MPSO and MGA Approaches for Mobile Robot Navigation
A Comparative Study
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In this book, Modified Genetic Algorithm (MGA) and Modified Particle Swarm Optimization (MPSO) are developed to increase the capability of these optimization algorithms for a global path planning. Despite the fact that (GA) has rapid search and high search quality, infeasible paths and high computational cost problems are exist associated with this algorithm. To address these problems, the MGA is presented. Improvements presented in MPSO are mainly trying to address the problem of premature convergence associated with the original PSO. In the MPSO an error factor is modelled to ensure that the...
In this book, Modified Genetic Algorithm (MGA) and Modified Particle Swarm Optimization (MPSO) are developed to increase the capability of these optimization algorithms for a global path planning. Despite the fact that (GA) has rapid search and high search quality, infeasible paths and high computational cost problems are exist associated with this algorithm. To address these problems, the MGA is presented. Improvements presented in MPSO are mainly trying to address the problem of premature convergence associated with the original PSO. In the MPSO an error factor is modelled to ensure that the PSO converges. A modified procedure is carrying out in the MPSO to solve the infeasible path problem. According to the simulation results using Matlab version R2012 (m-file), both algorithms (MGA and MPSO) are tested in different environments and the results are compared with previous researches. The results demonstrate that these two algorithms have a great potential to solve mobile robot path planning with satisfactory results in terms of minimizing distance and execution time.
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