Comment on “Particle Swarm Optimization Based Highly Nonlinear Substitution-Boxes Generation for Security Applications”

Author(s):  
Alexandr Kuznetsov ◽  
Kateryna Kuznetsova
IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 116132-116147 ◽  
Author(s):  
Musheer Ahmad ◽  
Ishfaq Ahmad Khaja ◽  
Abdullah Baz ◽  
Hosam Alhakami ◽  
Wajdi Alhakami

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R767-R781 ◽  
Author(s):  
Mattia Aleardi ◽  
Silvio Pierini ◽  
Angelo Sajeva

We have compared the performances of six recently developed global optimization algorithms: imperialist competitive algorithm, firefly algorithm (FA), water cycle algorithm (WCA), whale optimization algorithm (WOA), fireworks algorithm (FWA), and quantum particle swarm optimization (QPSO). These methods have been introduced in the past few years and have found very limited or no applications to geophysical exploration problems thus far. We benchmark the algorithms’ results against the particle swarm optimization (PSO), which is a popular and well-established global search method. In particular, we are interested in assessing the exploration and exploitation capabilities of each method as the dimension of the model space increases. First, we test the different algorithms on two multiminima and two convex analytic objective functions. Then, we compare them using the residual statics corrections and 1D elastic full-waveform inversion, which are highly nonlinear geophysical optimization problems. Our results demonstrate that FA, FWA, and WOA are characterized by optimal exploration capabilities because they outperform the other approaches in the case of optimization problems with multiminima objective functions. Differently, QPSO and PSO have good exploitation capabilities because they easily solve ill-conditioned optimizations characterized by a nearly flat valley in the objective function. QPSO, PSO, and WCA offer a good compromise between exploitation and exploration.


Author(s):  
Hossein Mansourinejad ◽  
Kamran Daneshjou

The performance function of many engineering structures and mechanisms is usually complex, highly nonlinear, and described in the implicit form. The reliability analysis of these structures using common methods requires high cost and time. In this paper, a new approach for reliability analysis of engineering structures and mechanisms by using the particle swarm optimization algorithm is presented. The advantages of this method in comparison with the conventional methods are its simplicity and accuracy. In addition, the limitations of the common previously presented methods are eliminated by the proposed method. This approach is based on a new redefinition of most probable point in the reliability analysis. To evaluate the performance and validity of the proposed method, some examples in the reliability analysis of various functions are employed. Finally, the superiority of the proposed method in performance and accuracy is demonstrated and compared to the conventional methods and it can be used for reliability analysis of complicated engineering structures.


2020 ◽  
Vol 23 (1) ◽  
pp. 45-50
Author(s):  
Hazem Ali ◽  
Azhar Jabbar Abdulridha ◽  
Rawaa Khaleel ◽  
Kareem Kareem A. Hussein

In this work, the design procedure of a hybrid robust controller for crane system is presented. The proposed hybrid controller combines the linear quadratic regulator (LQR) properties with the sliding mode control (SMC) to obtain an optimal and robust LQR/SMC controller. The crane system which is represented by pendulum and cart is used to verify the effectiveness of the proposed controller. The crane system is considered one of the highly nonlinear and uncertain systems in addition to the under-actuating properties. The parameters of the proposed LQR/SMC are selected using Particle Swarm Optimization (PSO) method. The results show that the proposed LQR/SMC controller can achieve a better performance if only SMC controller is used. The robustness of the proposed controller is examined by considering a  variation in system parameters with applying an external disturbance input. Finally, the superiority of the proposed LQR/SMC controller over the SMC controller is shown in this work.


2015 ◽  
Vol 785 ◽  
pp. 58-62 ◽  
Author(s):  
Muhd Azri Abdul Razak ◽  
Muhammad Murtadha Othman ◽  
Mohd Ainor Yahya ◽  
Zilaila Zakaria ◽  
Ismail Musirin ◽  
...  

