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提出一种改进人工蜂群局部搜索能力的优化算法,对陷入局部最优值的雇佣蜂,使用禁忌表存储其局部极值,并引入混沌序列重新初始化,在迭代中产生局部极值的邻域点,帮助其逃离束缚并快速搜寻到最优解.改进算法有效地结合标准蜂群算法的全局优化能力、禁忌表的记忆能力和混沌局部搜索能力,对经典函数的测试计算表明,改进算法提高了蜂群寻优能力,在收敛速度和精度上均优于标准蜂群算法,适合工程应用中的复杂函数优化问题.
This paper proposes an improved algorithm to improve the local search ability of artificial bee colony. For the hovering into local optimal value hops, the local extremums are stored using tabu list, and chaotic sequences are reinitialized to generate neighborhoods with local extremums in iteration Points to help them escape from the shackles and quickly find the optimal solution.The improved algorithm effectively combines the global optimization ability of the standard bee colony algorithm, the memory of tabu list and the local search ability of chaos, the test calculation of classical functions shows that the improved algorithm is improved The bee colony optimization ability is superior to the standard bee colony algorithm in the convergence speed and accuracy, which is suitable for the optimization of complex functions in engineering applications.