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A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 43卷 1-30页

作者： Nenavath, Hathiram Jatoth, Ravi Kumar Das, Swagatam Natl Inst Technol Dept Elect & Commun Engn Warangal 506004 Andhra Pradesh India Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India

Due to its simplicity and efficiency, a recently proposed optimization algorithm, sine cosine algorithm (SCA), has gained the interest of researchers from various fields for solving optimization problems. However, it is prone to premature convergence at local minima as it lacks internal memory. To overcome this drawback, a novel Hybrid SCA-PSO algorithm for solving optimization problems and object tracking is proposed. The P-best and G(best) components of PSO (Particle Swarm Optimization) is added to traditional SCA to guide the search process for potential candidate solutions and PSO is then initialized with P-best of SCA to exploit the search space further. The proposed algorithm combines the exploitation capability of PSO and exploration capability of SCA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 23 classical, CEC 2005 and CEC 2014 benchmark functions. Statistical parameters are employed to observe the efficiency of the Hybrid SCA-PSO qualitatively and results prove that the proposed algorithm is very competitive compared to the state-of-the-art metaheuristic algorithms. The Hybrid SCA-PSO algorithm is applied for object tracking as a real thought-provoking case study. Experimental results show that the Hybrid SCA-PSO-based tracker can robustly track an arbitrary target in various challenging conditions. To reveal the capability of the proposed algorithm, comparative studies of tracking accuracy and speed of the Hybrid SCA-PSO based tracking framework and other trackers, viz., Particle filter, Mean-shift, Particle swarm optimization, Bat algorithm, sine cosine algorithm (SCA) and Hybrid Gravitational Search algorithm (HGSA) is presented.

关键词： Global optimization sine-cosine algorithm Particle swami optimization Object tracking Hybrid SCA-PSO

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A New Binary Variant of sine-cosine algorithm: Development and Application to Solve Profit-Based Unit Commitment Problem

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第8期43卷 4041-4056页

作者： Reddy, K. Srikanth Panwar, Lokesh Kumar Panigrahi, B. K. Kumar, Rajesh IIT Delhi Dept Elect Engn New Delhi India IIT Delhi Ctr Energy Studies New Delhi India MNIT Jaipur Dept Elect Engn Jaipur Rajasthan India

The profit-based unit commitment (PBUC) procedure is a binary natured operational planning problem of generation company (GENCO) in competitive electricity market. The GENCO solves PBUC problem in day ahead market of energy and reserve markets by commitment and scheduling of thermal units with an objective of profit maximization for the set of given price and load forecasts. Therefore, the solution quality and efficacy of PBUC optimization problem play a vital role in ensuring maximum returns to the GENCO. This paper presents a mathematical model-inspired heuristic approach called binary sine-cosine algorithm (BSCA) for solving PBUC problem. The SCA algorithm is a heuristic approach that uses the fluctuating nature of individual candidates/search agents in the search space around the global solution. The fluctuating mechanism is realized by using mathematical functions, i.e. sine and cosine functions. The proposed binary approach of SCA algorithm uses modified sigmoidal transformation function for binary mapping of continuous real-valued search space to binary counterpart. The efficacy of proposed approach in terms of solution quality and convergence is demonstrated using test system with different market participation policies. The results demonstrate the effectiveness of proposed BSCA approach over existing approaches for solving PBUC.

关键词： Profit-based unit commitment (PBUC) Heuristic optimization sine-cosine algorithm Binary sine-cosine optimization algorithm (BSCA) Power system operation Electricity market

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Maiden application of an sine-cosine algorithm optimised FO cascade controller in automatic generation control of multi-area thermal system incorporating dish-Stirling solar and geothermal power plants

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IET RENEWABLE POWER GENERATION 2018年第5期12卷 585-597页

作者： Tasnin, Washima Saikia, Lalit C. Natl Inst Technol Silchar Dept Elect Engn Silchar Assam India

The present study highlights an attempt of integrating the geothermal power plant (GTPP) in automatic generation control of an interconnected system comprising of dish-Stirling solar-thermal system (DSTS) and the conventional thermal system (TS). Generation rate constraints of 3%/min are considered for the TSs. A new fractional-order (FO) cascade controller named as FO proportional (P)-integral (I)-FOP-derivative (D) (FOPI-FOPD) is proposed as secondary controller and performance is compared with commonly used classical controllers. Controller gains and other parameters are optimised using a novel stochastic algorithm called sine-cosine algorithm. The analysis reveals the superiority of FOPI-FOPD over others. The effect of inclusion of GTPP and DSTS is also analysed on the conventional TS, both in a combined manner and separately. Sensitivity analysis reflects the robustness of optimum FOPI-FOPD controller gains and other parameters obtained at nominal and recommend that the optimised parameters do not suffer much deviations and are able to withstand wide fluctuations in system operating conditions, system loading and inertia constant. The dynamic behaviour of the system is studied with 1% step load perturbation in area1.

