Multi-modal multi-objective evolution method with double reference vector guidance

The invention relates to the technical field of multi-modal multi-objective optimization in evolutionary computation, in particular to a multi-modal multi-objective evolutionary method guided by double reference vectors, which uses the reference vectors to divide a decision space and a target space, and combines a crowding distance value in the decision space and an APD value in the target space to obtain a multi-modal multi-objective evolutionary algorithm. The non-dominated space with higher diversity is selected and stored in an archive, so that the diversity of the decision space and the target space is improved; meanwhile, a next generation population is generated based on a special crowding distance index, so that the diversity between a decision space and a target space can be balanced; in addition, a strategy of combining local convergence quality and a special congestion distance and methods of reserving a potential boundary solution and the like are adopted for environment selection, so that the opt