Installing capacitors in a large unbalanced electrical distribution system will indeed improves the performance of the system in terms of its voltage profile and real power loss stability. However, determining the suitable locations for capacitors installation with an appropriate sizing in an unbalanced electrical distribution system involves an intricate process. This impediment can be resolved by implementing an optimal capacitors placement and sizing. The proposed technique is a highly nonlinear optimization problem which requires discrete and multi-dimensional control variables of capacitor locations and sizes. This paper proposed a new artificial intelligence approach used to reduce the total line real power loss and total real power consumption while maintaining the voltage profile along the feeders. It was done by integrating the circuitry schematic diagram of an unbalanced electrical distribution system modeled in SIMULINK® software with the computational programming based differential evolution particle swarm optimization (DEPSO) for optimal capacitors placement and sizing developed under the MATLAB® software. In this study, pre-selection of the capacitor locations can be considered as the first stage of the proposed concept and it is commenced prior to the optimization process performed by the DEPSO algorithm considered as the second stage of the proposed concept. A modified IEEE 13-bus unbalanced radial distribution system is used verify effectiveness of the proposed technique in solving the problem. The results will be discussed notably through comparative studies on the objective function of total cost and performance of the DEPSO technique.


2018 ◽  
Vol 24 (2) ◽  
pp. 101-115 ◽  
Author(s):  
Abdul Jaleel ◽  
K. Aparna

Distillation is the most commonly used method for separating fluid mixtures in oil and gas industries. It is a process that requires high energy usage. One of the efficient ways to save energy in a distillation column is by heat integration. One such type of distillation column is called a heat-integrated distillation column (HIDC). In HIDC, the prediction of mole fractions of the component in the product can be made using proper identification, or modeling, of the HIDC. However, nonlinear modeling of HIDC is a highly challenging task. Methods based on first principles are not sufficient for a highly nonlinear HIDC. Hence, a novel method for identification of HIDC using a non-parametric ?support vector regression (SVR)? method for predicting benzene composition in benzene-toluene HIDC is proposed in this work. The data used for identification is generated using process simulation software HYSYS. 100 samples of data were used for training and 50 samples of data were employed for validating the model. Particle swarm optimization (PSO) was also incorporated with SVR for obtaining optimized parameters of SVR. The proposed model is compared with other SVR models optimized with optimization methods other than PSO. The proposed model showed better performance over others.


Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Angus Wu ◽  
Zhen-Lun Yang

Population-based optimization algorithms are useful tools in solving engineering problems. This paper presents an elitist transposon quantum-based particle swarm algorithm to solve economic dispatch (ED) problems. It is a complex and highly nonlinear constrained optimization problem. The proposed approach, double elitist breeding quantum-based particle swarm optimization (DEB-QPSO), makes use of two elitist breeding strategies to promote the diversity of the swarm so as to enhance the global search ability and an improved efficient heuristic handling technique to manage the equality and inequality constraints of ED problems. Investigating on 15-unit, 40-unit, and 140-unit widely used test systems, through performance comparison, the proposed DEB-QPSO algorithm is able to obtain higher-quality solutions efficiently and stably superior than the other the state-of-the-art algorithms.


Author(s):  
Ernesto Araujo ◽  
Ubiratan S. Freitas ◽  
Elbert A. N. Macau ◽  
Leandro S. Coelho ◽  
Luis A. Aguirre

Two nonlinear identification methods are employed in this paper in an experimental comparative approach to generate dynamical models for a thermal-vacuum system. Used for space environment emulation and satellite qualification, a thermal-vacuum chamber presents highly nonlinear and time-delay characteristics. While, in the first nonlinear identification approach, Particle Swarm Optimization (PSO) derive a Takagi-Sugeno fuzzy model, the second one was based on NARMAX polynomial identification technique. PSO is a stochastic global optimization technique that uses a population of particles, where the position and velocity of each particle represent a solution to the problem. It is employed as an auxiliary mechanism for finding out a T-S fuzzy model. The NARMAX polynomial identification technique uses a criterion called Error Reduction Ratio (ERR) computed by employing an orthogonal least squares method whose terms are selected in a forward-regression manner. Results indicate that both methods are feasible solutions for eliciting models from the available data.


2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.


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