关键词： geothermal power stations solar power stations PI control power generation control cascade control stochastic processes sine-cosine algorithm FO cascade controller automatic generation control multiarea thermal system dish-Stirling solar power plants geothermal power plants GTPP interconnected system inertia constant system loading system operating conditions FOPI-FOPD controller gains stochastic algorithm secondary controller fractional-order cascade controller generation rate constraints DSTS dish-Stirling solar-thermal system

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An Improved Block-Matching algorithm Based on Chaotic sine-cosine algorithm for Motion Estimation 27th

An Improved Block-Matching Algorithm Based on Chaotic Sine-C...

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27th International Conference on Artificial Neural Networks (ICANN)

作者： Dash, Bodhisattva Rup, Suvendu IIIT Bhubaneswar Image & Video Proc Lab Bhubaneswar India

ISBN: (纸本)9783030014247

Motion estimation (ME) plays an important role in a video coding solution to achieve a low bit rate. The selection of the optimal motion vector (MV) has a significant impact on the quality of the compressed video. Block-matching (BM) algorithm is one of the widely accepted ME techniques to estimate the motion between the successive frames. In any BM technique, the motion vectors (MVs) are obtained for the current frame over a pre-defined search region in the previous frame by minimizing certain matching criterion. However, the computation of these matching criteria is highly expensive (in terms of the computational time). Hence, the block-based ME (BME) can be realized as an optimization problem which aims at finding the best-matched block within a specified search region. In this context, an improved block-matching technique is proposed that incorporates a chaotic-based sinecosine optimization algorithm along with a fitness approximation (FA) strategy. The proposed approach has been compared with several other BM techniques in terms of different parameters, namely, the peak-signal-to-noise-ratio (PSNR), PSNR degradation ratio (DPSNR), and the number of search points. The analysis of the results obtained demonstrates that the proposed method yields potential improvements over other competent schemes.

关键词： Block-matching Optimization Motion estimation sine-cosine algorithm Motion vector

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Small-Signal Stability Analysis of Hybrid Power System With Quasi-Oppositional sine cosine algorithm Optimized Fractional Order PID Controller

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IEEE ACCESS 2020年 8卷 155971-155986页

作者： Guha, Dipayan Roy, Provas Kumar Banerjee, Subrata Padmanaban, Sanjeevikumar Blaabjerg, Frede Chittathuru, Dhanamjayulu Motilal Nehru Natl Inst Technol Allahabad Elect Engn Dept Prayagraj 211004 India Kalyani Govt Engn Coll Elect Engn Dept Kalyani 741235 W Bengal India Natl Inst Technol Durgapur Elect Engn Dept Durgapur 713209 India Aalborg Univ Dept Energy Technol DK-6700 Esbjerg Denmark Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark Vellore Inst Technol VIT Sch Elect Engn Vellore 632014 Tamil Nadu India

This article deals with the frequency instability problem of a hybrid energy power system (HEPS) coordinated with reheat thermal power plant. A stochastic optimization method called a sine-cosine algorithm (SCA) is, initially, applied for optimum tuning of fractional-order proportional-integral-derivative (FOPI-D) controller gains to balance the power generation and load profile. To accelerate the convergence mobility and escape the solutions from the local optimal level, quasi-oppositional based learning (Q-OBL) is integrated with SCA, which results in QOSCA. In this work, the PID-controller's derivative term is placed in the feedback path to avoid the set-point kick problem. A comparative assessment of the energy-storing devices is shown for analyzing the performances of the same in HEPS. The qualitative and quantitative evaluation of the results shows the best performance with the proposed QOSCA: FOPI-D controller compared to SCA-, grey wolf optimizer (GWO), and hyper-spherical search (HSS) optimized FOPI-D controller. It is also seen from the results that the proposed QOSCA: FOPI-D controller has satisfactory disturbance rejection ability and shows robust performance against parametric uncertainties and random load perturbation. The efficacy of the designed controller is confirmed by considering generation rate constraint, governor dead-band, and boiler dynamics effects.

