SSO and Soft Computing

In the past decade, there is an increasing interest in Soft Computing motivated approaches for finding optimal or good quality solutions to larger problems, including neural networks, GAs, tabu searches, simulated annealing, ant colony optimations, PSOs, and SSOs. Among these methods, SSO was the most recently proposed. It was originally designed by Yeh in 2009 and called the discrete PSO to overcome the drawback of PSO in a discrete problem initially. SSO has some appealing features, including easy implementation, having just a few parameters to tune, and a fast convergence rate. The SSO, a population-based stochastic optimization technique, belongs to the category of swarm intelligence methods; it is also an evolutionary computational method inspired by particle swarm optimization (PSO), which is a powerful swarm intelligence and evolutionary algorithm proposed for solving discrete optimization problems.. The SSO has been proved to be a more efficient method than PSO, GA, and ABC in some applications.