Research on the Application of Improved GSA Algorithm in Robot Path Planning
To address the problems that the universal gravitational search algorithm(GSA)is low conver-gence accuracy and easy to fall into local optimum,this paper proposed an improved gravity search algo-rithm(IGSA).Firstly,in order to enhance the ability of the algorithm to escape from local optimum,the improved algorithm used the current iteration number t that is introduced to dynamically adjust the gravita-tional constant.Secondly,in order to preserve the particle diversity and improve the convergence accuracy of the algorithm,the improved algorithm based on based on the boundary value to improve the particle crossing processing strategy.Meanwhile,the improved algorithm used the mid-pipeline algorithm to update the position of the free particles to accelerate the convergence of the free particles.To adapt to the afore-mentioned strategy,the improved algorithm used the adaptive weight factor to update particle position strat-egy to improve the convergence speed.And the experimental results of the algorithm on 10 benchmark test functions show that the improved algorithm has greater advantages in terms of stability,convergence speed and accuracy.Finally,the improved algorithm is applied to robot path planning and compared with other in-telligent bionic algorithm path planning through simulation experiments.The results show that the improved algorithm has shorter paths,fewer inflection points and higher search efficiency.
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