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The input parameters are consistent with the input parameters of the proposed algo- rithm; refer to the input parameters of the proposed algorithm below. The initialization is the same as the proposed algorithm; refer to the proposed algorithm below. g te x i λ i , z is the decomposition approach. There are many decomposition approaches; among them, the boundary intersection approach, Tchebycheff approach, and weighted sum approach are three popular decomposition approaches. The basic idea of TLBO is to simulate the learning process of the class, which is completed in two phases: the teaching phase and the learning phase. Each individual learns from the teacher (the best individual is selected as the teacher) in the teaching phase. Individuals learn from each other (an individual is randomly selected) in the learning phase. The TLBO is originally designed to solve the continuous optimization problem because the formulas used to update individuals in the teaching and learning phase are continuous variable oriented. Some changes should be made when the scheduling problem is solved by the TLBO. This study modifies the TLBO by replacing the update formula by the discrete crossover operation. According to the idea of MOEA/D and TLBO, this study proposes a hybrid method (MOEA/DTL). The Figure 3 shows the flow chart of MOEA/DTL. The details of the proposed MOEA/DTL are shown in Algorithm 1.
Figure3. The flow chart of Multi-Objective Evolutionary Algorithm based on Decomposition and Teaching–Learning-Based Optimization (MOEA/DTL).
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