A multiobjective optimization based framework to balance the global exploration and local exploitation in expensive optimization
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In many engineering optimization problems, objective function evaluations can be extremely computationally expensive. The effective global optimization (EGO) is a widely used approach for expensive optimization. Balance between global exploration and local exploitation is a very important issue in designing EGO-like algorithms. This paper proposes a multiobjective optimization based EGO (EGO-MO) for addressing this issue. In EGO-MO, a global surrogate model for the objective function is firstly constructed using some initial database of designs. Then, a multiobjective optimization problem (MOP) is formulated, in which two objectives measure the global exploration and local exploitation. At each generation, the multiobjective evolutionary algorithm based on decomposition is used for solving the MOP. Several solutions selected from the obtained Pareto front are evaluated. In such a way, it can generate multiple test solutions simultaneously to take the advantage of parallel computing and reduce the computational time. Numerical experiments on a suite of test problems have shown that EGO-MO outperforms EGO in terms of iteration numbers.
KeywordsExpensive optimization Gaussian stochastic processes Efficient global optimization Multiobjective optimization MOEA/D
The authors thank the associate editor for his very helpful and constructive comments, which have helped to improve the quality of this paper significantly. This research was supported by the National Natural Science Foundation of China under the Grant of 11272345 and 51375486.
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