关键词： Frequency stabilization hybrid energy power system sine-cosine algorithm energy storage system quasi-oppositional based learning fractional order calculus

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Oil Consumption Forecasting Using Optimized Adaptive Neuro-Fuzzy Inference System Based on sine cosine algorithm

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IEEE ACCESS 2018年 6卷 68394-68402页

作者： Al-Qaness, Mohammed A. A. Abd Elaziz, Mohamed Ewees, Ahmed A. Wuhan Univ Sch Comp Sci Wuhan 430072 Hubei Peoples R China Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Damietta Univ Dept Comp Dumyat 34511 Egypt

Oil consumption is one of the main factors that affect industry and economy. Therefore, it is very important to estimate and forecast the consumption of oil. This helps the governments to take the right decisions and avoid the wrong decisions that lead to negative outcomes. For that reason, there are several methods that have been applied to forecast the oil consumption, such as the adaptive neuro-fuzzy inference system (ANFIS) model. It is one of the most popular data mining methods used to perform the forecast. However, the ANFIS model may not be accurate (biased) in all data, since its parameters require to be determined and updated and this may lead to stuck in the local point and not convergence to the optimal value. To this end, this paper presents an alternative oil consumption forecasting method by improving the ANFIS using the sine-cosine algorithm (SCA). In the proposed method called SCA-ANFIS, the parameters of the ANFIS are optimized using the SCA. In order to assess the performance of the proposed SCA-ANFIS method, a real dataset of petroleum products' consumption of three countries, namely, Canada, Germany, and Japan, is used. This dataset is collected on the period between 2007 and 2017, which contains 120 records per month for each country. Moreover, the results of the proposed method are compared with variants of ANFIS models. The experimental results demonstrate that the proposed SCA-ANFIS method outperforms other algorithms.

关键词： Time-series optimized ANFIS sine-cosine algorithm oil consumption forecasting

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Estimation of solar cell parameters through utilization of adaptive sine-cosine particle swarm optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2024年第15期36卷 8757-8773页

作者： Issa, Mohamed Helmi, Ahmed M. Ghetas, Mohamed Egypt Japan Univ Sci & Technol Fac Comp Sci & Informat Technol New Borg El Arab Egypt Buraydah Private Coll Coll Engn & Informat Technol Dept Comp Engn Buraydah 51418 Saudi Arabia Zagazig Univ Fac Engn Dept Comp & Syst Engn Zagazig 44519 Egypt Galala Univ Fac Comp Sci & Engn Suez Egypt

Due to the growing demand for clean and sustainable energy sources, there has been an increasing interest in solar cells and photovoltaic panels. Nevertheless, determining the right design parameters to achieve the most efficient energy output that aligns with the energy system's needs can be quite challenging. This complexity arises from the intricate models and the inherent inaccuracies in the available information. To tackle this challenge, this paper introduces the adaptive sine-cosine particle swarm optimization algorithm (ASCA-PSO) as a method for estimating the parameters of solar cells and photovoltaic modules. The ASCA-PSO approach combines the strengths of the SCA and PSO algorithms in a two-tier process. In this process, SCA search agents explore the search space, while the PSO search agents leverage the outcomes derived from SCA exploration. This study evaluates the effectiveness of ASCA-PSO in accurately estimating the parameters of single- and double-diode models using data from two commercial solar cells. The findings are compared with those of cutting-edge methods. It is demonstrated that ASCA-PSO can identify global solutions for multifaceted and intricate objective functions. Furthermore, it proves to be a viable option for designing solar cells even in the presence of noise.

关键词： Solar cells Photovoltaic modules Parameter estimation sine-cosine algorithm Particle swarm optimization

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A leaded sine-cosine artificial protozoa optimizer algorithm for solving multi-objective optimization problems

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第5期28卷 1-65页

作者： Liao, Junjie Sethumadhavan, Syam Melethil Ke, Rui Gao, Zheng-Ming Zhao, Juan China Three Gorges Univ Coll Comp & Informat Technol Yichang 443002 Peoples R China Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hyd Yichang 443002 Peoples R China Jingchu Univ Technol Big Data Res Ctr Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China

