Open AccessResearch on optimization of manned robot swarm scheduling based on ant-sparrow algorithm
Author(s) -
Qingfei Gao,
Juan Zheng,
Wencai Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2078/1/012002
Subject(s) - computer science , ant colony optimization algorithms , sparrow , mathematical optimization , scheduling (production processes) , particle swarm optimization , divide and conquer algorithms , robot , algorithm , artificial intelligence , mathematics , ecology , biology
Considering the optimization problem of manned robot swarm scheduling in public environment, we constructed a demand-time-space-energy consumption scheduling model taking passenger waiting time and robot swarm energy consumption as optimization goals. This paper proposes an ant-sparrow algorithm based on the same number constraints colonies of ant and sparrow, which combines the advantages of ant colony algorithm great initial solution and the fast convergence speed of the sparrow search algorithm. After a limited number of initial iterations, the ant colony algorithm is transferred to the sparrow search algorithm. In order to increase the diversity of feasible solutions in the later stage of the ant-sparrow algorithm iteration, a divide-and-conquer strategy is introduced to divide the feasible solution sequence into the same small modules and solve them step by step. Applying it to the manned robot swarm scheduling service in the public environment, experiments show that the ant-sparrow algorithm introduced with a divide-and-conquer strategy can effectively improve the quality and convergence speed of feasible solutions.