摘要
为了平衡鸽群优化算法的探索与利用能力,本文提出了一种广义鸽群优化算法.传统的鸽群优化算法包含两种优化算子,分别为地图与指南针算子和地标算子.这两种算子依次执行,在一次算法运行中,仅执行一轮迭代.在广义鸽群优化算法中,将算法搜索分为多个阶段,每个阶段分别执行两种算子.在算法的一次运行中,两种算子执行多轮.地图与指南针算子侧重于算法的探索能力,而地标算子侧重于算法的利用能力.改进算法仅改变了两种算子的执行顺序,无需增加额外的函数值计算.此外,广义鸽群优化算法扩展了解集合结构和算子参数设置,这对于提高算法的搜索质量大有裨益.在11个单目标测试函数和8个多模态优化测试函数上进行仿真对比试验,结果表明广义鸽群优化算法提高了鸽群优化算法的搜索效率,改进了算法的搜索结果.
A generalized pigeon-inspired optimization(GPIO) algorithm for balancing the exploration and exploitation abilities is proposed herein. The traditional pigeon-inspired optimization algorithm includes two operators, namely the map and compass operator and the landmark operator. These two operators are implemented only for one round at a single run. In the GPIO algorithm, the search process is divided into multiple stages, and two operators are implemented in each stage. These two operators are implemented for multiple rounds at one single run. The map and compass operator focuses on the exploration ability, while the landmark operator focuses on the exploitation ability. The GPIO algorithm changes the execution order of the two operators without additional objective function evaluation. Moreover, the structure of the solutions and the parameter settings are extended in the GPIO algorithm, which is beneficial to search quality improvement. The simulation results show that the GPIO algorithm improves the search efficiency and the search results of the algorithm.
作者
程适
张明明
史玉回
路辉
雷秀娟
王锐
CHENG Shi;ZHANG MingMing;SHI YuHui;LU Hui;LEI XiuJuan;WANG Rui(School of Computer Science,Shaanxi Normal University,Xi'an 710119,China;Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Southern University of Science and Technology,Shenzhen 518055,China;School of Electronics and Information Engineering,Beihang University,Beijing 100191,China;College of Systems Engineering,National University of Defense Technology,Changsha 410073,China)
出处
《中国科学：技术科学》
EI
CSCD
北大核心
2023年第2期268-279,共12页
Scientia Sinica(Technologica)
基金
国家自然科学基金项目(批准号:61806119)
广东省重点实验室项目(编号:2020B121201001)
国家自然科学优秀青年基金项目(批准号:62122093)
季华实验室项目(编号:X210101UZ210)
中央高校基本科研业务费专项资金项目(编号:GK202003078)
陕西师范大学研究生创新团队项目课题(编号:TD2020014Z)资助。
关键词
群体智能
鸽群优化算法
探索与利用
多模态优化
swarm intelligence
pigeon-inspired algorithm
exploration and exploitation
multimodal optimization
