From the perspective of intelligent optimization, the results of targeted research on general types of mopop are few. Only Zhang renchong et al. [17] designed a multi-objective immune optimization algorithm (moioa) with adaptive sample allocation strategy based on MC random simulation for mopop under chance constraints. Its efficiency advantage is obvious, and its evolution ability and noise reduction effect need to be improved.