Particle Swarm Optimization
Particle Swarm Optimization
复制标题
DOI：
10.1201/9781003206477-5
复制
发表时间：
2021-08
期刊：
影响因子：
--
通讯作者：
A. Badar
中科院分区：
文献类型：
--
作者：
A. Badar
姓名：wow gold Devarakonda，SaiPrasanth代顿大学顾问：Raul Ordonez博士粒子群算法是一种迭代优化问题的计算方法。由于邻域决定了信息流的充分性和频繁性，本文讨论了静态邻域和动态邻域。总结了针对特定问题选择算法的不同方法的特点。采用三种不同的方法研究了动态邻域粒子群优化算法的性能。在目前的工作中，两个基准测试功能使用该算法进行测试。通过测试不同的基准函数，得出结论，反映了粒子群算法的性能与动态邻域。用同步和异步粒子群算法对所有的基准函数进行了分析。
PARTICLE SWARM OPTIMIZATION Name: Devarakonda, SaiPrasanth University of Dayton Advisor: Dr. Raul Ordonez The particle swarm algorithm is a computational method to optimize a problem iteratively. As the neighborhood determines the sufficiency and frequency of information flow, the static and dynamic neighborhoods are discussed. The characteristics of the different methods for the selection of the algorithm for a particular problem are summarized. The performance of particle swarm optimization with dynamic neighborhood is investigated by three different methods. In the present work two more benchmark functions are tested using the algorithm. Conclusions are drawn by testing the different benchmark functions that reflect the performance of the PSO with dynamic neighborhood. And all the benchmark functions are analyzed by both Synchronous and Asynchronous PSO algorithms.