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Solving job-shop scheduling problem based on an improved adaptive particle swarm optimization algorithm. (English) Zbl 1324.90073

Summary: An improved adaptive particle swarm optimization (IAPSO) algorithm is presented for solving the minimum makespan problem of job shop scheduling problem. 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 algorithms.

MSC:

90B36 Stochastic scheduling theory in operations research
90C59 Approximation methods and heuristics in mathematical programming
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
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