Clustering is one of the most widely used data mining techniques that can be used to create homogeneous clusters.K-means is one of the popular clustering algorithms that,despite its inherent simplicity,has also some m...Clustering is one of the most widely used data mining techniques that can be used to create homogeneous clusters.K-means is one of the popular clustering algorithms that,despite its inherent simplicity,has also some major problems.One way to resolve these problems and improve the k-means algorithm is the use of evolutionary algorithms in clustering.In this study,the Imperialist Competitive Algorithm(ICA) is developed and then used in the clustering process.Clustering of IRIS,Wine and CMC datasets using developed ICA and comparing them with the results of clustering by the original ICA,GA and PSO algorithms,demonstrate the improvement of Imperialist competitive algorithm.展开更多
This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in differe...This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in different environmental conditions.We propose a robust FLC with low computational complexity by reducing the number of membership functions and rules.To optimize the performance of the FLC,metaheuristic algorithms are employed to determine the parameters of the FLC.We evaluate the proposed FLC in various panel configurations under different environmental conditions.The results indicate that the proposed FLC can easily adapt to various panel configurations and perform better than other benchmarks in terms of enhanced stability,responsiveness,and power transfer under various scenarios.展开更多
In this paper, a new evolutionary algorithm, the well-known imperialist competition algorithm, is proposed for optimizing the optical thin-films. In this method, the process is modeled of the competition between count...In this paper, a new evolutionary algorithm, the well-known imperialist competition algorithm, is proposed for optimizing the optical thin-films. In this method, the process is modeled of the competition between countries as imperialists and their colonizing of others as colonies. This algorithm could be an appropriate alternative to some of the more popular algorithms for optimizing the optical thin-films for good performance. The polarizer and edge filter for example are designed by using the imperialist competition algorithm method and the results are compared with those from two optimization high-performance methods： the genetic algorithm and differential evolutionary algorithm. Based on these results,the performance of the imperialist competition algorithm method shows that this algorithm is not sensitive to the change of its parameters and it can be an important advantage for quickly achieving a global optimal point. On the other hand the results show a better ratio of P-polarization transmittance to S-polarization transmittance in the design of a 1540-nm polarizer, which is more appropriate than the results from the other two methods. In the second design, an edge filter with a lower number of layers and more uniform bandpass spectrum than the counterparts of those methods is obtained. These results indicate that the imperialist competition algorithm is a robust method for optical thin-film designs.展开更多
本文介绍了一种较为新颖的优化算法——拔河优化算法(tug of war optimization,TWO)[1]。该算法属于自然启发式、基于群体的元启发式算法。利用体育隐喻,将每个候选解视为参与一系列拔河比赛的团队。这些团队根据它们所代表的解的质量...本文介绍了一种较为新颖的优化算法——拔河优化算法(tug of war optimization,TWO)[1]。该算法属于自然启发式、基于群体的元启发式算法。利用体育隐喻,将每个候选解视为参与一系列拔河比赛的团队。这些团队根据它们所代表的解的质量对彼此施加拉力。竞争的团队根据牛顿力学的运动规律移动到新的位置。与许多其他元启发式方法不同,该算法考虑了相互作用团队的质量。TWO适用于全局优化问题,包括不连续、多峰、非光滑和非凸函数。并在本文中与PSO、SA等其它算法进行了对比验证,实验结果表明TWO具有较高的可靠性与搜索速度。展开更多
为提高白酒固态发酵的副产物黄水中淀粉含量预测模型精度和建模效率。采用傅里叶变换近红外光谱仪采集黄水光谱信息,利用一阶导数对光谱进行预处理,并结合偏最小二乘回归(partial least squares regression,PLSR)建立黄水淀粉定量预测...为提高白酒固态发酵的副产物黄水中淀粉含量预测模型精度和建模效率。采用傅里叶变换近红外光谱仪采集黄水光谱信息,利用一阶导数对光谱进行预处理,并结合偏最小二乘回归(partial least squares regression,PLSR)建立黄水淀粉定量预测模型。使用决定系数(R^(2))和预测均方误差(root mean square error of prediction,RMSEP)评价模型性能。光谱中含有大量冗余信息,为有效提升黄水淀粉含量检测精度和优化模型效率,将不同特征提取方法的优点结合,发现使用竞争性自适应重加权算法(competitive adaptive reweighted sampling,CARS)结合连续投影算法(successive projections algorithm,SPA)提取的光谱特征所建立的PLSR模型,相较于未使用特征提取或仅使用单一特征提取所建立的模型均有明显提升。在单一使用CARS时,模型的R^(2)为0.9654,RMSEP为0.2012%,而结合SPA后,R2为0.9738,RMSEP为0.1748%。此外,光谱维度从2203个减少到了126个,不仅提高了预测精度,也提升了建模效率。本研究提出的方法可作为黄水近红外定量模型优化的有效途径。展开更多
文摘Clustering is one of the most widely used data mining techniques that can be used to create homogeneous clusters.K-means is one of the popular clustering algorithms that,despite its inherent simplicity,has also some major problems.One way to resolve these problems and improve the k-means algorithm is the use of evolutionary algorithms in clustering.In this study,the Imperialist Competitive Algorithm(ICA) is developed and then used in the clustering process.Clustering of IRIS,Wine and CMC datasets using developed ICA and comparing them with the results of clustering by the original ICA,GA and PSO algorithms,demonstrate the improvement of Imperialist competitive algorithm.
