Abstract
Broadly, swarm intelligence (SI) algorithms are considered as nature-inspired techniques improved depending on the idea of communications between living entities such as birds' flocks, Ant Colony, and fish, which means deliberates the group behavior evolving through self-organizing of population individuals. SI has been stimulated via the surveillance of group behavior in its populations in nature because their behavior appears to have the ability to solve complex tasks and optimization problems. The fitness function which is based on SI has been improved to solve combinatorial and mathematical optimization problems by using these algorithms. This means, these techniques work based on the behaviors of individuals in their population so the observation, swam algorithms can be employed for solving the different problems in various applications such as in the medical systems or to enhance the performance of other application systems. In this paper, some swarm intelligent methodologies are reviewed and concerns with their applications in some areas are mentioned.
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