[1]尚志刚,王力,李蒙蒙,等.引入迷失探索与集群分裂机制的改进鸽群优化算法[J].郑州大学学报(工学版),2019,40(04):5.
 Improved Pigeon Herd Optimization Algorithm with Lost Exploration and Cluster Splitting Mechanism[J].Journal of Zhengzhou University (Engineering Science),2019,40(04):5.
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引入迷失探索与集群分裂机制的改进鸽群优化算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40
期数:
2019年04期
页码:
5
栏目:
出版日期:
2019-07-10

文章信息/Info

Title:
Improved Pigeon Herd Optimization Algorithm with Lost Exploration and Cluster Splitting Mechanism
作者:
尚志刚王力李蒙蒙李志辉
文献标志码:
A
摘要:
鸽群优化算法(Pigeon inspired optimization,PIO)作为一种新兴的优化技术,具有收敛速度快,精度高等优点,但其对于一些具有局部最优值的问题它的求解效果并不理想.将自然界中鸽群飞行时的迷失探索和集群分裂机制引入了原始鸽群优化算法,提出了一种迷失探索与集群分裂鸽群优化算法(Lost and split pigeon inspired optimization,LSPIO),迷失探索机制的引用加强了算法的全局搜索性能,而集群分裂机制增加了种群多样性.本文选取9个标准测试函数进行算法性能评估,并与标准鸽群算法和粒子群算法进行对比,结果表明,LSPIO算法在保持良好收敛性质的同时可以有效的避免早熟问题,且提高种群多样性
Abstract:
Pigeon inspired optimization (PIO) algorithm , as an emerging optimization technology, has the advantages of fast convergence and high precision. But it is not ideal for some problems with local optimal values. By introducing lost&exploration and c luster splitting mechanisms of natural flying pigeons, an improved PIO algorithm based on lost&exploration and cluster splitting (LSPIO) is proposed in this paper. The lost&exploration mechanism enhances the global search performance of the algorithm, and the cluster splitting mechanism increases the diversity of the population. In this paper, 9 standard test functions are selected for algorithm performance evaluation. Compared with standard pigeon group algorithm and particle swarm algorithm, the results show that the new LSPIO algorithm can effectively avoid premature problems while maintaining good convergence properties
更新日期/Last Update: 2019-07-29