerical comparisons were made with the exact solution of the MINLP model, using different solvers in the GAMS software, the genetic-convex strategy, and the discrete-continuous versions of the Chu and Beasley genetic algorithm and the salp swarm optimization algorithm. The numerical results show that DSCA-BONMIN achieves a global solution to the problem under study, making the proposed method an effective tool for decision-making in distribution companies.

关键词： GAMS computational tool MATLAB computational tool distribution networks interface interconnection distribution static compensators sine and cosine algorithm radial and meshed topology minimizing annual operating costs

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Optimizing Energy Consumption of Rotor UAV by Path Planning

Optimizing Energy Consumption of Rotor UAV by Path Planning

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作者： Shilei Zhang Taining Cheng School of Software Yunnan University

Rotor Unmanned Aerial Vehicles(UAVs) have been widely used in daily life due to their portability, flexibility, and low cost. However, the limited endurance of rotor UAV becomes the bottleneck in various applications. Optimization of UAV energy consumption by path planning is the primary energy consumption optimization. The energy consumption of rotor UAVs depends not only on the flight distance but also on the speed and the number of turns they take. In this paper, we formulate the rotor UAV path planning as a traveling salesman problem to optimize rotor UAV energy consumption. We propose an improved sine and cosine algorithm based on opposition-based learning(SCAO) to solve the optimization problem.

关键词： rotor unmanned aerial vehicle energy consumption optimization path planning sine and cosine algorithm

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