Salp Swarm Algorithm (SSA) for Training Feed-Forward Neural Networks

Artificial neural networks (ANNs) have shown efficient results in statistics and computer science applications. Feed-forward neural network (FNN) is the most popular and simplest neural network architecture, capable of solving nonlinearity. In this paper, feed-forward neural networks’ weight and bias figuring using a newly proposed metaheuristic Salp Swarm Algorithm (SSA) are proposed. SSA is a swarm-based metaheuristic inspired by the navigating and foraging behaviour of salp swarm. The performance is evaluated for some of the benchmarked datasets and compared with some well-known metaheuristics.

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