A co-evolutionary teaching-learning-based optimization algorithm for stochastic RCPSP

A co-evolutionary teaching-learning-based optimization (CTLBO) algorithm is proposed in this paper to solve the stochastic resource-constrained project scheduling problem (SRCPSP). The activity list is used for encoding, and resource-based policies are used for decoding. Also, a new competition phase is developed to select the best solution of each class as the teacher. To make two classes evolve cooperatively, both the teacher phase and student phase of the TLBO are modified. Moreover, Taguchi method of design of experiments is used to investigate the effect of parameter setting. Computational results are provided based on the well-known PSPLIB with certain probability distributions. The comparisons between the CTLBO and some state-of-the-art algorithms are provided. It shows that the CTLBO is more effective in solving the problems with medium to large variance.

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