Robot Path Planning Based on Improved Seagull Optimization Algorithm
A multi-strategy fusion improved seagull optimization algorithm(MFSOA)is proposed to ad-dress the problems of low convergence accuracy and susceptibility to local optimality of the seagull optimi-zation algorithm(SOA).Tent chaotic mapping is introduced to initialize the population and increase the di-versity of the seagull population;the nonlinear convergence factor based on the current number of iterations t is dynamically adjusted to nonlinearize the linear search of the seagull optimization algorithm,enhance the speed and accuracy of the search,and avoid the algorithm falling into local optimum.The Levy flight strate-gy is introduced to enhance the global search ability of the algorithm during the seagull position update;fi-nally,the position update of the population is guided by using the golden sine mechanism to further narrow the search range and improve the local search ability of the algorithm.Six benchmark test functions are se-lected to test the performance of the algorithm,and the test results show that IMPA converges faster and has higher convergence accuracy;finally,the improved algorithm is applied to mobile robot path planning,and the simulation results show that the algorithm plans a shorter path length and higher search efficiency.
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