Path Planning of Pattern Transfer Based on Dual-Operator and a Dual-Population Ant Colony Algorithm for Digital Mask Projection Lithography

Wang, Yingzhi; Han, Tong; Xu, Jia; Yan, Yajing; Hong, Liu

doi:10.3390/e22030295

Cited by 4 publications

(3 citation statements)

References 42 publications

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“…The settings of each parameter in the ant colony algorithm will affect the performance of the algorithm. In the existing related research [49], the parameter settings of the ant colony algorithm usually need to be determined based on past experience and comparative experiments.…”

Section: Setup Of Experimental Parametersmentioning

confidence: 99%

Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem

Ma,

Liu

2024

Applied Sciences

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The split delivery vehicle routing problem (SDVRP) is a classic combinatorial optimization problem, which is usually solved using a heuristic algorithm. The ant colony optimization algorithm is an excellent heuristic algorithm that has been successfully applied to solve various practical problems, and it has achieved good results. However, in the existing ant colony optimization algorithms, there are issues with weak targeting of different customer selection strategies, difficulty in balancing convergence speed and global search ability, and a predisposition to become trapped in local optima. To solve these problems, this paper proposes an improved ant colony algorithm (IACA). First, in terms of customer point selection, the initial customer and noninitial customer selection strategies are proposed for different customers, and the adaptive selection threshold is designed. Second, in terms of pheromone processing, an initial pheromone distribution method based on a greedy strategy, a pheromone backtracking mechanism, and an adaptive pheromone volatile factor are proposed. Finally, based on the 2-opt local search method, vehicle path self-search and intervehicle path search are proposed to further improve the quality of the solution. This paper tests the performance of the IACA on datasets of different scales. The experimental results show that compared with the clustering algorithm, artificial bee colony algorithm, particle swarm optimization algorithm, traditional ant colony algorithm, and other algorithms, the IACA can achieve more competitive results. Specifically, compared to the path length calculated by other algorithms, the path length calculated by IACA decreased by an average of 1.58%, 4.28%, and 3.64% in small, medium, and large-scale tests, respectively.

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Section: Setup Of Experimental Parametersmentioning

confidence: 99%

Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem

Ma,

Liu

2024

Applied Sciences

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“…Hub et al [13] propose the maximum and minimum ant strategy, the pheromone concentration on the map is limited between the maximum pheromone concentration and the minimum pheromone concentration. Wang et al [19] propose a new dual-operator and dual-population ant colony optimization (DODPACO) algorithm, the load operators are adopted to limit the accumulation of the pheromone on the path in the early search path, and adjust the pheromone concentration on the path to avoid falling into the local optimum. Gao et al [16] propose a path merging strategy, which connects different paths from different ants to jump out of the local optimum and obtain the global optimal path.…”

Section: Related Workmentioning

confidence: 99%

Research on Multi-AGVs Path Planning and Coordination Mechanism

et al. 2020

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Reasonable automatic guided vehicle path planning can shorten the transportation time of materials and improve the production efficiency of the intelligent assembly workshop. Ant colony algorithm is a widely used path planning method, however, it suffers from the shortcomings that being easy to fall into local optimum and low search efficiency. To overcome these shortcomings, first, this paper proposes a step optimization method to improve the search efficiency of the ant colony algorithm, and a path simplification method to avoid getting blindly tortuous paths; Second, to overcome the problem that the ant colony algorithm is easy to fall into the local optimum, this paper proposes an adaptive pheromone volatilization coefficient strategy, which uses different pheromone volatilization coefficients at different stages of the search path; third, for the path conflict problem of multiple automatic guided vehicles, this paper proposes a load balancing strategy to avoid it, which is based on the consideration that, path conflicts are caused by excessive concentration of multiple automatic guided vehicles paths. Extensive simulation results demonstrate the feasibility and efficiency of the proposed methods. INDEX TERMS ant colony algorithm, intelligent assembly, multiple automatic guided vehicles, path planning;

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“…The ant colony algorithm [ 8 ], one of the representative bionic algorithms, draws a lesson from the behavior of ants exploring paths to find food, showing strong robustness. However, it has the problems of slow convergence and a poor quality of the solution when dealing with large-scale problems [ 9 ]. The artificial potential field (APF) [ 10 ] method assumes a gravitational force of the goal state and repulsive forces of the obstacles.…”

Section: Introductionmentioning

confidence: 99%

A Sampling-Based Algorithm with the Metropolis Acceptance Criterion for Robot Motion Planning

Liu

Zhao

Yan

et al. 2022

Sensors

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Motion planning is one of the important research topics of robotics. As an improvement of Rapidly exploring Random Tree (RRT), the RRT* motion planning algorithm is widely used because of its asymptotic optimality. However, the running time of RRT* increases rapidly with the number of potential path vertices, resulting in slow convergence or even an inability to converge, which seriously reduces the performance and practical value of RRT*. To solve this issue, this paper proposes a two-phase motion planning algorithm named Metropolis RRT* (M-RRT*) based on the Metropolis acceptance criterion. First, to efficiently obtain the initial path and start the optimal path search phase earlier, an asymptotic vertex acceptance criterion is defined in the initial path estimation phase of M-RRT*. Second, to improve the convergence rate of the algorithm, a nonlinear dynamic vertex acceptance criterion is defined in the optimal path search phase, which preferentially accepts vertices that may improve the current path. The effectiveness of M-RRT* is verified by comparing it with existing algorithms through the simulation results in three test environments.

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Path Planning of Pattern Transfer Based on Dual-Operator and a Dual-Population Ant Colony Algorithm for Digital Mask Projection Lithography

Cited by 4 publications

References 42 publications

Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem

Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem

Research on Multi-AGVs Path Planning and Coordination Mechanism

A Sampling-Based Algorithm with the Metropolis Acceptance Criterion for Robot Motion Planning

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