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Multiple-solution Optimization Strategy for Multi-robot Task Allocation

Huang, Li, Ding, Y and Jin, Yaochu (2018) Multiple-solution Optimization Strategy for Multi-robot Task Allocation IEEE Transactions on Systems, Man and Cybernetics: Systems.

SMCA-18-04-0423-Clean.pdf - Accepted version Manuscript

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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 work proposes an optimization strategy for multi-robot 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 work presents a niching immune-based optimization algorithm based on Softmax regression (sNIOA) to handle it. A pre-judgment of population is done before entering an evaluation process to reduce the evaluation time and to avoid unnecessary computation. Furthermore, a guiding mutation 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 guiding mutation operator by comparing sNIOA with and without it. Two MRTA application cases are tested finally.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Huang, Li
Ding, Y
Date : 18 July 2018
DOI : 10.1109/TSMC.2018.2847608
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Uncontrolled Keywords : Multi-robot task allocation, multimodal optimization, niching immune-based optimization algorithm, Softmax regression, guiding mutation operator.
Depositing User : Melanie Hughes
Date Deposited : 28 Jun 2018 09:29
Last Modified : 07 Nov 2018 08:58

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