Metaheuristic Algorithms for Convolution Neural Network

TL;DR

This paper explores the use of three metaheuristic algorithms—simulated annealing, differential evolution, and harmony search—to optimize convolutional neural networks, improving accuracy on MNIST and CIFAR datasets despite increased computation time.

Contribution

It introduces implementation strategies for metaheuristics in CNN optimization and compares their effectiveness against standard CNN models.

Findings

Metaheuristics improved CNN accuracy by up to 7.14%.

The methods increased computation time but enhanced performance.

Differential evolution showed the most promising results among the three.

Abstract

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).