欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
针对经典混合蛙跳优化算法寻优精度不高和易陷入局部收敛区域的缺点，结合云模型在定性与定量之间相互转换的优良特性，提出一种自适应分组混沌云模型蛙跳算法．通过反向学习机制初始化种群，应用云模型算法对优秀子群组的收敛区域进行局部搜索更优位置，应用混沌理论在收敛区域以外空间探索全局最优位置．典型复杂函数测试表明，所提出的算法能有效找出全局最优解，适用于多峰值函数寻优．
英文摘要：
The shuffled flog leaping algorithm for optimization in function easily falls into local optimal solution and the premature quickly converges of such shortcomings. Combined with the excellent characteristics of cloud model transformation between qualitative and quantitative, an adaptive grouping chaotic cloud model shuffled frog leaping algorithm is proposed based on the cloud model theory. The population is initialized through reverse learning mechanism, the cloud model algorithm is used to local refinement in the region of convergence in order to explore the better position, and the chaos theory is used to obtain global optimization in the space outside the convergence region in order to explore the global optimum position. The simulation results show that the proposed algorithm has fine capability of finding global optimum, especially multi-peak function.
同期刊论文项目
同项目期刊论文
Quantum ant colony optimization algorithm based on Bloch spherical search (SCI: 000309320500002; EI:
Model and algorithm of quantum inspired neural network with sequence input based on controlled rotat
期刊信息
位置：