Tumbleweed Optimization Algorithm and Its Application in Vehicle Path Planning in Smart City

Abstract

With the increasing complexity of optimization problems, the requirements for algorithm optimization capabilities are getting higher and higher. In order to better solve complex optimization problems, this paper proposes a new swarm intelligence optimization algorithm named Tumbleweed Optimization Algorithm (TOA). The TOA algorithm consists of two stages, which simulate the seedling growth phase and seed propagation phase of tumbleweed respectively. The TOA algorithm adopts a multi-group structure to improve the global searching ability of the algorithm. In order to verify the performance of the TOA algorithm in numerical optimization and solving practical application problems, this paper selects the CEC2013 benchmark function library and the vehicle path planning in the smart city for testing. Through the comparison of experimental results, the TOA algorithm can both show strong optimization capabilities. Compared with the other ten intelligent optimization algorithms, the TOA algorithm proposed in this paper can also show strong competitiveness.