A kriging-assisted bi-objective constrained global optimization algorithm for expensive constrained optimization problems

Computationally expensive constrained optimization problems are challenging owing to their high complexity and computational cost. To solve these problems efficiently, a kriging-assisted bi-objective constrained global optimization (BOCGO) algorithm is developed, where three phases with three bi-objective subproblems are performed. In phase I, the constraints are searched locally and globally to find the feasible region. Once a feasible region has been located, the two terms of the constrained expected improvement function are utilized to exploit and explore the feasible region in phase II. As the kriging models are accurate enough in the concerned region, a local search is processed to improve the optimal solution in phase III. The capability of the BOCGO algorithm is demonstrated by comparison with two classical and two state-of-the-art algorithms on 20 problems and an engineering simulation problem. The results show that the BOCGO algorithm performs better in more than three-fifths of problems, illustrating its effectiveness and robustness.

Automated summary

In this paper, a kriging-assisted bi-objective constrained global optimization (BOCGO) algorithm is proposed to solve computationally expensive constrained optimization problems. The algorithm consists of three phases and three bi-objective subproblems. Experimental results show that the BOCGO algorithm outperforms other algorithms in the majority of problems, demonstrating its effectiveness and robustness.