Optimization of complex engineering problems using modified sine cosine algorithm

Chen, Shang; Zhou, Tingting; Liu, Shuai

doi:10.1038/s41598-022-24840-z

Cited by 11 publications

(7 citation statements)

References 71 publications

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“…By drawing inspiration from the oscillatory behavior of sine and cosine functions, SCO strikes a balance between exploration and exploitation, enabling efficient search and convergence. With its simplicity, versatility, and competitive performance, SCO has emerged as a valuable optimization tool for researchers and practitioners alike [ 29 , 30 ].…”

Section: Sine Cosine Algorithmmentioning

confidence: 99%

“…This algorithm is widely used in various fields [ 28 ]. It has been successfully applied in Optimal power flow in electrical power systems [ 29 ], Parameter tuning in machine learning (ELDPs) [ 18 ], Routing optimization in wireless sensor networks [ 30 ], Energy-efficient scheduling in cloud computing [ 31 ] and Optimization of machining parameters. In the field of electrical DC motors.…”

Section: Introductionmentioning

confidence: 99%

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Sine cosine optimization algorithm combined with balloon effect for adaptive position control of a cart forced by an armature-controlled DC motor

Mohamed,

Alkhalaf,

Senjyu

et al. 2024

PLoS ONE

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For a car that is propelled by an armature-controlled DC motor This study proposes an adjustable linear positioning control. In this paper, to optimize the parameters of the car’s position controller the sine cosine optimization algorithm (SCA) is utilized, with support from the Balloon effect (BE), The BE is incorporated to enhance the responsiveness of the traditional sine cosine optimization algorithm when faced with external disturbances and variations in system parameters. In the proposed approach, the determined value of the open loop transfer function of the motor and the updated values of the controller gains serve as the basis for the modified sine cosine algorithm’s objective function (OF). Under the influence of changes in motor parameters and step load disturbances, the system using the suggested controller is evaluated. Results from simulations and experiments show that the proposed adaptive controller, which implements the modified sine cosine algorithm, enhances the system’s overall performance in the presence of load disturbances and parameter uncertainties.

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Section: Sine Cosine Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sine cosine optimization algorithm combined with balloon effect for adaptive position control of a cart forced by an armature-controlled DC motor

Mohamed,

Alkhalaf,

Senjyu

et al. 2024

PLoS ONE

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show abstract

“…For the step size search factor r 1 = a -at / Iter max ( a is a constant, t is the number of iterations, and this paper sets a = 1) of the basic sine and cosine algorithm shows a linear decreasing trend, which is not conducive to further balancing the global search and local development capabilities of AGTO algorithm. Inspired by literature 43 , the step size search factor is improved, and the transformation curve is shown in Fig. 4 .…”

Section: Agto Algorithm Integrates Sine and Cosine And Cauchy Mutationmentioning

confidence: 99%

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

Wang,

Cao,

Chen

et al. 2024

Sci Rep

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To address the issues of lacking ability, loss of population diversity, and tendency to fall into the local extreme value in the later stage of optimization searching, resulting in slow convergence and lack of exploration ability of the artificial gorilla troops optimizer algorithm (AGTO), this paper proposes a gorilla search algorithm that integrates the positive cosine and Cauchy's variance (SCAGTO). Firstly, the population is initialized using the refractive reverse learning mechanism to increase species diversity. A positive cosine strategy and nonlinearly decreasing search and weight factors are introduced into the finder position update to coordinate the global and local optimization ability of the algorithm. The follower position is updated by introducing Cauchy variation to perturb the optimal solution, thereby improving the algorithm's ability to obtain the global optimal solution. The SCAGTO algorithm is evaluated using 30 classical test functions of Test Functions 2018 in terms of convergence speed, convergence accuracy, average absolute error, and other indexes, and two engineering design optimization problems, namely, the pressure vessel optimization design problem and the welded beam design problem, are introduced for verification. The experimental results demonstrate that the improved gorilla search algorithm significantly enhances convergence speed and optimization accuracy, and exhibits good robustness. The SCAGTO algorithm demonstrates certain solution advantages in optimizing the pressure vessel design problem and welded beam design problem, verifying the superior optimization ability and engineering practicality of the SCAGTO algorithm.

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“…The deterministic algorithm mainly adopts the mathematical programming method in operations research, and the estimation result of the model parameters is overly dependent on the selection of the initial value. 11 The heuristic algorithm 12,13 is not limited by the traditional mathematical solution rules to solve the problem, which searches through random behavior and has achieved recognized results in the field of PV parameter identification. Zhou et al 14 proposed an adaptive differential evolution algorithm based on a dynamic opposite learning strategy to effectively extract the optimal parameters of different PV cell models.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm

Zhou,

Shang

2024

Energy Science & Engineering

Self Cite

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Accurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine–cosine algorithm (SCA), named enhanced sine–cosine algorithm (ESCA), to evaluate nondirectly measurable parameters of PV cells. The ESCA introduces the concept of population average position to increase the population exploration ability, and at the same time introduces the personal destination agent mutation mechanism and competitive selection mechanism into SCA to provide more search directions for ESCA while ensuring the search accuracy and diversity maintenance. To prove that the proposed ESCA is the best choice for extracting nondirectly measurable parameters of PV cells, ESCA is evaluated by the single‐diode model, the double‐diode model, the three‐diode model, and the photovoltaic module model (PVM), and compared with eight existing popular methods. Experimental results show that ESCA outperforms similar methods in terms of diversity maintenance, high efficiency, and stability. In particular, the proposed ESCA method is less than the SCA by 0.081, 0.144, and 0.578 in the standard deviation statistics metrics of the three PVM models (PV‐PWP201, STM6‐40/36, and STP6‐120/36), respectively. Therefore, the proposed ESCA is an accurate and reliable method for parameter identification of PV cells.

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Optimization of complex engineering problems using modified sine cosine algorithm

Cited by 11 publications

References 71 publications

Sine cosine optimization algorithm combined with balloon effect for adaptive position control of a cart forced by an armature-controlled DC motor

Sine cosine optimization algorithm combined with balloon effect for adaptive position control of a cart forced by an armature-controlled DC motor

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm

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