A Kriging-assisted multiobjective evolutionary algorithm

作者：Venturelli Giovanni; Benini Ernesto^*; Laniewski Wollk Lukasz

来源：Applied Soft Computing, 2017, 58: 155-175.

DOI：10.1016/j.asoc.2017.04.017

• Soft-computing
• Evolutionary computation
• Multiobjective optimization
• Surrogates
• Metamodels
• Kriging

A surrogate-assisted (SA) evolutionary algorithm for Multiobjective Optimization Problems (MOOPs) is presented as a contribution to Soft Computing (SC) in Artificial Intelligence (AI). Such algorithm is grounded on the cooperation between a pure evolutionary algorithm and a Kriging based algorithm featuring the Expected Hyper-Volume Improvement (EHVI) metric. Comparison with state-of-art pure and Kriging-assisted algorithms over two- and three-objective test functions have demonstrated that the proposed algorithm can achieve high performance in the approximation of the Pareto-optimal front mitigating the drawbacks of its parent algorithms.

• 出版日期2017-9