Optimization of neural networks through classical metaheuristic algorithms: A review of past decade

Metaheuristic Algorithms have gained remarkable attention in the past years due to their capability of optimization. These algorithms tend to provide near-optimal solutions to complex computational problems. Nature-inspired metaheuristic algorithms take inspiration of their approach from natural processes. In this paper, we have studied 3 nature-inspired metaheuristic algorithms: Genetic Algorithm, Particle Swarm Optimization and Ant Colony Optimization. Artificial Neural Networks are adaptive models that have the capability to learn from the past mistakes and keep improving on a continuous basis. These mimic the biological neuron network in a human brain. However, there is always a need to optimize these neural networks. Many tasks such as parameter selection, data training etc. are major challenges in ANNs. In this study, we see how numerous researchers have applied metaheuristic techniques to ANNs in order to tackle these problems.