Abstract Two evolutionary models, individual based evolutionary model (IND) and population based evolutionary model (POP) are proposed. Based on these two models, two kinds of multi-objective evolutionary algorithms (LHS) are designed based on Latin hypercube sampling, namely LHS-MOEAs. In LHS-MOEAs, the LHS local search is designed for exploiting promising areas and the evolutionary operator is designed for exploring new searching areas in feasible space. The combination of LHS local search and evolutionary operator in LHS-MOEA can prevent degeneration effectively. Experimental results demonstrate that the proposed LHS-MOEAs performs better and it is more preponderant than the classical NSGA-II in solving CPS_MOPs.
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