TY - JOUR
T1 - Multiple-Solution Optimization Strategy for Multirobot Task Allocation
AU - Huang, Li
AU - Ding, Yongsheng
AU - Zhou, Meng Chu
AU - Jin, Yaoch
AU - Hao, Kuangrong
N1 - Funding Information:
She is currently a visiting student with the Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA, supported by the China Scholarship Council. Her current research interests include cooperative control and dynamic optimization of multirobot systems and intelligent automation.
Funding Information:
Manuscript received April 3, 2018; accepted June 8, 2018. Date of publication July 18, 2018; date of current version October 15, 2020. This work was supported in part by the National Key Research and Development Plan from Ministry of Science and Technology under Grant 2016YFB0302700, in part by the National Natural Science Foundation of China under Grant 61473077, Grant 61473078, Grant 61503075, and Grant 61603090, in part by the International Collaborative Project of the Shanghai Committee of Science and Technology under Grant 16510711100, in part by the Shanghai Science and Technology Promotion Project form Shanghai Municipal Agriculture Commission under Grant 2016-1-5-12, in part by the Fundamental Research Funds for the Central Universities under Grant 2232015D3-32, in part by the Cooperative Research funds of the National Natural Science Funds Overseas and Hong Kong and Macau Scholars under Grant 61428302, and in part by the Program for Changjiang Scholars from the Ministry of Education 2015–2019. This paper was recommended by Associate Editor S. Nahavandi. (Corresponding author: MengChu Zhou.) L. Huang, Y. Ding, and K. Hao are with the Engineering Research Center of Digitized Textile and Apparel Technology, Ministry of Education, Shanghai 201620, China, and also with the College of Information Science and Technology, Donghua University, Shanghai 201620, China (e-mail: huanglili622@126.com; ysding@dhu.edu.cn; krhao@dhu.edu.cn).
Publisher Copyright:
© 2018 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Multiple solutions are often needed because of different kinds of uncertain failures in a plan execution process and scenarios for which precise mathematical models and constraints are difficult to obtain. This paper proposes an optimization strategy for multirobot task allocation (MRTA) problems and makes efforts on offering multiple solutions with same or similar quality for switching and selection. Since the mentioned problem can be regarded as a multimodal optimization one, this paper presents a niching immune-based optimization algorithm based on Softmax regression (sNIOA) to handle it. A prejudgment of population is done before entering an evaluation process to reduce the evaluation time and to avoid unnecessary computation. Furthermore, a guiding mutation (GM) operator inspired by the base pair in theory of gene mutation is introduced into sNIOA to strengthen its search ability. When a certain gene mutates, the others in the same gene group are more likely to mutate with a higher probability. Experimental results show the improvement of sNIOA on the aspect of accelerating computation speed with comparison to other heuristic algorithms. They also show the effectiveness of the proposed GM operator by comparing sNIOA with and without it. Two MRTA application cases are tested finally.
AB - Multiple solutions are often needed because of different kinds of uncertain failures in a plan execution process and scenarios for which precise mathematical models and constraints are difficult to obtain. This paper proposes an optimization strategy for multirobot task allocation (MRTA) problems and makes efforts on offering multiple solutions with same or similar quality for switching and selection. Since the mentioned problem can be regarded as a multimodal optimization one, this paper presents a niching immune-based optimization algorithm based on Softmax regression (sNIOA) to handle it. A prejudgment of population is done before entering an evaluation process to reduce the evaluation time and to avoid unnecessary computation. Furthermore, a guiding mutation (GM) operator inspired by the base pair in theory of gene mutation is introduced into sNIOA to strengthen its search ability. When a certain gene mutates, the others in the same gene group are more likely to mutate with a higher probability. Experimental results show the improvement of sNIOA on the aspect of accelerating computation speed with comparison to other heuristic algorithms. They also show the effectiveness of the proposed GM operator by comparing sNIOA with and without it. Two MRTA application cases are tested finally.
KW - Guiding mutation (GM) operator
KW - multimodal optimization
KW - multirobot task allocation (MRTA)
KW - niching immune-based optimization algorithm (NIOA)
KW - softmax regression
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U2 - 10.1109/TSMC.2018.2847608
DO - 10.1109/TSMC.2018.2847608
M3 - Article
AN - SCOPUS:85059111339
SN - 2168-2216
VL - 50
SP - 4283
EP - 4294
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 11
M1 - 8412761
ER -