Comparing the Performance of Genetic Algorithm and Ant Colony Optimization Algorithm for Mobile Robot Path Planning in the Dynamic Environments with Different Complexities

Abstract

In this paper, genetic algorithm and ant colony optimization algorithm are used for route moving robot in dynamic environments with various complexities. Both algorithms work with global routing and need a general map from environment. Since environments are dynamic and different paths have variable length, chromosome structure with variable length is employed. In this study the performance of both algorithms in the execution speed and the number of occurrences for obtaining the optimal path in various dynamic environments has been evaluated using MATLAB simulation methods. Obtained results from comparing the performance of these two algorithms by considering performance and adjusting the parameters and their advantages beside limitations, developed optimization algorithms for route moving robots.