Quantum ant colony optimization algorithm for AGVs path planning based on Bloch coordinates of pheromones

Li, Junjun; Xu, Baoyun; Yang, Yongsheng; Wu, Huafeng

doi:10.1007/s11047-018-9711-0

Cited by 10 publications

(3 citation statements)

References 32 publications

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“…Lately, ACO has been improved and used to solve different problems more efficiently. For example, for automated guided vehicles (Li et al 2020), for topologybased link prediction (Cao et al 2018) and for query optimization (Mohsin et al 2021). These implementations are based on the parallel nature of quantum systems.…”

Section: Previous Workmentioning

confidence: 99%

Implementable hybrid quantum ant colony optimization algorithm

Andoin¹,

Osaba²,

Oregi³

et al. 2022

Quantum Mach. Intell.

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We propose a new hybrid quantum algorithm based on the classical Ant Colony Optimization algorithm to produce approximate solutions for NP-hard problems, in particular optimization problems. First, we discuss some previously proposed Quantum Ant Colony Optimization algorithms, and based on them, we develop an improved algorithm that can be truly implemented on near-term quantum computers. Our iterative algorithm codifies only the information about the pheromones and the exploration parameter in the quantum state, while subrogating the calculation of the numerical result to a classical computer. A new guided exploration strategy is used in order to take advantage of the quantum computation power and generate new possible solutions as a superposition of states. This approach is specially useful to solve constrained optimization problems, where we can implement efficiently the exploration of new paths without having to check the correspondence of a path to a solution before the measurement of the state. As an example of a NP-hard problem, we choose to solve the Quadratic Assignment Problem. The benchmarks made by simulating the noiseless quantum circuit and the experiments made on IBM quantum computers show the validity of the algorithm.

show abstract

Section: Previous Workmentioning

confidence: 99%

Implementable hybrid quantum ant colony optimization algorithm

Andoin¹,

Osaba²,

Oregi³

et al. 2022

Quantum Mach. Intell.

View full text Add to dashboard Cite

show abstract

“…Lately, ACO has been improved and used to solve different problems more efficiently. For example, for automated guided vehicles (Li et al, 2020), for topology-based link prediction (Cao et al, 2018) and for query optimization (Mohsin et al, 2021). These implementations are based on the parallel nature of quantum systems.…”

Section: Previous Workmentioning

confidence: 99%

Implementable Hybrid Quantum Ant Colony Optimization Algorithm

de Andoin,

Echanobe

2021

Preprint

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We propose a new hybrid quantum algorithm based on the classical Ant Colony Optimization algorithm to solve NP-hard problems, in particular optimization problems. First, we discuss some previously proposed Quantum Ant Colony Optimization algorithms, and based on them, we develop an improved algorithm that can be truly implemented on near-term quantum computers. Our iterative algorithm codes only the information about the pheromones and the exploration parameter in the quantum state, while subrogating the calculation of the numerical result to a classical computer. A new guided exploration strategy is used in order to take advantage of the quantum computation power and generate new possible solutions as a superposition of states. This approach is specially useful to solve constrained optimization problems, where we can implement efficiently the exploration of new paths using Fredkin gates without having to check the correspondence of a path to a solution before the measurement of the state. As an example of a NP-hard problem, we choose to solve the Quadratic Assignment Problem. The benchmarks made by simulating the noiseless quantum circuit and the experiments made on IBM quantum computers show the validity of the algorithm.

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“…Chen et al [8] utilized the ant agent optimized by a repulsive potential field to improve collision avoidance, transportation distance, and efficiency. Li et al [9] proposed a novel quantum ant colony optimization algorithm that combines the advantages of various methods. Choi et al [10] proposed a QMIX-based scheme for cooperative path control of multiple AGVs.…”

Section: Introductionmentioning

confidence: 99%

An Improved Bounded Conflict-Based Search for Multi-AGV Pathfinding in Automated Container Terminals

Zhou,

Zhu

2024

CMES

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As the number of automated guided vehicles (AGVs) within automated container terminals (ACT) continues to rise, conflicts have become more frequent. Addressing point and edge conflicts of AGVs, a multi-AGV conflict-free path planning model has been formulated to minimize the total path length of AGVs between shore bridges and yards. For larger terminal maps and complex environments, the grid method is employed to model AGVs' road networks. An improved bounded conflict-based search (IBCBS) algorithm tailored to ACT is proposed, leveraging the binary tree principle to resolve conflicts and employing focal search to expand the search range. Comparative experiments involving 60 AGVs indicate a reduction in computing time by 37.397% to 64.06% while maintaining the over cost within 1.019%. Numerical experiments validate the proposed algorithm's efficacy in enhancing efficiency and ensuring solution quality.

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Quantum ant colony optimization algorithm for AGVs path planning based on Bloch coordinates of pheromones

Cited by 10 publications

References 32 publications

Implementable hybrid quantum ant colony optimization algorithm

Implementable hybrid quantum ant colony optimization algorithm

Implementable Hybrid Quantum Ant Colony Optimization Algorithm

An Improved Bounded Conflict-Based Search for Multi-AGV Pathfinding in Automated Container Terminals

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