Pigeon-inspired optimization:a new swarm intelligence optimizer for air robot path planning  

作　　者：Haibin Duan Peixin Qiao 

机构地区：[1]School of Automation Science and Electrical Engineering,Beihang University(Beijing University of Aeronautics and Astronautics,BUAA),Beijing,P.R.China

出　　处：《International Journal of Intelligent Computing and Cybernetics》2014年第1期24-37,共14页智能计算与控制论国际期刊（英文）

基　　金：Natural Science Foundation of China(NSFC)under grant no.61333004,no.61273054 and no.60975072;National Key Basic Research Program of China(973 Project)under grant no.2014CB046401;Top-Notch Young Talents Program of China,and Aeronautical Foundation of China under grant no.20135851042.

摘　　要：Purpose–The purpose of this paper is to present a novel swarm intelligence optimizer—pigeoninspired optimization(PIO)—and describe how this algorithm was applied to solve air robot path planning problems.Design/methodology/approach–The formulation of threat resources and objective function in air robot path planning is given.The mathematical model and detailed implementation process of PIO is presented.Comparative experiments with standard differential evolution(DE)algorithm are also conducted.Findings–The feasibility,effectiveness and robustness of the proposed PIO algorithm are shown by a series of comparative experiments with standard DE algorithm.The computational results also show that the proposed PIO algorithm can effectively improve the convergence speed,and the superiority of global search is also verified in various cases.Originality/value–In this paper,the authors first presented a PIO algorithm.In this newly presented algorithm,map and compass operator model is presented based on magnetic field and sun,while landmark operator model is designed based on landmarks.The authors also applied this newly proposed PIO algorithm for solving air robot path planning problems.

关 键 词：Evolutionary computation ROBOTICS 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]