Exploring the effectiveness of surrogate-assisted evolutionary algorithms on the batch processing problem

TL;DR

This paper evaluates how surrogate models can enhance evolutionary algorithms like GA and DE in solving a batch processing optimization problem, showing potential improvements but also highlighting the importance of hyper-parameter tuning.

Contribution

It introduces a surrogate-assisted framework for evolutionary algorithms applied to batch processing, demonstrating its impact on solution quality across different scenarios.

Findings

Surrogate-assisted algorithms can improve solution quality in some cases.

Hyper-parameter tuning is crucial for surrogate-assisted methods.

Surrogates may cause deterioration if not properly tuned.

Abstract

Real-world optimisation problems typically have objective functions which cannot be expressed analytically. These optimisation problems are evaluated through expensive physical experiments or simulations. Cheap approximations of the objective function can reduce the computational requirements for solving these expensive optimisation problems. These cheap approximations may be machine learning or statistical models and are known as surrogate models. This paper introduces a simulation of a well-known batch processing problem in the literature. Evolutionary algorithms such as Genetic Algorithm (GA), Differential Evolution (DE) are used to find the optimal schedule for the simulation. We then compare the quality of solutions obtained by the surrogate-assisted versions of the algorithms against the baseline algorithms. Surrogate-assistance is achieved through Probablistic Surrogate-Assisted Framework (PSAF). The results highlight the potential for improving baseline evolutionary algorithms through surrogates. For different time horizons, the solutions are evaluated with respect to several quality indicators. It is shown that the PSAF assisted GA (PSAF-GA) and PSAF-assisted DE (PSAF-DE) provided improvement in some time horizons. In others, they either maintained the solutions or showed some deterioration. The results also highlight the need to tune the hyper-parameters used by the surrogate-assisted framework, as the surrogate, in some instances, shows some deterioration over the baseline algorithm.