Multi-Strategy Discrete Teaching–Learning-Based Optimization Algorithm to Solve No-Wait Flow-Shop-Scheduling Problem

To address the problems of single evolutionary approach, decreasing diversity, inhomogeneity, and meaningfulness in destruction process when teaching–learning-based optimization (TLBO) algorithm solves no-wait flow-shop-scheduling problem, multi-strategy discrete (MSDTLBO) is introduced. Considering differences between individuals, redefined from student’s point view, giving basic integer sequence encoding. fact that prone to falling into local optimum leading a reduction search accuracy, population was divided three groups according learning ability different teaching strategies were adopted achieve effect their needs. improve destruction-and-reconstruction with symmetry, an iterative greedy destruction–reconstruction used as main body, knowledge base control number meaningless artifacts be destroyed dynamically change artifact-selection method process. Finally, applied problem (NWFSP) test its practical application value. After comparing twenty-one benchmark functions six algorithms, experimental results showed has certain effectiveness solving NWFSP.