摘要
针对基本FPA算法在求解复杂工程优化问题时存在收敛速度慢的缺点,提出应用精英混沌搜索的花粉授粉算法(CSFPA).CSFPA算法在搜索过程中从当前种群中随机选择出一个个体,对其执行精英混沌搜索操作,从而加快算法的收敛速度.将提出的CSFPA算法与基本FPA算法在几个国际上常用的基准测试问题上进行了比较实验,实验结果表明CSFPA算法能够在大多数测试问题上比基本FPA算法获得更优的结果.
Flower pollination algorithm （FPA） is an emerging function optimization algorithm. However, the traditional FPA tends to suffer from slow convergence when solving complex engineering optimization problems. Aiming at this weakness of the basic FPA, an enhanced flower pollination algorithm based on elite chaotic search （CSFPA） is proposed in this paper. In the evolution process, CSFPA randomly selects an individual to execute the elite chaotic search strategy, which can accelerate the convergence speed. In the experiments, the proposed CSFPA is compared with the basic FPA on several benchmark test problems. The experimental results validate the effectiveness of the proposed CSFPA.
出处
《江西理工大学学报》
CAS
2015年第5期74-79,共6页
Journal of Jiangxi University of Science and Technology
基金
江西省青年科学家(井冈之星)培养对象资助项目(20153BCB23010)
关键词
优化算法
演化算法
混沌搜索
花粉授粉算法
optimization algorithm
evolutionary algorithm
chaotic search
flower pollination algorithm
