论文部分内容阅读
借鉴自然界群居生物的搜索行为模式,提出一种群体区域搜索算法.该算法在优化过程中逐步收缩个体搜索半径并进行适度随机调整,引入巡游追随机制,以一种简单而自然的方式有效地实现了算法广域探索能力与局部开发能力之间的平衡.算法结构简单、易实现,易与其他算法相结合.通过6个典型测试函数的实验结果表明,该算法全局优化能力强、收敛精度高、稳定性好、总体性能优,适用于复杂函数优化问题的处理.
In this paper, a group region search algorithm is proposed by reference to the search behavior patterns of gregarious creatures in nature. This algorithm narrows the search radius of individuals gradually and adjusts them moderately and stochastically in the process of optimization, and introduces a parade follow-up mechanism to effectively implement in a simple and natural way The algorithm has the advantages of simple structure, easy implementation and easy combination with other algorithms.Experimental results of six typical test functions show that the algorithm has strong global optimization ability and high convergence precision , Good stability, excellent overall performance, suitable for complex function optimization problems.