Practicable Strategy of High-Dimensional Multi-objective Coevolution

With the rapid development of social economy, people’s demand for diversified and precise goals is increasingly prominent. In the face of a specific engineering application practice, how to find a satisfactory equilibrium solution among multiple objectives has been the focus of researchers at home and abroad. Aiming at the convergence and diversity imbalance in the current high-dimensional multi-objective evolutionary algorithm based on reference points, this paper suggests a constrained evolutionary algorithm based on spatial division, angle culling and hybrid matching selection strategy. Experimental practices show that the proposed algorithm has better performance compared with other related variants on DTLZ/WFG benchmark functions and in solving the problem of electricity market price.