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