Multi-task allocation with an optimized quantum particle swarm method

Li, Mincan; Liu, Chubo; Li, Kenli; Liao, Xiangke; Li, Keqin

doi:10.1016/j.asoc.2020.106603

Cited by 43 publications

(7 citation statements)

References 23 publications

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“…Task allocation is an invaluable topic within the field of multi-robot systems(MRS) and is one of the most challenging problems [20][21] . This problem can be seen as an optimal assignment problem where the objective is to optimally assign a set of robots to a set of tasks in such a way that optimizes the overall system performance subject to a set of constraints [22] .…”

Section: Introductionmentioning

confidence: 99%

“…This problem can be seen as an optimal assignment problem where the objective is to optimally assign a set of robots to a set of tasks in such a way that optimizes the overall system performance subject to a set of constraints [22] . The phenomenon of division of labor in biology is an important source of inspiration for solving this problem, for example, self-organized task allocation systems inspired by the emergence of cooperation in biology [23] , and intelligent optimization algorithms such as particle swarm optimization(PSO), genetic algorithm(GA), and artificial neural network(ANN) proposed to simulate biological behavior provide satisfactory solutions to the robot combination problem [20] . Division of labor within community game model is designed for cooperation in large labor division scenarios and is fully applicable to multi-robot systems.…”

Section: Introductionmentioning

confidence: 99%

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Urgency coefficient and responsibility coefficient facilitate the evolution of cooperation in multi-agent systems

Zhong

Zhang

2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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Task allocation is a significant topic in the field of artificial intelligence, and ideas for solving this problem are often drawn from the widespread phenomenon of division of labor in nature. For the problem that the existing labor division game model does not match the characteristics of the large-scale division of labor scenarios, We propose a new model named division of labor within community game(DOLC). The article establishes the mathematical model of DOLC, then we investigate the effects of selection intensity, urgency coefficient and responsibility coefficient on the evolution of division of labor. We derived the following conclusions: When both urgency and responsibility coefficients are zero, this causes the tragedy of DOLC, while group has the opportunity to maintain the division of labor if one of them is positive. The responsible group has always been cooperative. Relative to urgency coefficient, responsibility coefficient plays a more noticeable role in facilitating the evolution of cooperation in population. DOLC model is well suited to task allocation problem in multi-agent systems, and the findings of this article provide new insights for improving the intelligence of unmanned systems and optimizing task allocation and scheduling.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Urgency coefficient and responsibility coefficient facilitate the evolution of cooperation in multi-agent systems

Zhong

Zhang

2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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“…Zhao [12] proposed an improved Artificial Bee Colony (ABC) algorithm to solve the problem of dynamic reconnaissance resource allocation of multi-UAVs, which improves the efficiency of dynamic reconnaissance resource allocation and meets the requirements of dynamic reconnaissance. In order to balance the task revenue and the success rate of task execution, Li [13] proposed an effective Stability Quantum Particle Swarm Optimization (SQPSO) algorithm under the condition of fully considering the stability of multi-agent system. Junier [14] proposed a heuristic that combines a swarm intelligence strategy with the generalized assignment problem (GAP) method in view of the defects in resource allocation when multi-UAVs perform tasks cooperatively in a dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAVs Cooperative Reconnaissance Task Allocation Under Heterogeneous Target Values

Wei

Xiaolin

2022

IEEE Access

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This paper presents a Multiple Unmanned Aerial Vehicles (UAVs) cooperative reconnaissance task allocation model based on heterogeneous target value and proposes an improved Multi-Verse Optimizer (MVO) algorithm. Firstly, according to the reconnaissance value of the target, the reconnaissance targets are divided into high-value targets, low-value targets and decoy targets. It improves the authenticity of the problem. The purpose of task allocation is to maximize the reconnaissance revenue of UAVs as much as possible under the condition of minimizing the reconnaissance time and fuel loss of UAVs to the targets. Then, to solve the model, this paper improves the traditional MVO algorithm. Adaptive compression factor is introduced to improve the convergence speed of the algorithm. In addition, the differential mutation operation is performed in the wormhole movement stage to enhance the global search ability of the algorithm. The simulation results show that the improved algorithm can successfully solve the reconnaissance task allocation problem under different target values, and has obvious advantages in reconnaissance revenue and calculation speed compared with other methods.INDEX TERMS Multi-UAVs, reconnaissance task allocation, heterogeneous target value, MVO algorithm.

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“…Particle swarm optimisation, proposed in 1995 by Kennedy et al [1] is an optimisation algorithm widely used for a range of problems. Since its invention, due to its simple structure and effectiveness, PSO has attracted a lot of attention from researchers which resulted in many variants [2] and applications in a range of fields [3] [4]. The vast majority of PSO variants proposed in the literature address the problem of premature convergence to improve the performance of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

HIDMS-PSO with Bio-inspired Fission-Fusion Behaviour and a Quorum Decision Mechanism

Varna

Husbands

2021

2021 IEEE Congress on Evolutionary Computation (CEC)

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In this study, we propose a new variant of the HIDMS-PSO algorithm with a bio-inspired fission-fusion behaviour and a quorum decision mechanism (FFQ-HIDMS-PSO). In the new algorithm, units are conceptualised as self-organising fission-fusion societies that determine and adopt a suitable behaviour using unit-based quorum decisions. The incorporation of the two bio-inspired mechanisms provide "diversity aware" selforganising units that react to stagnation of particles by adopting a suitable fission-fusion behaviour, leading to a more efficient algorithm capable of maintaining significantly better population diversity throughout the search. The performance of the proposed algorithm was verified with three distinct experiments conducted using CEC'17 and CEC'05 test suites at 30 and 50 dimensions, comparing against 12 state-of-the-art metaheuristics and 12 state-of-the-art PSO variants. The proposed algorithm showed superior performance in these experiments by outperforming all 24 algorithms in all three experiments at 30 and 50 dimensions. The empirical evidence suggests that the proposed method also maintains significantly superior population diversity in comparison to the original HIDMS-PSO.

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Multi-task allocation with an optimized quantum particle swarm method

Cited by 43 publications

References 23 publications

Urgency coefficient and responsibility coefficient facilitate the evolution of cooperation in multi-agent systems

Urgency coefficient and responsibility coefficient facilitate the evolution of cooperation in multi-agent systems

Multi-UAVs Cooperative Reconnaissance Task Allocation Under Heterogeneous Target Values

HIDMS-PSO with Bio-inspired Fission-Fusion Behaviour and a Quorum Decision Mechanism

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