Double Search Mode Firefly Algorithm Based on Dynamic Reverse Learning and Levy Flight

Aiming at the problems of poor convergence and insufficient distribution of multi-objective firefly algorithm in solving complex multi-objective problems, we propose a double search mode firefly algorithm based on dynamic reverse learning and levy flight (MOFA-LR). By comparing the fitness values of any firefly with other fireflies in the population, the algorithm judges the dominance relationship between them, and selects different search modes according to different dominance relationships. When fireflies are dominant, we should pay attention to the quality on the pareto frontier solution near. By dynamic reverse learning strategies, the reverse of the current individual solution using reverse solution combining the global optimal solution to guide the firefly mobile search mode, better to explore potential solutions, make the fireflies best possible to favourable