Using Neural Networks and Diversifying Differential Evolution for Dynamic Optimisation

TL;DR

This paper evaluates the effectiveness of neural networks combined with diversity mechanisms in differential evolution for dynamic optimization problems, emphasizing the importance of diversity and timing in improving results.

Contribution

It investigates the competitiveness of neural networks versus simple diversity mechanisms and explores their integration to enhance dynamic optimization performance.

Findings

Neural networks can improve results when integrated with diversity mechanisms.

The effectiveness depends on change type and frequency.

Proper population diversity is crucial for neural network success.

Abstract

Dynamic optimisation occurs in a variety of real-world problems. To tackle these problems, evolutionary algorithms have been extensively used due to their effectiveness and minimum design effort. However, for dynamic problems, extra mechanisms are required on top of standard evolutionary algorithms. Among them, diversity mechanisms have proven to be competitive in handling dynamism, and recently, the use of neural networks have become popular for this purpose. Considering the complexity of using neural networks in the process compared to simple diversity mechanisms, we investigate whether they are competitive and the possibility of integrating them to improve the results. However, for a fair comparison, we need to consider the same time budget for each algorithm. Thus, instead of the usual number of fitness evaluations as the measure for the available time between changes, we use wall clock timing. The results show the significance of the improvement when integrating the neural network and diversity mechanisms depends on the type and the frequency of changes. Moreover, we observe that for differential evolution, having a proper diversity in population when using neural networks plays a key role in the neural network's ability to improve the results.