一种面向多模态优化的新型群体智能优化方法:羊群迁徙优化算法  

Novel swarm intelligence method for multimodal optimization:Sheep flock migrate optimization algorithm

作　　者：海星朔 张文良 冯强[1,3] 王自力[1,3] HAI Xingshuo;ZHANG Wenliang;FENG Qiang;WANG Zili(Institute of Reliability Engineering,Beihang University,Beijing 100191,China;School of Electrical and Electronic Engineering,Singapore 639798,Singapore;School of Reliability and Systems Engineering,Beihang University,Beijing 100191,China)

机构地区：[1]北京航空航天大学可靠性工程研究所,北京100191 [2]南洋理工大学电气与电子工程学院,新加坡639798 [3]北京航空航天大学可靠性与系统工程学院,北京100191

出　　处：《工程科学学报》2023年第12期2070-2084,共15页Chinese Journal of Engineering

基　　金：国家重点研发计划资助项目(2019YFE0105100)。

摘　　要：群体智能优化算法是根据生物集群运动、交互、进化等行为机制而开发的自然启发算法,凭借其显著的灵活性、适应性、鲁棒性以及全局寻优能力,被广泛应用于现实世界中各类优化问题的求解.受羊群间歇性集体运动现象启发,本文提出了一种新的仿生群体智能优化方法—羊群迁徙优化(Sheep flock migrate optimization,SFMO)算法,创新性地建立了3个核心运算模块,即放牧算子、集体运动算子和补偿策略.与现有的群体智能优化算法相比,SFMO可以通过广泛随机搜索指导下的种群迁徙,降低算法陷于局部最优的概率,为群体智能优化领域提供了一种新的解决方案.收敛性证明和复杂度分析进一步为SFMO提供了理论支撑.以CEC-2017基准函数为基础的数值仿真验证表明:SFMO能够有效解决函数优化问题,并在多模态函数优化问题中具有显著优势.Swarm intelligence optimization algorithms have garnered considerable attention for solving real-world optimization problems owing to their ability to emulate collective behaviors such as the movement,interaction,and evolution observed in biological swarms.In this paper,we propose a novel bionic swarm intelligence optimization method called the sheep flock migrate optimization(SFMO)algorithm,which is inspired by the intermittent collective motion behavior exhibited by sheep.The SFMO algorithm comprises grazing operator,collective motion operator,and compensation strategy.The grazing operator is formulated based on mathematical models that capture the local foraging behavior of sheep within a confined range.This operator is inspired by the“two-phase motion of sheep”,as well as the widely recognized“green wave chasing”mechanism observed in herbivores.The grazing operator,which is responsible for the local search functionality,enhances the algorithm’s exploitation capability,thereby enhancing its ability to effectively exploit the search space.The collective motion operator builds upon the“two-phase motion mechanism”and incorporates the“leader-follower”mechanism observed during the movement of a sheep flock.By simulating the overall migration behavior of a sheep flock,this operator assumes the role of global search and aims to enhance the algorithm’s exploration ability.The compensation strategy temporarily expands the search range by leveraging the social learning mechanism observed in flock behavior,thereby improving the algorithm’s ability to escape from local optima.Distinguished from the existing swarm intelligence-based optimization methods,the SFMO algorithm alternately executes the grazing operator and collective motion operator,mirroring the intermittent collective motion mechanism exhibited by flocks.The compensation mechanism is adaptively triggered when the algorithm is likely to converge to a local optimal solution,ensuring a balance between the exploration and exploitation capabil

关 键 词：群体智能 间歇性集体运动 羊群迁徙优化 局部最优 多模态函数优化 

分 类 号：TG142.71[一般工业技术—材料科学与工程]