Multiple-Solution Optimization Strategy for Multirobot Task Allocation

Document Type

Article

Publication Date

11-1-2020

Abstract

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.

Identifier

85059111339 (Scopus)

Publication Title

IEEE Transactions on Systems Man and Cybernetics Systems

External Full Text Location

https://doi.org/10.1109/TSMC.2018.2847608

e-ISSN

21682232

ISSN

21682216

First Page

4283

Last Page

4294

Issue

11

Volume

50

Grant

2016YFB0302700

Fund Ref

National Natural Science Foundation of China

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