scholarly journals Parameterization of Solar Cell Model Using Multiculture & Hybrid Mutation Based Evolutionary Programming

2018 ◽  
Vol 7 (3.4) ◽  
pp. 138
Author(s):  
Sridhar N ◽  
Nagaraj Ramrao ◽  
Manoj Kumar Singh
Keyword(s):  
Solar Cell ◽  
Cell Model ◽  
Optimal Parameter ◽  
Harmony Search ◽  
Absolute Error ◽  
Evolutionary Programming ◽  
Swarm Optimization ◽  
Second Stage ◽  
Mutation Strategy ◽  
Cauchy Mutation

In this paper, parameterization of the single diode model for solar cell has presented. The problem of obtaining the optimal parameter has transformed as an optimization problem where individual absolute error has minimized by hybrid mutation strategy in the Evolutionary programming. Hybridization has given between Gaussian mutation strategy and Cauchy mutation strategy to obtain the better offspring. To increase the reliability of the solution, two stages based a multiculture architecture has proposed. On the first stage, a multi-population strategy has applied to form a multiculture environment, where each population evolved independently to explore the solution           domain.This stage will prevent the solution to trap in the local minima. In the second stage, evolved population from first stage combine and members having high fitness are selected to form a new population of the same size as the individual population in the first stage. This second stage population evolved further to meet the final objective. The performance of the proposed method has evaluated over a 57mm diameter commercial solar cell. The obtained performance has compared with results available in current literature where various other approaches like, Levenberg–Marquardt with Simulated annealing, Global Grouping-based Harmony Search, Artificial Bee Swarm Optimization, Chaotic Particle Swarm Optimization, Differential Evolution, etc. have considered. The proposed solution has delivered the minimum error in comparison to other methods and very closer to the experimental data. 

scholarly journals Programação da produção e meta-heurísticas: uma revisão de mapeamento sistemático

2018 ◽  
Vol 14 (3) ◽  
Author(s):  
Ailton Silva Ferreira ◽  
Hussein Adnen Mustafa ◽  
Cristiano Manhães Oliveira ◽  
Tiago Andrade Muniz Terra ◽  
Thiago Dan Said
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Web Of Science ◽  
Harmony Search ◽  
Evolutionary Programming ◽  
Memetic Algorithms ◽  
Hill Climbing ◽  
Swarm Optimization ◽  
Page Rank

A Programação da Produção tem recebido atenção de pesquisadores da área de Pesquisa Operacional desde a década de 50 e um grande número de trabalhos foi desenvolvido desde então, da mesma forma, devido à Internet, o rastreamento bibliográfico de um campo de pesquisa está vez mais demorado, como também encontrar relações entre autores e publicações relevantes, está ficando cada vez mais difícil, devido ao crescente volume de informações. Dessa forma, este trabalho busca realizar uma revisão de mapeamento sistemático, com relação à programação da produção e algumas das principais meta-heurísticas (Hill Climbing, Simulated Annealing, TabuSearch,Harmony Search, GRASP, Evolutionary Algorithms, Genetic Algorithms, Evolution Strategies, Genetic Programming, Differencia lEvolution, Evolutionary Programming, Particle Swarm Optimization, Memetic Algorithms e Ant Colony), através de indicadores bibliométricos  e análise de redes (Page Rank), busca-se demonstrar a relação de artigos, autores e publicações mais importantes e de maior prestígio de dados originados da base Web of Science com auxílio das ferramentas NAILS e GEPHI. Finalmente, é demonstrado que a utilização de meta-heurísticas na Programação da Produção - PP, apesar de ser uma linha de pesquisa antiga, ainda se mantém com grande interesse e em constante desenvolvimento.   

scholarly journals Parameter Optimization of Single-Diode Model of Photovoltaic Cell Using Memetic Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Yourim Yoon ◽  
Zong Woo Geem
Keyword(s):  
Solar Cell ◽  
Least Squares Method ◽  
Memetic Algorithm ◽  
Cell Model ◽  
Harmony Search ◽  
Cuckoo Search ◽  
Photovoltaic Cell ◽  
Pattern Search ◽  
Cell Parameters ◽  
Local Searches

This study proposes a memetic approach for optimally determining the parameter values of single-diode-equivalent solar cell model. The memetic algorithm, which combines metaheuristic and gradient-based techniques, has the merit of good performance in both global and local searches. First, 10 single algorithms were considered including genetic algorithm, simulated annealing, particle swarm optimization, harmony search, differential evolution, cuckoo search, least squares method, and pattern search; then their final solutions were used as initial vectors for generalized reduced gradient technique. From this memetic approach, we could further improve the accuracy of the estimated solar cell parameters when compared with single algorithm approaches.

scholarly journals Cylindricity Error Evaluation Based on an Improved Harmony Search Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yang Yang ◽  
Ming Li ◽  
Chen Wang ◽  
QingYue Wei
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
International Standards ◽  
Zone Method ◽  
Assembly Accuracy ◽  
Mutation Strategy ◽  
Cauchy Mutation ◽  
Improved Harmony Search ◽  
Cylindricity Error

