Research on Slotting Optimization Based on MOEA/D

Decomposition-based multi-objective optimization algorithms decompose a complex multi-objective optimization problem into several simple single-objective optimization problems using a set of different decomposition method and solve these single-objective sub-problems simultaneously. It has outstanding performance in solving multi-objective optimization problems, so this paper propose a method to optimize the slotting optimization problem using MOEA/D. However, in evolutionary algorithms, the selection of operators directly determines the performance of the algorithm, and since the population has different characteristics at different evolutionary stages, the evolutionary process is divided into five stages to verify the effects of different operators on the performance of the algorithm, so as to select a suitable operator. The improvement of the evolutionary operator selection strategy further accelerates the convergence speed without affecting the convergence accuracy. We have verified the performance of the algorithm on the slotting optimization problem, and through many comparative experiments we can find that the proposed algorithm has better performance in searching the global optimal solution and running time.