Optimization Algorithm
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2021 ◽  
Zhifeng Zhang ◽  
Shaolin Zhu ◽  
Tianqi Li ◽  
Baohuan Li

Abstract With the increasing of the number of dimensions or variables in the search space, the inductive learning of fuzzy rule classifier will be influenced by the generation and optimization of rules. Thus, the extensibility and accuracy of fuzzy systems will be affected. In this paper, the brain storm optimization algorithm was used. A new fuzzy system was designed by modifying the rules definition process in traditional fuzzy system. In the derivation of rules, the exponential model was introduced to improve the traditional brain storming algorithm. On the basis, this new fuzzy system was used for the research on data classification. The experimental results show that this new fuzzy system can improve the accuracy of data classification.

2021 ◽  
Vol 12 (2) ◽  
pp. 875-889
Yitian Wang ◽  
Liu Zhang ◽  
Huanyu Zhao ◽  
Fan Zhang

Abstract. A thin-film diffraction imaging system is a type of space telescope imaging system with high resolution and loose surface tolerance often used in various fields, such as ground observation and military reconnaissance. However, because this system is a large and flexible multi-body structure, it can produce flexural vibration easily during the orbit operation, which has a serious effect on the attitude stability of the system and results in low pointing accuracy. Therefore, this study proposes an optimization method based on the Kriging model and the improved particle swarm optimization algorithm to improve the stability and optimize the structure of the entire system. Results showed the area–mass ratio of the thin-film diffraction imaging system decreased by 9.874 %, the first-order natural frequency increased by 23.789 %, and the attitude stability of the thin-film diffraction imaging system improved.

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Jasleen Kaur ◽  
Punam Rani ◽  
Brahm Prakash Dahiya

Purpose This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency. Design/methodology/approach The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB. Findings The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly Optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay. Originality/value This research work is original.

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2308
Abdelhady Ramadan ◽  
Salah Kamel ◽  
Ibrahim B. M. Taha ◽  
Marcos Tostado-Véliz

The increase in industrial and commercial applications of photovoltaic systems (PV) has a significant impact on the increase in interest in studying the improvement of the efficiency of these systems. Estimating the efficiency of PV is considered one of the most important problems facing those in charge of manufacturing these systems, which makes it interesting to many researchers. The difficulty in estimating the efficiency of PV is due to the high non-linear current–voltage characteristics and power–voltage characteristics. In addition, the absence of ample efficiency information in the manufacturers’ datasheets has led to the development of an effective electrical mathematical equivalent model necessary to simulate the PV module. In this paper, an application for an optimization algorithm named Wild Horse Optimizer (WHO) is proposed to extract the parameters of a double-diode PV model (DDM), modified double-diode PV model (MDDM), triple-diode PV model (TDM), and modified triple-diode PV model (MTDM). This study focuses on two main objectives. The first concerns comparing the original models (DDM and TDM) and their modification (MDDM and MTDM). The second concerns the algorithm behavior with the optimization problem and comparing this behavior with other recent algorithms. The evaluation process uses different methods, such as Root Mean Square Error (RMSE) for accuracy and statistical analysis for robustness. Based on the results obtained by the WHO, the estimated parameters using the WHO are more accurate than those obtained by the other studied optimization algorithms; furthermore, the MDDM and MTDM modifications enhanced the original DDM and TDM efficiencies.

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2299
Łukasz Knypiński ◽  
Sebastian Kuroczycki ◽  
Fausto Pedro García Márquez

This paper presents the application of the cuckoo search (CS) algorithm in attempts to the minimization of the commutation torque ripple in the brushless DC motor (BLDC). The optimization algorithm was created based on the cuckoo’s reproductive behavior. The lumped-parameters mathematical model of the BLDC motor was developed. The values of self-inductances, mutual inductances, and back-electromotive force waveforms applied in the mathematical model were calculated by the use of the finite element method. The optimization algorithm was developed in Python 3.8. The CS algorithm was coupled with the static penalty function. During the optimization process, the shape of the voltage supplying the stator windings was determined to minimize the commutation torque ripple. Selected results of computer simulation are presented and discussed.

Zhongda Tian

In recent years, short-term wind power forecasting has proved to be an effective technology, which can promote the development of industrial informatization and play an important role in solving the control and utilization problems of renewable energy system. However, the application of short-term wind power prediction needs to deal with a large number of data to avoid the instability of forecasting, which is facing more and more difficulties. In order to solve this problem, this paper proposes a novel prediction approach based on kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm. Short-term wind power generation is affected by many factors. The original multi-dimensional input variables are pre-processed by kernel principal component analysis to determine the principal components that affect wind power. The dimension of principal component is less than the original input data, which reduces the complexity of modeling. The convergence and stability of the echo state network can be improved by using the principal component of the input variable. The advantage is to reduce the input variables, eliminate the correlation between the input variables, and improve the prediction performance of the prediction model. Furthermore, an improved particle swarm optimization algorithm is proposed to optimize the dynamic reservoir parameters of echo state network. Compared with other state-of-the-art prediction models, the case studies show that the proposed approach has good prediction performance for actual wind power data.

2021 ◽  
Vol 11 (18) ◽  
pp. 8709
Marek Sznura ◽  
Piotr Przystałka

This paper deals with the development of a power and communication bus named DLN (Device Lightweight Network) that can be seen as a new interface with auto-addressing functionality to transfer power and data by means of two wires in modern cars. The main research goal of this paper is to elaborate a new method based on a hardware in the loop technique aided by computational intelligence algorithms in order to search for the optimal structure of the communication modules, as well as optimal features of hardware parts and the values of software parameters. The desired properties of communication modules, which have a strong influence on the performance of the bus, cannot be found using a classical engineering approach due to the large number of possible combinations of configuration of the hardware and software parts of the whole system. Therefore, an HIL-based optimization method for bus prototyping is proposed, in which the optimization task is formulated as a multi-criteria optimization problem. Several criterion functions are proposed, corresponding to the automotive objectives and requirements. Different soft computing optimization algorithms, such as a single-objective/multi-objectives evolutionary algorithm and a particle swarm optimization algorithm, are applied to searching for the optimal solution. The verification study was carried out in order to show the merits and limitations of the proposed approach. Attention was also paid to the problem of the selection of the behavioural parameters of the heuristic algorithms. The overall results proved the high practical potential of the DLN, which was developed using the proposed optimization method.

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 273
José M. Villegas ◽  
Camilo Caraveo ◽  
David A. Mejía ◽  
José L. Rodríguez ◽  
Yuridia Vega ◽  

The optimization is essential in the engineering area and, in conjunction with use of meta-heuristics, has had a great impact in recent years; this is because of its great precision in search of optimal parameters for the solution of problems. In this work, the use of the Artificial Bee Colony Algorithm (ABC) is presented to optimize the values for the variables of a proportional integral controller (PI) to observe the behavior of the controller with the optimized Ti and Kp values. It is proposed using a robot built using the MINDSTORMS version EV3 kit. The objective of this work is to demonstrate the improvement and efficiency of the controllers in conjunction with optimization meta-heuristics. In the results section, we observe that the results improve considerably compared to traditional methods. In this work, the main contribution is the implementation of an optimization algorithm (ABC) applied to a controller (PI), and the results are tested to control the movement of a robot. There are many papers where the kit is used in various domains such as education as well as research for science and technology tasks and some real-world problems by engineering scholars, showing the acceptable result.

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