Title: A chaotic genetic algorithm with polynomial mutation for warehouse robot path planning

Authors: Ling Lin; Jiangtao Xu; Weijun Gao; Xianjie Peng; Chengming Yang

Addresses: School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610031, China ' Hangzhou CITIC Senior Living CORP, Zhejiang, Hangzhou, 310002, China ' Faculty of Mechanical Engineering, RWTH Aachen, Aachen, 52062, Germany ' Department of Human Resources, Tongji University, Shanghai, 200092, China ' Institute of Education, Tsinghua University, Beijing, 100084, China

Abstract: To plan an efficient picking path for warehouse robots, a chaotic genetic algorithm with polynomial mutation is recommended in this paper. First, in order to improve the efficiency of the genetic algorithm, the chaotic theory is employed to design a population initialisation strategy, which can increase the diversity of the initial population. Second, on the basis of the mutation operator designed based on polynomial mutation, the algorithm's capacity for diversity preservation can be enhanced. Third, two novel adaptive adjustments are presented for crossover and mutation operations in order to achieve a balance between convergence and diversity. As assessment indices of the fitness function, the path length, turn timings, and running energy consumption of the robot are taken into considerations. Simulation results indicate that the suggested approach outperforms the basic genetic algorithm and the ant colony optimisation algorithm in terms of path length and energy consumption.

Keywords: warehouse robots; genetic algorithm; diversity; energy consumption; convergence; adaptive crossover; adaptive mutation; population initialisation.

DOI: 10.1504/IJHPSA.2023.139893

International Journal of High Performance Systems Architecture, 2023 Vol.11 No.4, pp.191 - 197

Received: 16 Nov 2022
Accepted: 10 Apr 2023
Published online: 09 Jul 2024 *

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