A fine-grained fast parallel genetic algorithm based on a ternary optical computer for solving traveling salesman problem

Wang, Zhehe; Shen, Yunfu; Li, Shuang; Wang, Shuxin

doi:10.1007/s11227-022-04813-9

Cited by 14 publications

(3 citation statements)

References 42 publications

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“…Admittedly, in order to fully utilize all the CPUs of computers, parallelizability is becoming extremely essential for algorithms designed to solve large and complicated problems. Some parallel algorithms for TSPs can be seen in [53,54].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Layered Clustering Framework with Improved Genetic Algorithm for Solving Large-Scale Traveling Salesman Problems

Xu¹,

Lan²

2023

Preprint

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Traveling salesman problems (TSPs) are well-known combinatorial optimization problems, and most existing algorithms are challenging for solving TSPs when its scale is large. To improve the efficiency of solving large-scale TSPs, this work presents a novel adaptive layered clustering framework with improved genetic algorithm (ALC\_IGA). The primary idea behind ALC\_IGA is to break down a large-scale problem into a series of small-scale problems. First, the $k$-means and improved genetic algorithm are used to segment the large-scale TSPs layer by layer and generate the initial solution. Then, the developed two phases simplified $2$-opt algorithm is applied to further improve the quality of the initial solution. The analysis reveals that the computational complexity of the ALC\_IGA is between $O(n\log n)$ and $O(n^2)$. The results of numerical experiments on various TSP instances indicate that, in most situations, the ALC\_IGA surpasses the state-of-the-art algorithms in convergence speed, stability, and solution quality. Specifically, the ALC\_IGA can solve instances with $2 \times 10^5$ nodes within 0.15h, $1.4 \times 10^6$ nodes within 1h, and $2 \times 10^6$ nodes in three dimensions within 1.5h.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Layered Clustering Framework with Improved Genetic Algorithm for Solving Large-Scale Traveling Salesman Problems

Xu¹,

Lan²

2023

Preprint

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“…Ebadinezhad et al (2020) [6] proposed an ACO algorithm that included a dynamic evaporation strategy (DEACO). Wang et al (2022) [7] proposed a fine-grained fast parallel GA algorithm based on a ternary optical computer, and Zheng et al (2022) [8] proposed a transfer learning-based PSO algorithm. [9] proposed a variable neighborhood discrete WOA algorithm (VDWOA), while Panwar et al (2021) [10] proposed a novel discrete GWO algorithm.…”

Section: Introductionmentioning

confidence: 99%

Dynamic sub-route-based self-adaptive beam search Q-learning algorithm for traveling salesman problem

2023

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In this paper, a dynamic sub-route-based self-adaptive beam search Q-learning (DSRABSQL) algorithm is proposed that provides a reinforcement learning (RL) framework combined with local search to solve the traveling salesman problem (TSP). DSRABSQL builds upon the Q-learning (QL) algorithm. Considering its problems of slow convergence and low accuracy, four strategies within the QL framework are designed first: the weighting function-based reward matrix, the power function-based initial Q-table, a self-adaptive ε-beam search strategy, and a new Q-value update formula. Then, a self-adaptive beam search Q-learning (ABSQL) algorithm is designed. To solve the problem that the sub-route is not fully optimized in the ABSQL algorithm, a dynamic sub-route optimization strategy is introduced outside the QL framework, and then the DSRABSQL algorithm is designed. Experiments are conducted to compare QL, ABSQL, DSRABSQL, our previously proposed variable neighborhood discrete whale optimization algorithm, and two advanced reinforcement learning algorithms. The experimental results show that DSRABSQL significantly outperforms the other algorithms. In addition, two groups of algorithms are designed based on the QL and DSRABSQL algorithms to test the effectiveness of the five strategies. From the experimental results, it can be found that the dynamic sub-route optimization strategy and self-adaptive ε-beam search strategy contribute the most for small-, medium-, and large-scale instances. At the same time, collaboration exists between the four strategies within the QL framework, which increases with the expansion of the instance scale.

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“…(1) Ternary The feature of "ternary" originates from the fact that light has three stable physical states suitable for representing information in a computer. These three physical states are: dark state, bright state with horizontal polarization, and bright state with vertical polarization [12][13][14]. The physical nature of a computer can be described as using its own stable, unchanged during transmission, easily stored in devices, easily altered by devices, and complete set of physical states to represent information, and information processing is achieved by changing these physical states.…”

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confidence: 99%

Key technologies for task processing in ternary optical computer

Li,

Yang,

et al. 2024

Preprint

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This paper introduces a key technology for task processing in ternary optical computer(TOC) - the operation-data file (SZG file). Firstly, we analyze and summarize the data processing advantages of ternary optical computer. Then, we propose the design principles of SZG file and provide a detailed explanation of the significant role played by SZG file in the task processing process. Next, we will propose the design principles of SZG file and provide a detailed explanation of the significant role played by SZG file in the task processing process. Furthermore, we will discuss how to construct a collaborative task processing mechanism based on SZG file, focusing on the format of SZG file and the task classification strategy based on SZG file. Finally, we will conduct experimental testing on the ternary optical computer platform using a specific example. Tasks can be executed correctly in parallel on ternary optical computer, successfully coordinating the differences in task processing characteristics between electronic computers and ternary optical computer. These will establish a bridge for collaborative work between electronic computers and ternary optical computer, while also providing new ideas for other emerging computing systems in terms of task processing methods.

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A fine-grained fast parallel genetic algorithm based on a ternary optical computer for solving traveling salesman problem

Cited by 14 publications

References 42 publications

An Adaptive Layered Clustering Framework with Improved Genetic Algorithm for Solving Large-Scale Traveling Salesman Problems

An Adaptive Layered Clustering Framework with Improved Genetic Algorithm for Solving Large-Scale Traveling Salesman Problems

Dynamic sub-route-based self-adaptive beam search Q-learning algorithm for traveling salesman problem

Key technologies for task processing in ternary optical computer

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