A Large-scale multi-objective firefly algorithm based on Reward and punishment mechanisms and Adaptive dimensional reorganization

Multi-objective optimization problems with large-scale variables are the focus and difficulty of current research in the field of multi-objective evolutionary algorithms. In order to cope with the problem of "dimensional catastrophe", this paper proposes a large-scale multi-objective firefly algorithm based on reward and punishment mechanisms and adaptive dimensional reorganization. First, a reward and punishment mechanisms is proposed to make full use of the information of the solution itself, so that the population can break the constraint and rapidly approach the Pareto frontier, effectively improving the convergence of the population; then, an adaptive dimensional reorganization mechanism is proposed to select individuals with large differences according to different iteration periods to interact and learn from each other, effectively improving the diversity of the population. In the experimental part, to verify the effectiveness of the algorithm, the UF series and the variable expanded ZDT series, 15 test problems are selected for simulation experiments and compared with 8 advanced large-scale optimization algorithms, the results show that L-MOFA-RA has the advantages of fast convergence speed and high convergence accuracy, and the comprehensive experimental results show that compared with the comparison algorithm L-MOFA-RA has better optimization performance. The comprehensive experimental results show that L-MOFA-RA has better optimization performance compared to the comparison algorithm.