Transactions of Nanjing University of Aeronautics & Astronautics
Solving Job Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm
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Abstract:
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 (Hl) 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.
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Project Supported:
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).
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