To solve the issues of inadequate productivity and sluggish optimization velocity in wheeled robot path planning,an improved snake optimizer(ISO)was proposed.In the initial stage,sine chaotic mapping expansion algorithm is introduced to optimize the space and improve the quality of the solution.A bidirectional search strategy is devised to approximate the global optimal value simultaneously in the two directions led by the best and the worst individual,which makes the convergence speed faster.The improved evolutionary population dynamic mechanism is added to the algorithm to replace the poor quality individuals so as to improve the population quality.In addition,utilizing the elite opposition-based learning strategy is used to improve the local development ability of the algorithm.The simulation results show that the ISO algorithm performs better in various indicators and has higher optimization efficiency compared to other comparative algorithms in wheeled robot path planning process and can effectively help the wheeled robot to complete the planning task.
维普
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