Many real-world engineering problems require simultaneous optimization of multiple objectives. The artificial protozoa optimizer (APO), a recently proposed and efficient optimization method, has demonstrated potential in solving multi-objective problems. In order to solve multi-objective problems, this paper proposes a novel algorithm based on the APO framework, the Leaded sine cosine Multi-objective APO (LSCMOAPO) algorithm. LSCMOAPO mainly integrates the sine-cosine algorithm (SCA) and a leader selection strategy to guide the population towards the true Pareto front. The algorithm was validated through extensive simulations involving 41 benchmark test functions (ZDT-series, UF-series, CF-series, and CEC2020-series) and five practical engineering problems. Performance was evaluated using the inverted generational distance, generational distance, Hypervolume, and spacing metrics. Comparative analysis with ten other multi-objective optimization algorithms (MOPSO, MOSSA, MOGWO, MODA, MOCryStAl, MOALO, MOGOA, MOSMA, MOGNDO, and MOAPO) also validated the better performance of LSCMOAPO in more than 86% occasions (The average of the evaluation indicators) in realizing high-quality solutions to all multi-objective problems, including linear, nonlinear, continuous, and discrete Pareto optimal front. It will set a new benchmark for new algorithm proposed.

关键词： Multi-objective problems (MOPs) Artificial protozoa optimizer (APO) sine-cosine algorithm Leader selection strategy Benchmark functions Engineering problems

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SCChOA:Hybrid sine-cosine Chimp Optimization algorithm for Feature Selection

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Computers, Materials & Continua 2023年第12期77卷 3057-3075页

作者： Shanshan Wang Quan Yuan Weiwei Tan Tengfei Yang Liang Zeng School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan430068China Xiangyang Industrial Institute of Hubei University of Technology Xiangyang441100China

Feature Selection(FS)is an important problem that involves selecting the most informative subset of features from a dataset to improve classification ***,due to the high dimensionality and complexity of the dataset,most optimization algorithms for feature selection suffer from a balance issue during the search ***,the present paper proposes a hybrid sine-cosine Chimp Optimization algorithm(SCChOA)to address the feature selection *** this approach,firstly,a multi-cycle iterative strategy is designed to better combine the sine-cosine algorithm(SCA)and the Chimp Optimization algorithm(ChOA),enabling a more effective search in the objective ***,an S-shaped transfer function is introduced to perform binary transformation on ***,the binary SCChOA is combined with the K-Nearest Neighbor(KNN)classifier to form a novel binary hybrid wrapper feature selection *** evaluate the performance of the proposed method,16 datasets from different dimensions of the UCI repository along with four evaluation metrics of average fitness value,average classification accuracy,average feature selection number,and average running time are ***,seven state-of-the-art metaheuristic algorithms for solving the feature selection problem are chosen for *** results demonstrate that the proposed method outperforms other compared algorithms in solving the feature selection *** is capable of maximizing the reduction in the number of selected features while maintaining a high classification ***,the results of statistical tests also confirm the significant effectiveness of this method.

关键词： Metaheuristics chimp optimization algorithm sine-cosine algorithm feature selection and classification

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Research on thermal error of electric spindle based on ISCA-GRNN

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第11-12期134卷 5805-5819页

作者： Dai, Ye Liu, Chenxu Min, Chuang Zhan, Shiqiang Li, Weiwei Chen, Xinda Harbin Univ Sci & Technol Key Lab Adv Mfg & Intelligent Technol Minist Educ Harbin 150080 Peoples R China Harbin Univ Sci & Technol Sch Mech & Power Engn Harbin 150080 Peoples R China

To meet the requirement for enhancing the machining precision of electric spindles in high-speed precision machine tools, we have optimized and designed a new type of electric spindle using a cage-type asynchronous AC motor. Thermal properties have been improved by optimizing the integration of heat sources and the cooling system to address heat accumulation in bearing components. Experimentation confirmed that, at speeds ranging from 6000 to 10,000 revolutions per minute (rpm), the new spindle exhibited a 19.61% decrease in axial thermal displacement and a 59.52% reduction in radial thermal displacement compared to conventional spindles. In order to further control thermal error accurately, a new type of thermal error prediction model for electric spindles was constructed by integrating the Improved sine cosine algorithm (ISCA) and Generalized Regression Neural Network (GRNN). Using the experimental data within the aforementioned speed range, the thermal errors of the new electrospindle were predicted. Compared with the unimproved SCA-GRNN prediction model, the ISCA-GRNN prediction model reduces the mean absolute error by 38.90% and the root mean square error by 27.47%, signifying a significant performance enhancement that verifies the validity of the proposed model.

关键词： High-speed precision machine tool Spindle Thermal displacement sine-cosine algorithm Generalized regression neural network

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