Robot path planning based on tabu particle swarm optimization integrating cauchy mutation
TL;DR: this paper proposes a method of tabu particle swarm optimization based on Cauchy Mutation to tackle the problem of mobile robot path planning
Abstract: In view of the shortcomings of the traditional Particle Swarm Optimization (PSO) in robot path planning, such as long path planning time, slow convergence speed, insufficient search ability in the middle and late stages, and easy to fall into local optimum. Inspired by the Tabu Search (TS) algorithm and Cauchy Mutation Perturbation, this paper proposes a Tabu Particle Swarm Optimization (PSOTS) based on Cauchy Mutation. Firstly, the beta distribution random number strategy is used to adaptively adjust the inertia weight to improve the search accuracy of the algorithm in the middle and late stages. Secondly, the Cauchy mutation perturbation is introduced in the particle swarm optimization (PSO) stage to update the position of particles, so as to reduce the possibility of particles falling into the local optimum.Finally, in the middle and late stages of the search, the roulette method was used to select some particles and adopt the Tabu Search (TS)
algorithm, so as to enhance the local search ability of the particles in the middle and late stages. Through the test of CEC benchmark function, compared with the traditional particle swarm optimization (PSO) algorithm, it is proved that it has excellent performance with fewer iterations and running time, and compared with the test results of specific obstacle environments, PSOTS can effectively generate the optimal path with high smoothness and shorter length, and improve the convergence speed and stability of PSO, which proves its
superiority in solving robot path planning problems.
Submission Number: 43
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