A multiobjective optimization based framework to balance the global exploration and local exploitation in expensive optimization

Zhiwei Feng, Qingbin Zhang (), Qingfu Zhang, Qiangang Tang, Tao Yang and Yang Ma

Journal of Global Optimization, 2015, vol. 61, issue 4, 677-694

Abstract: 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. Copyright Springer Science+Business Media New York 2015

Keywords: Expensive optimization; Gaussian stochastic processes; Efficient global optimization; Multiobjective optimization; MOEA/D (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10898-014-0210-2

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