A hybrid many-objective competitive swarm optimization algorithm for large-scale multirobot task allocation problem
- PDF / 1,610,229 Bytes
- 15 Pages / 595.276 x 790.866 pts Page_size
- 95 Downloads / 178 Views
ORIGINAL ARTICLE
A hybrid many‑objective competitive swarm optimization algorithm for large‑scale multirobot task allocation problem Fei Xue1 · Tingting Dong1,2 · Siqing You1 · Yan Liu1 · Hengliang Tang1 · Lei Chen1 · Xi Yang1 · Juntao Li1 Received: 16 April 2020 / Accepted: 20 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Large-scale multi-robot task allocation (MRTA) problem is an important part of intelligent logistics scheduling. And the load capacity of robot and picking station are important factors affecting the MRTA problem. In this paper, the MRTA problem is built as a many-objective optimization model with four objectives, which takes the load capacity of single robot, single picking station, all robots and all picking stations into account. To solve the model, a hybrid many-objective competitive swarm optimization (HMaCSO) algorithm is designed. The novel selection method employing two different measurement mechanisms will form the mating selection operation. Then the population will be updated by employing the competitive swarm optimization strategy. Meanwhile, the environment selection will play a role in choosing the excellent solution. To prove the superiority of our approach, there are two series of experiments are carried out. On the one hand, our approach is compared with other five famous many-objective algorithms on benchmark problem. On the other hand, the involved algorithms are applied in solving large-scale MRTA problem. Simulation results prove that the performance of our approach is superior than other algorithms. Keywords Multi-robot task allocation (MRTA) problem · Many-objective optimization · Competitive swarm optimization (CSO)
1 Introduction In e-commerce environments, the task orders have the characteristic of multi-variety, low-volume, and high-frequency, which requires an efficient picking efficiency to ensure normal task scheduling. Therefore, the traditional artificial picking mode will not be applicable to solve such problems [1]. With the development of information technology, largescale logistics robot systems are developed and have been widely applied in intelligent warehouse systems to replace human labor in recent years [2]. In general, the parallel operation mode of large-scale logistics robots can significantly improve order picking efficiency. Further, with advances in technology, the manufacturing costs of logistics robots have gradually decreased, making large-scale logistics robots a * Tingting Dong [email protected] 1
School of Information, Beijing Wuzi University, Beijing 101149, China
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2
development trend in intelligent warehouse systems. An example of this development trend is the deployment of the KIVA logistics robot by Amazon in 2012 [3, 4]. Specifically speaking, there are three important components are contained in large-scale logistics robot intelligent warehouse systems: logistics robot, task, and picking sta
Data Loading...
