Study comparison between firefly algorithm and particle swarm optimization for SLAM problems

Abstract

Simultaneous localization and mapping abbreviated in SLAM is a procedure used to elaborate autonomous mobile robots that can construct a map of an unknown surroundings and simultaneously use that map to calculate its own position. There is many implementations of methods for solving the SLAM problem. In this context, Firefly Algorithm is inspired by fireflies behavior in nature and is one of the latest models same as PSO that is the abbreviation for Particle Swarm Optimization which is inspired by bird flocking or fish schooling and described as population depend on optimization process. We conducted a sequence of experiments using each algorithm and the results of those experiements were evaluated and compared to find the best solutions.