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- Title
Hybridizing Particle Swarm Optimization and Differential Evolution for the Mobile Robot Global Path Planning.
- Authors
Biwei Tang; Zhanxia Zhu; Jianjun Luo
- Abstract
Global path planning is a challenging issue in the filed of mobile robotics due to its complexity and the nature of nondeterministic polynomial-time hard (NP-hard). Particle swarm optimization (PSO) has gained increasing popularity in global path planning due to its simplicity and high convergence speed. However, since the basic PSO has difficulties balancing exploration and exploitation, and suffers from stagnation, its efficiency in solving global path planning may be restricted. Aiming at overcoming these drawbacks and solving the global path planning problem efficiently, this paper proposes a hybrid PSO algorithm that hybridizes PSO and differential evolution (DE) algorithms. To dynamically adjust the exploration and exploitation abilities of the hybrid PSO, a novel PSO, the nonlinear timevarying PSO (NTVPSO), is proposed for updating the velocities and positions of particles in the hybrid PSO. In an attempt to avoid stagnation, a modified DE, the rankingbased self-adaptive DE (RBSADE), is developed to evolve the personal best experience of particles in the hybrid PSO. The proposed algorithm is compared with four state-ofthe- art evolutionary algorithms. Simulation results show that the proposed algorithm is highly competitive in terms of path optimality and can be considered as a vital alternative for solving global path planning.
- Subjects
DIFFERENTIAL evolution; ROBOTIC path planning; MOBILE robots; PARTICLE swarm optimization; PROBLEM solving; COMPUTER simulation
- Publication
International Journal of Advanced Robotic Systems, 2016, Vol 13, Issue 3, p1
- ISSN
1729-8806
- Publication type
Article
- DOI
10.5772/63812