The cylindricity error is one of the basic form errors in mechanical parts, which greatly influences the assembly accuracy and service life of relevant parts. For the minimum zone method (MZM) in international standards, there is no specific formula to calculate the cylindricity error. Therefore, the evaluation methods of the cylindricity error under the MZM have been widely concerned by international scholars. To improve the evaluation accuracy and accelerate the iteration speed of the cylindricity, an improved harmony search (IHS) algorithm is proposed and applied to compute the cylindricity. On the basis of the standard harmony search algorithm, the logistic chaotic initialization is introduced into the generation of initial solution to improve the quality of solutions. During the iterative process, the global and local search capabilities are balanced by adopting the par and bw operators adaptively. After each iteration, the Cauchy mutation strategy is adopted to the best solution to further improve the calculation precision of the IHS algorithm. Finally, four test functions and three groups of cylindricity error examples were applied to validity verification of the IHS algorithm, the simulation test results show that the IHS algorithm has advantages of the computing accuracy and iteration speed compared with other traditional algorithms, and it is very effective for the application in the evaluation of the cylindricity error.

scholarly journals Estimating PV models using multi-group salp swarm algorithm

2021 ◽  
Vol 10 (2) ◽  
pp. 398
Author(s):  
Mohammad Al-Shabi ◽  
Chaouki Ghenai ◽  
Maamar Bettayeb ◽  
Fahad Faraz Ahmad ◽  
Mamdouh El Haj Assad
Keyword(s):  
Solar Cell ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Cell Models ◽  
Maximum Absolute Error ◽  
Swarm Optimization ◽  
Salp Swarm Algorithm ◽  
Squared Error ◽  
The Social ◽  
Swarm Algorithm

<span id="docs-internal-guid-ea798321-7fff-3e0c-24d7-776c9b1165b3"><span>In this paper, a multi-group salp swarm algorithm (MGSSA) is presented for estimating the photovoltaic (PV) solar cell models. The SSA is a metaheuristic technique that mimics the social behavior of the salp. The salps work in a group that follow a certain leader. The leader approaches the food source and the rest follows it, hence resulting in slow convergence of SSA toward the solution. For several groups, the searching mechanism is going to be improved. In this work, a recently developed algorithm based on several salp groups is implemented to estimate the single-, double-, triple-, Quadruple-, and Quintuple-diode models of a PV solar cell. Six versions of MGSSA algorithms are developed with different chain numbers; one, two, four, six, eight and half number of the salps. The results are compared to the regular particle swarm optimization (PSO) and some of its newly developed forms. The results show that MGSSA has a faster convergence rate, and shorter settling time than SSA. Similar to the inspired actual salp chain, the leader is the most important member in the chain; the rest has less significant effect on the algorithm. Therefore, it is highly recommended to increase the number of leaders and reduce the chain length. Increasing the number of leaders (number of groups) can reduce the root mean squared error (RMSE) and maximum absolute error (MAE) by 50% of its value.</span></span>

A Novel Control Scheme for PWM Boost Converter based on Sliding Mode Control using Particle Swarm Optimization

2020 ◽  
Vol 14 ◽  
Author(s):  
Gang Liu ◽  
Dong Qiu ◽  
Xiuru Wang ◽  
Ke Zhang ◽  
Huafeng Huang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Sliding Mode ◽  
Particle Swarm ◽  
Mathematic Model ◽  
Absolute Error ◽  
Boost Converter ◽  
Sliding Mode Controller ◽  
Swarm Optimization ◽  
Steady State Performance

Background: The PWM Boost converter is a strongly nonlinear discrete system, especially when the input voltage or load varies widely, therefore, tuning the control parameters of which is a challenge work. Objective: In order to overcome the issues, particle swarm optimization (PSO) is employed for tuning the parameters of a sliding mode controller of a boost converter. Methods: Based on the analysis of the Boost converter model and its non-linear characteristics, a mathematic model of a boost converter with a sliding mode controller is built firstly. Then, the parameters of the Boost controller are adjusted based on the integrated time and absolute error (ITAE), integral square error (ISE) and integrated absolute error (IAE) indexes by PSO. Results: Simulation verification was performed, and the results show that the controllers tuned by the three indexes all have excellent robust stability. Conclusion: The controllers tuned by ITAE and ISE indexes have excellent steady-state performance, but the overshoot is large during the startup. The controller tuned by IAE index has better startup performance and slightly worse steady-state performance.

scholarly journals Performance of the Modified Dolphin Monitoring Operator for Weight Optimization of Skeletal Structures

2018 ◽  
Author(s):  
Ali Kaveh ◽  
S.R. Hoseini Vaez ◽  
Pedram Hosseini
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Structural Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Harmony Search ◽  
Average Weight ◽  
Weight Optimization ◽  
Swarm Optimization ◽  
Population Dispersion

In this study, the Modified Dolphin Monitoring (MDM) operator is used to enhance the performance of some metaheuristic algorithms. The MDM is a recently presented operator that controls the population dispersion in each iteration. Algorithms are selected from some well-established algorithms. Here, this operator is applied on Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Vibrating Particles System (VPS), Enhanced Vibrating Particles System (EVPS), Colliding Bodied Optimization (CBO) and Harmony Search (HS) and the performance of these algorithms are evaluated with and without this operator on three well-known structural optimization problems. The results show the performance of this operator on these algorithms for the best, the worst, average and average weight of the first quarter of answers.

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