In this paper, the sine-cosine optimization algorithm (SCO) is used to solve the shape optimization of a vehicle clutch lever. The design problem is posed for the shape optimization of a clutch lever with a mass objec...
详细信息
In this paper, the sine-cosine optimization algorithm (SCO) is used to solve the shape optimization of a vehicle clutch lever. The design problem is posed for the shape optimization of a clutch lever with a mass objective function and a stress constraint. Actual function evaluations are based on finite element analysis, while the response surface method is used to obtain the equations for objective and constraint functions. Recent optimization techniques such as the salp swarm algorithm, grasshopper optimizationalgorithm, and sine-cosinealgorithm are used for shape optimization. The results show the ability of the sine-cosine optimization algorithm to optimize automobile components in the industry.
One of the most common diseases in the world is diabetes for which no certain cure has been found yet;the only promising way for these patients to survive is to take care. Fasting blood sugar (FBS) is one of the most ...
详细信息
ISBN:
(纸本)9781728108728
One of the most common diseases in the world is diabetes for which no certain cure has been found yet;the only promising way for these patients to survive is to take care. Fasting blood sugar (FBS) is one of the most important indicators of diabetes. But its test is not feasible for the public and requires preparations before implementation. In this study, the prediction of fasting blood sugar (FBS) is considered as a strategy for predicting diabetes for the first time. This study presents a model for prediction of FBS from other factors in blood test of the people. The proposed model, best feature is selected using sine-cosine optimization algorithm;in the second phase, uses neural network (NN) for prediction. In fact, the idea behind this study is to improve sine-cosinealgorithm in selecting the features of dataset derived from diabetic patients of Isfahan city which has not been conducted so far. The prediction results of three different neural networks (with training and supervision, without supervision and semi-supervision) showed that multilayer perceptron NN managed to predict FBS with error less than 0.0017.
Optimal conductor selection can reduce the electrical power losses and enhance the voltage profile of electrical power systems in a cost-effective manner. In this paper, a recent population-based optimization, known a...
详细信息
ISBN:
(纸本)9781538609903
Optimal conductor selection can reduce the electrical power losses and enhance the voltage profile of electrical power systems in a cost-effective manner. In this paper, a recent population-based optimization, known as sine-cosine optimization algorithm (SCA), is used for the optimal selection of conductors in radial distribution networks. A current library of conductors based on actual manufacturer data that include twenty different types of conductors is employed. The proposed algorithm is applied to a real Egyptian distribution system. The achieved results revealed the effectiveness of the SCA in reducing the network losses while maintaining the specified constraints over a ten-year period taking into account high annual load growth rate.
The PID controller is one of the common control strategies in automatic control systems and is applied in various practical scenarios. Optimizing the design of PID controllers is an important topic at present. In this...
详细信息
The PID controller is one of the common control strategies in automatic control systems and is applied in various practical scenarios. Optimizing the design of PID controllers is an important topic at present. In this article, to solve the disadvantages of traditional PID parameter tuning methods such as time-consuming, prone to local search, complex calculation, and unclear termination criteria, a PID parameter tuning strategy based on multi-strategy fusion improved zebra optimizationalgorithm (MZOA) is proposed. For a series of problems such as the zebra optimizationalgorithm (ZOA) is prone to local optimization and slow convergence speed, the chaotic mapping and householder mirror reflection learning are combined to initialize the population, improve the distribution quality of the initial population in the search space, and introduce the tangent flight strategy based on the tangent search algorithm. The tangent flight strategy can stably produce a larger step length throughout the iteration, optimize the global search ability of the algorithm, and avoid falling into the local optimum. In the stage of resisting predator attacks, a sine-cosine optimization algorithm on hyperbolic cosine enhancement factor is introduced, using its oscillation to disturb the population and enhance the global search ability. Finally, the improved zebra optimizationalgorithm is used to optimize the parameters of the PID controller, and the MZOA-PID parameter tuning model and the ZOA-PID parameter tuning model are simulated. The simulation results show that compared with ZOA, MZOA has higher convergence accuracy and performance, can tune PID parameters faster, and makes the actual output curve of PID control parameters closest to the theoretical output curve.
In modern power systems, microgrids play a pivotal role with several economical, technical, and environmental benefits. However, there are still challenges that need to be properly addressed, including: i) accurate mo...
详细信息
In modern power systems, microgrids play a pivotal role with several economical, technical, and environmental benefits. However, there are still challenges that need to be properly addressed, including: i) accurate modeling of the uncertain parameters behavior, ii) considering the correlation between the random variables, and iii) find the optimal solutions with low computational burden. To address these issues, this paper proposes a nonparametric-correlative stochastic framework for microgrids (MGs) energy management. The proposed method imposes no assumption on the probability density function of renewable generations and electrical load consumption. To this end, an improved kernel density estimator (IKDE) is presented to estimate the probability density function (PDF) of uncertain parameters, e.g., renewable generations and load. To account for the correlation between the uncertain parameters, Cholesky decomposition is utilized. Furthermore, a multi-objective MG energy management problem considering reliability has been reformulated, and to solve the problem, a sine-cosine optimization algorithm (SCOA) is developed. Numerical results demonstrate the effectiveness and superiority of the proposed stochastic framework through comparison with several optimizationalgorithms by reducing the total cost of MG more than 11% in comparison with several metaheuristic algorithms and stochastic frameworks with less than 1% error.
COVID-19 is a global pandemic that aroused the interest of scientists to prevent it and design a drug for it. Nowadays, presenting intelligent biological data analysis tools at a low cost is important to analyze the b...
详细信息
COVID-19 is a global pandemic that aroused the interest of scientists to prevent it and design a drug for it. Nowadays, presenting intelligent biological data analysis tools at a low cost is important to analyze the biological structure of COVID-19. The global alignment algorithm is one of the important bioinformatics tools that measure the most accurate similarity between a pair of biological sequences. The huge time consumption of the standard global alignment algorithm is its main limitation especially for sequences with huge lengths. This work proposed a fast global alignment tool (G-Aligner) based on meta-heuristic algorithms that estimate similarity measurements near the exact ones at a reasonable time with low cost. The huge length of sequences leads G-Aligner based on standard sine-cosine optimization algorithm (SCA) to trap in local minima. Therefore, an improved version of SCA was presented in this work that is based on integration with PSO. Besides, mutation and opposition operators are applied to enhance the exploration capability and avoiding trapping in local minima. The performance of the improved SCA algorithm (SP-MO) was evaluated on a set of IEEE CEC functions. Besides, G-Aligner based on the SP-MO algorithm was tested to measure the similarity of real biological sequence. It was used also to measure the similarity of the COVID-19 virus with the other 13 viruses to validate its performance. The tests concluded that the SP-MO algorithm has superiority over the relevant studies in the literature and produce the highest average similarity measurements 75% of the exact one. (C) 2021 Elsevier B.V. All rights reserved.
暂无评论