In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of ...In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of the first carrier wave’s search for the optimal point in implementing the sophisticated searching during the second carrier wave is faster and more accurate. In addition, the concept of using the carrier wave three times is proposed and put into practice to tackle the multi-variables opti- mization problems, where the searching for the optimal point of the last several variables is frequently worse than the first several ones.展开更多
To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)mo...To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN.展开更多
郊狼优化算法(Coyote optimization algorithm,COA)是最近提出的一种新颖且具有较大应用潜力的群智能优化算法,具有独特的搜索机制和能较好解决全局优化问题等优势,但在处理复杂优化问题时存在搜索效率低、可操作性差和收敛速度慢等不足...郊狼优化算法(Coyote optimization algorithm,COA)是最近提出的一种新颖且具有较大应用潜力的群智能优化算法,具有独特的搜索机制和能较好解决全局优化问题等优势,但在处理复杂优化问题时存在搜索效率低、可操作性差和收敛速度慢等不足.为弥补其不足,并借鉴灰狼优化算法(Grey wolf optimizer,GWO)的优势,提出了一种COA与GWO的混合算法(Hybrid COA with GWO,HCOAG).首先提出了一种改进的COA(Improved COA,ICOA),即将一种高斯全局趋优成长算子替换原算法的成长算子以提高搜索效率和收敛速度,并提出一种动态调整组内郊狼数方案,使得算法的搜索能力和可操作性都得到增强;然后提出了一种简化操作的GWO(Simplified GWO,SGWO),以提高算法的可操作性和降低其计算复杂度;最后采用正弦交叉策略将ICOA与SGWO二者融合,进一步获得更好的优化性能.大量的经典函数和CEC2017复杂函数优化以及K-Means聚类优化的实验结果表明,与COA相比,HCOAG具有更高的搜索效率、更强的可操作性和更快的收敛速度,与其他先进的对比算法相比,HCOAG具有更好的优化性能,能更好地解决聚类优化问题.展开更多
基金Project supported by the National Natural Science Foundation of China (No. 60474064), and the Natural Science Foundation of Zhejiang Province (No. Y105694), China
文摘In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of the first carrier wave’s search for the optimal point in implementing the sophisticated searching during the second carrier wave is faster and more accurate. In addition, the concept of using the carrier wave three times is proposed and put into practice to tackle the multi-variables opti- mization problems, where the searching for the optimal point of the last several variables is frequently worse than the first several ones.
基金supported in part by the National Key Research and Development Program of China(No.2018YFB1500800)the National Natural Science Foundation of China(No.51807134)the State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology(No.EERI_KF20200014)。
文摘To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN.
文摘郊狼优化算法(Coyote optimization algorithm,COA)是最近提出的一种新颖且具有较大应用潜力的群智能优化算法,具有独特的搜索机制和能较好解决全局优化问题等优势,但在处理复杂优化问题时存在搜索效率低、可操作性差和收敛速度慢等不足.为弥补其不足,并借鉴灰狼优化算法(Grey wolf optimizer,GWO)的优势,提出了一种COA与GWO的混合算法(Hybrid COA with GWO,HCOAG).首先提出了一种改进的COA(Improved COA,ICOA),即将一种高斯全局趋优成长算子替换原算法的成长算子以提高搜索效率和收敛速度,并提出一种动态调整组内郊狼数方案,使得算法的搜索能力和可操作性都得到增强;然后提出了一种简化操作的GWO(Simplified GWO,SGWO),以提高算法的可操作性和降低其计算复杂度;最后采用正弦交叉策略将ICOA与SGWO二者融合,进一步获得更好的优化性能.大量的经典函数和CEC2017复杂函数优化以及K-Means聚类优化的实验结果表明,与COA相比,HCOAG具有更高的搜索效率、更强的可操作性和更快的收敛速度,与其他先进的对比算法相比,HCOAG具有更好的优化性能,能更好地解决聚类优化问题.
