A Surrogate-Assisted Constrained Optimization Evolutionary Algorithm by Searching Multiple Kinds of Global and Local Regions
Yong Zeng, Yuansheng Cheng, Jun Liu
Abstract
This paper proposes a surrogate-assisted evolutionary algorithm to tackle expensive inequality-constrained optimization problems through global exploration and local exploitation. The algorithm begins with an exploration stage that involves sampling in three kinds of global regions: the feasible region, the better-objective region, and the converging region. Specifically, sampling in the uncertain feasible region mitigates issues caused by inaccurate objective surrogates. In addition, sampling in the uncertain region containing better objective values than the current best feasible solution reduces the risk of missing the global optimum due to inaccurate constraint surrogates. Moreover, sampling in the converging region facilitates quick convergence to the global feasible optimum. Following the exploration stage, promising feasible and infeasible solutions are further refined using local surrogate-based search strategies. To address the risk of missing the global optimum resulting from limited local region scope, the regions are adaptively extended if predicted infill points lie on the boundary. If an infill point is determined to showcase a better objective value after accurate evaluation, a rewarding local search is performed within the local region. This exploration-exploitation process iterates until the computation budget is exhausted. Experimental results demonstrate that the proposed algorithm outperforms the selected state-of-the-art algorithms on the majority of tested problems.