Multi-Strategy Improved Seagull Optimization Algorithm for Solving Robot Path Planning
To solve the problems of slow solving efficiency,local stagnation and low searching accuracy in the path planning of mobile music robot,a multi-strategy improved seagull optimization algorithm(MSOA)was proposed.Firstly,the algorithm introduced the Circle chaotic mapping to initialize the population and en-sured the optimization efficiency.Secondly,the multi-direction somersault and somersault jump spiral search strategies are proposed,which are embedded into the migration behavior and predation behavior of the algo-rithm respectively,to improve the algorithm's optimization ability.Finally,the hybrid wave nonlinear collision control factor is introduced to dynamically balance the local search and global development capabilities of the algorithm.Experimental results show that MSOA algorithm is superior to other algorithms on test function and path planning problem.For the path planning problem of a robot,the proposed algorithm is suitable to solve the path planning problem of a mobile robot due to its superior performance,feasibility and stability,its ability to quickly and accurately avoid obstacles in complex environments,and its minimal path length.
path planningseagull optimization algorithmmulti-direction somersaultjump spiralhybrid wave control factor
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