摘要
针对已有的基于差分演化算法的两阶段均匀实验设计方法仍存在种群在约束区域分布多样性不佳和局部搜索能力不强的问题,提出了一种基于果蝇算法的两阶段均匀实验设计方法(two phase fruit fly optimization algorithm, ToPFOA)。ToPFOA第1阶段运用融合差分算子的果蝇搜索策略、基于K-means聚类及外部文档更新类中心等方法,以动态改进种群在约束区域分布的多样性;在此基础上,第2阶段进一步使用自定义果蝇算子提高约束区域内局部搜索能力。实验结果表明ToPFOA在解质量和稳定性上均优于ToPDE和ToPDEEDA。
To solve the problems that existing two-phase differential evolutionary algorithms still have poor diversity of population distribution and weak local search ability in solving uniform designs in constrained experimental region, a new two-phase fruit fly optimization algorithm(ToPFOA) based on uniform experimental design is proposed. In the first stage, fruit fly search strategy combined with differential operator, K-means clustering and external document updating the centers of clusters is used to dynamically improve distribution diversity of population in constrained region. In the second stage, a new fruit fly operator is designed to improve local search ability in constrained region. The experimental results show that ToPFOA is superior to ToPDE and ToPDEEDA in terms of solution quality and stability.
作者
周佳伟
杜欣
倪友聪
张虎
张昊
倪皓然
王峰
Zhou Jiawei;Du Xin;Ni Youcong;Zhang Hu;Zhang Hao;Ni Haoran;Wang Feng(College of Computer and Cyber Security,Fujian Normal University,Fuzhou 350117,China;Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory,Beijing Electro-mechanical Engineering Institute,Beijing 100074,China;School of Photonic and Electronic Engineering,Fujian Normal University,Fuzhou 350117,China;School of Computer Science,Wuhan University,Wuhan 430072,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2023年第1期110-122,共13页
Journal of System Simulation
基金
国家自然科学基金(62172097)
福建省自然科学基金(2020J01165)。
关键词
均匀实验设计
约束区域
演化算法
果蝇优化算法
差分算子
uniform experimental design
constrained region
evolutionary algorithm
fruit fly optimization algorithm
differential operator
