摘要
为了更有效地求解复杂的非线性方程组,引入了人工蜂群算法。考虑到人工蜂群算法后期表现出的收敛速度慢、容易陷入局部最优值的缺点,提出了一种新的人工蜂群优化算法(IABC)。新算法对工蜂进行邻域搜索产生新解的方法进行了改进,引入了尝试次数,修改了向新食物源靠拢的递进步长,加快了原有算法的收敛速度。试验结果表明,改进算法较好地平衡了全局搜索能力和局部搜索能力,是一种求解非线性方程组的高效算法。
To solve the complex nonlinear equations more effectively, the artificial bee colony ( ABC ) algorithm is introduced. Considering the demerit of artificial bee colony algorithm, i. e. , easily to fall into local optimum value because of the slow late convergence speed, an improved artificial bee colony ( IABC ) optimized algorithm is proposed. The new algorithm improves the method for producing new solution when bees are doing neighborhood search, introduces the number of attempts, modifies the progressive step moving to a new food resource, and accelerates the original convergence speed. The test result indicates that the improved algorithm well balances the global and local seeking capability, so it is a highly efficient algorithm for solving nonlinear equations.
出处
《自动化仪表》
CAS
北大核心
2013年第2期19-22,共4页
Process Automation Instrumentation
基金
河北省教育厅自然科学基金资助项目(编号:z2010165)
河北省教育厅自然科学青年基金资助项目(编号:2011226)
关键词
人工蜂群算法
非线性方程组
邻域搜索
全局优化
人工智能
Artificial bee colony algorithm Nonlinear equations Neighborhood search Global optimization Artificial intelligence