元优化
帝国主义竞争算法
多群优化
无导数优化
群体行为
粒子群优化
数学优化
元启发式
算法
计算机科学
水准点(测量)
并行元启发式
优化算法
最优化问题
数学
大地测量学
地理
作者
Tiantian Wang,Long Yang
出处
期刊:Cornell University - arXiv
日期:2018-08-01
被引量:1
标识
DOI:10.48550/arxiv.1808.00206
摘要
In this paper, a new meta-heuristic algorithm, called beetle swarm optimization algorithm, is proposed by enhancing the performance of swarm optimization through beetle foraging principles. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization algorithm, genetic algorithm (GA) and grasshopper optimization algorithm . Numerical experiments show that the beetle swarm optimization algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design problems, namely, pressure vessel design problem and himmelblaus optimization problem, are also considered and the proposed beetle swarm optimization algorithm is shown to be competitive in those applications.
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