文摘This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in different environmental conditions.We propose a robust FLC with low computational complexity by reducing the number of membership functions and rules.To optimize the performance of the FLC,metaheuristic algorithms are employed to determine the parameters of the FLC.We evaluate the proposed FLC in various panel configurations under different environmental conditions.The results indicate that the proposed FLC can easily adapt to various panel configurations and perform better than other benchmarks in terms of enhanced stability,responsiveness,and power transfer under various scenarios.
文摘In this paper, a new evolutionary algorithm, the well-known imperialist competition algorithm, is proposed for optimizing the optical thin-films. In this method, the process is modeled of the competition between countries as imperialists and their colonizing of others as colonies. This algorithm could be an appropriate alternative to some of the more popular algorithms for optimizing the optical thin-films for good performance. The polarizer and edge filter for example are designed by using the imperialist competition algorithm method and the results are compared with those from two optimization high-performance methods： the genetic algorithm and differential evolutionary algorithm. Based on these results,the performance of the imperialist competition algorithm method shows that this algorithm is not sensitive to the change of its parameters and it can be an important advantage for quickly achieving a global optimal point. On the other hand the results show a better ratio of P-polarization transmittance to S-polarization transmittance in the design of a 1540-nm polarizer, which is more appropriate than the results from the other two methods. In the second design, an edge filter with a lower number of layers and more uniform bandpass spectrum than the counterparts of those methods is obtained. These results indicate that the imperialist competition algorithm is a robust method for optical thin-film designs.
文摘本文介绍了一种较为新颖的优化算法——拔河优化算法(tug of war optimization,TWO)[1]。该算法属于自然启发式、基于群体的元启发式算法。利用体育隐喻,将每个候选解视为参与一系列拔河比赛的团队。这些团队根据它们所代表的解的质量对彼此施加拉力。竞争的团队根据牛顿力学的运动规律移动到新的位置。与许多其他元启发式方法不同,该算法考虑了相互作用团队的质量。TWO适用于全局优化问题,包括不连续、多峰、非光滑和非凸函数。并在本文中与PSO、SA等其它算法进行了对比验证,实验结果表明TWO具有较高的可靠性与搜索速度。
文摘为提高白酒固态发酵的副产物黄水中淀粉含量预测模型精度和建模效率。采用傅里叶变换近红外光谱仪采集黄水光谱信息,利用一阶导数对光谱进行预处理,并结合偏最小二乘回归(partial least squares regression,PLSR)建立黄水淀粉定量预测模型。使用决定系数(R^(2))和预测均方误差(root mean square error of prediction,RMSEP)评价模型性能。光谱中含有大量冗余信息,为有效提升黄水淀粉含量检测精度和优化模型效率,将不同特征提取方法的优点结合,发现使用竞争性自适应重加权算法(competitive adaptive reweighted sampling,CARS)结合连续投影算法(successive projections algorithm,SPA)提取的光谱特征所建立的PLSR模型,相较于未使用特征提取或仅使用单一特征提取所建立的模型均有明显提升。在单一使用CARS时,模型的R^(2)为0.9654,RMSEP为0.2012%,而结合SPA后,R2为0.9738,RMSEP为0.1748%。此外,光谱维度从2203个减少到了126个,不仅提高了预测精度,也提升了建模效率。本研究提出的方法可作为黄水近红外定量模型优化的有效途径。
