Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems

By Handing Wang, Yaochu Jin, John James Doherty. 2017

356 citations recorded by the source corpus.

Function evaluations (FEs) of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to solve these problems. To address this challenge, the research on surrogate-assisted EAs has attracted increasing attention from both academia and industry over the past decades. However, most existing surrogate-assisted EAs (SAEAs) either still require thousands of expensive FEs to obtain acceptable solutions, or are only applied to very low-dimensional problems. In this paper, a novel surrogate-assisted particle swarm optimization (PSO) inspired from committee-based active learning (CAL) is proposed. In the proposed algorithm, a global model management strategy inspired from CAL is developed, which searches for the best and most uncertain solutions according to a surrogate ensemble using a PSO algorithm and evaluates these solutions using the expensive objective function. In addition, a local surrogate model is built around the best solution obtained so far. Then, a PSO algorithm searches on the local surrogate to find its optimum and evaluates it. The evolutionary search using the global model management strategy switches to the local search once no further improvement can be observed, and vice versa. This iterative search process continues until the computational budget is exhausted. Experimental results comparing the proposed algorithm with a few state-of-the-art SAEAs on both benchmark problems up to 30 decision variables as well as an airfoil design problem demonstrate that the proposed algorithm is able to achieve better or competitive solutions with a limited budget of hundreds of exact FEs.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020, methodology): “…expensive problems, which is usually solved by surrogate-assisted evolutionary algorithms (SAEAs) [36], using cheap approximated regression and classiﬁcation models (like Gaussian process model [37], radial basis function network [38], etc.) to replace the expensive ﬁtness evaluation [39].”
Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles (2019, methodology): “Many empirical model management strategies have been developed for online data-driven EAs [5], [21], where the main idea is either to enhance the accuracy of the surrogates [22], [23], to ensure correct environmental selection [24], or to encourage exploration [12], [18], [25], [26].”
A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems (2020, background): “Since different regression models have different strengths and weaknesses, multiple surrogate models are combined as an ensemble in a single SAEA to increase its robustness [22]–[25].”
Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019, methodology): “The recently proposed online data-driven EA, committee-based active learning-based surrogate-assisted particle swarm optimization (CAL-SAPSO) was employed to solve the airfoil design problem in [49].”