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A novel marine predator algorithm for robot task allocation problem

Published online by Cambridge University Press:  09 June 2026

Xuefeng Gao Open the ORCID record for Xuefeng Gao [Opens in a new window] ,
Junkai Yi ,
Yongyue Wang ,
Hao Tan ,
Lingling Tan  and
Zhen Liu
Xuefeng Gao
Affiliation:
School of Instrumentation Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, China
Junkai Yi*
Affiliation:
College of Computer Science, Beijing Information Science and Technology University, China
Yongyue Wang
Affiliation:
Jiangsu Shuguang Optoelectronics Co., Ltd., China
Hao Tan
Affiliation:
School of Automation, Beijing Information Science and Technology University, China
Lingling Tan
Affiliation:
School of Automation, Beijing Information Science and Technology University, China
Zhen Liu
Affiliation:
College of Computer Science, Beijing Information Science and Technology University, China
*
Corresponding author: Junkai Yi; Email: yijk@bistu.edu.cn
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Abstract

To overcome the limitations of conventional robot task allocation algorithms, which often converge to suboptimal solutions, suffer from low computational accuracy, and produce excessively long execution paths as problem scales increase, we propose a novel marine predator algorithm (NMPA). By redesigning the predator–prey encoding mechanism in the traditional marine predator algorithm (MPA) and integrating information-based environmental search strategies with local search techniques, the proposed method substantially improves global exploration capability and solution precision. The NMPA is then evaluated on multiple benchmark and practical scenarios, including the traveling salesman problem (TSP), multi-traveling salesman problem (MTSP), single-robot task allocation with rigid time windows, and multi-robot task allocation. Experimental results on TSP and MTSP indicate that NMPA achieves more stable convergence, higher accuracy, and stronger robustness than ant colony optimization (ACO), simulated annealing, genetic algorithms (GAs), and their variants. Moreover, validation on real-world factory data in both single-robot and multi-robot task allocation scenarios confirms that NMPA delivers superior solution quality and overall performance compared with ACO, GAs, and their variants.

Keywords

novel marine predator algorithmdiscrete sequence optimizationmetaheuristic optimizationrobot task allocationmulti-robot coordination

Information

Type
Research Article
Information
Robotica , First View , pp. 1 - 33
Copyright
© The Author(s), 2026. Published by Cambridge University Press

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