Expensive many-objective evolutionary optimization guided by two individual infill criteria

Recently, surrogate-assisted multi-objective evolutionary algorithms have achieved much attention for solving computationally expensive multi-/many-objective optimization problems. An effective infill sampling strategy is critical in surrogate-assisted multi-objective evolutionary optimization to assist evolutionary algorithms in identifying the optimal non-dominated solutions. This paper proposes a Kriging-assisted many-objective optimization algorithm guided by two infill sampling criteria to self-adaptively select two new solutions for expensive objective function evaluations to improve history models. The first uncertainty-based criterion selects the solution for expensive function evaluations with the maximum approximation uncertainty to improve the chance of discovering the optimal region. The approximation uncertainty of a solution is the weighted sum of approximation uncertainties on all objectives. The other indicator-based criterion selects the solution with the best indicator value to accelerate exploiting the non-dominated optimal solutions. The indicator of an individual is defined by the convergence-based and crowding-based distances in the objective space. Finally, two multi-objective test suites, DTLZ and MaF, and three real-world applications are applied to test the performance of the proposed method and four compared classical surrogate-assisted multi-objective evolutionary algorithms. The results show that the proposed algorithm is more competitive on most optimization problems.

Automated summary

This paper proposes a Kriging-assisted many-objective optimization algorithm that uses two infill sampling criteria to adaptively select new solutions for objective function evaluations, improving historical models. The proposed algorithm shows competitive performance on most optimization problems compared to four classical surrogate-assisted multi-objective evolutionary algorithms.