# Metaheuristic Design of Feedforward Neural Networks: A Review of Two Decades of Research

## TL;DR

This review paper summarizes two decades of research on optimizing feedforward neural networks, highlighting traditional and metaheuristic methods, and discusses emerging directions like deep learning and quantum neural networks.

## Contribution

It provides a comprehensive overview of FNN optimization techniques, connecting various research trends and identifying future challenges in the field.

## Key findings

- Metaheuristic algorithms are increasingly used for FNN optimization.
- Traditional gradient-based methods have limitations leading to exploration of alternative approaches.
- Emerging research directions include deep learning and quantum neural networks.

## Abstract

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.