Path Planning for Mobile Robot Based on Improved Artificial Gorilla Troops Optimization
Aiming at the problems of weak global optimization ability in the early stage,weak convergence ability in the later stage,and easy to fall into local optima in the path planning problem of mobile robots using traditional artificial gorilla troop optimization algorithms,an im-proved artificial gorilla troop optimization algorithm is proposed.In the improved algorithm,to improve the quality of the initial population,lo-gistic chaotic mapping is used to generate the population;Introduce new calculation formulas to improve the values of control parameters,mak-ing them linearly increase with the number of iterations;Integrating the position update strategy of the Osprey Optimization Algorithm to en-hance information exchange between individuals in the algorithm;In the later stage of algorithm development,the Levi flight strategy is ap-plied to update individual positions to ensure population diversity in the later stage of the algorithm.The simulation experiment results show that compared with SSA algorithm,GTO algorithm,and GWO algorithm,the improved algorithm reduces the average path obtained in M1 map environment by 9.72%,6.07%,and 7.99%,respectively;The average path obtained in the M2 map environment has been shortened by 22.04%,44.16%,and 50.3%,respectively,showing significant advantages.
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