Abstract
In surrogate-assisted multi-/many-objective evolutionary optimization, each solution normally has an approximated value on each objective, resulting in increased difficulties in selecting solutions for expensive objective evaluations due to complicated trade-off between different objectives and accumulated uncertainty in the approximation of the objective functions. Thus, it is highly challenging to design an efficient model management strategy for surrogate-assisted expensive multi-/many-objective optimization. In this paper, a surrogate model is built for each objective function, based on which a set of promising candidate solutions are found. Additionally, a Gaussian process model is constructed to approximate a newly designed performance indicator measuring both convergence and diversity properties of individual solutions. Finally, the solution of the found candidate solutions having the maximum expected improvement in terms of the performance indicator is selected for evaluation using the expensive objective functions. Comparative experiments are conducted on 3-, 5-, and 10-objective DTLZ, WFG, and MaF test functions, as well as two real-world applications. The experimental results show that the proposed method is competitive compared to five state-of-the-art surrogate-assisted evolutionary algorithms for expensive multi-/many-objective optimization.
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