Artificial Neural Networks Based Optimization Techniques: A Review

In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more.
• 2 The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created.
• 3 Input parameters are initialized within the specified range, and they can provide optimal solutions.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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