摘要
An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.
An improved adaptive particle swarm optimization （IAPSO） algorithm is presented for solving the mini- mum makespan problem of job shop scheduling problem （JSP）. Inspired by hormone modulation mechanism, an adaptive hormonal factor （HF）, composed of an adaptive local hormonal factor （ Hi ） and an adaptive global hor- monal factor （H~）, is devised to strengthen the information connection between particles. Using HF, each particle of the swarm can adjust its position self adaptively to avoid premature phenomena and reach better solution. The computational results validate the effectiveness and stability of the proposed IAPSO, which can not only find opti- mal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization （PSO） algorithms.
基金
Supported by the National Natural Science Foundation of China(51175262)
the Research Fund for Doctoral Program of Higher Education of China(20093218110020)
the Jiangsu Province Science Foundation for Excellent Youths(BK201210111)
the Jiangsu Province Industry-Academy-Research Grant(BY201220116)
the Innovative and Excellent Foundation for Doctoral Dissertation of Nanjing University of Aeronautics and Astronautics(BCXJ10-09)
作者简介
Tang Dunbing, Professor, E-mail： d. tang@nuaa. edu. cn.
