A surrogate-assisted evolutionary algorithm based on inverse distance weighting

Abstract

Solutions to many real-life optimization problems take a long time to evaluate. This limits the number of solutions we can evaluate. When optimizing with an Evolutionary Algorithm (EA) a frequently used approach is to approximate the objective using a surrogate function, replacing the time-consuming real evaluation. This surrogate model is combined with a so-called acquisition function, to select promising candidate solutions. The acquisition function balances the trade-off between exploration of parameter space and the exploitation of the surrogate. These candidates are subject to an expensive evaluation with the true objective function and are used to update the surrogate model. Iteratively applying this process can effectively optimize global optimization problems. In this work, we propose a new multi-objective optimization algorithm with inverse distance weighting as surrogate function, which we call IDW-SAEA (inverse distance weighting surrogate assisted evolutionary algorithm). We introduce a new objective to the optimization problem to improve exploration and reduce the complexity of the acquisition function. We show this algorithm is competitive with state-of-the-art kriging-based surrogate-assisted EAs on certain benchmark problems. Additionally, we use the algorithm to optimize a practical problem: a Finite Element Method simulation of the cervix region with applications in radiotherapy for cervical cancer.