Multi-Objective Optimization for Submarine Cable Route Planning Based on the Ant Colony Optimization Algorithm

Zhao, Zanshan; Wang, Jingting; Gao, Guanjun; Wang, Haoyu; Wang, Daobin

doi:10.3390/photonics10080896

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Then mathematical programming [24] is used to solve the problem, but the traditional solution method can only obtain an optimal solution each time, and the relationship between the objectives needs to be studied to transform the multi-objective solution into a single-objective solution, which is complex and inefficient. Intelligent algorithms based on Pareto optimal solutions mainly include multi-objective genetic algorithms (NSGA, NSGA-II, NSGA-III) [46], multi-objective particle swarm optimization (MOPSO) [47], and multi-target human worker ant colony algorithm (MOABC) [48], Their essence is to use modern intelligent algorithms to calculate the Pareto solution set of the multi-objective optimization model and can generate multiple Pareto optimal solutions at a time, which has been widely concerned by scholars at home and abroad. Genetic algorithm (GA) is a kind of search algorithm that simulates the genetic and evolutionary processes of natural organisms and has good applicability to complex nonlinear and multi-dimensional space optimization problems.…”

Section: Key Points Of the Algorithmmentioning

confidence: 99%

Research on the Carbon Reduction Technology Path of the Iron and Steel Industry Based on a Multi-Objective Genetic Algorithm

Xie,

Ma,

Wang

et al. 2024

Sustainability

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This paper establishes a multi-objective optimization model based on an improved NSGA-II algorithm, aiming to study the carbon reduction technology path of specific enterprises in the steel industry under the background of China’s dual-carbon goal and fill the research gap in the carbon reduction technology path of steel enterprises, which has certain guiding significance for the realization of China’s dual-carbon goal and the low-carbon development of steel enterprises. Firstly, through the analysis of the list of extreme energy efficiency technologies in the steel industry and the main process flow of steel industry production, the multi-objective optimization model is constructed from the two objective dimensions of maximum CO2 emission reduction and maximum enterprise economic benefit. Then the improved NSGA-II algorithm is used to solve the model. And the empirical analysis of a Hebei iron and steel enterprise, based on the technology application of enterprises before the release of the technology list, the technology path of enterprises to reduce carbon is predicted. The actual application data of the enterprise is used for verification and analysis, and suggestions on the technical path for the future low-carbon development of the enterprise are provided. The experimental results show that: (1) The optimal solution set of Pareto is consistent with the practical application of enterprises, and the constructed model is accurate and efficient, which can be used for the research of carbon reduction technology path. (2) When introducing technology, enterprises can give priority to the solution of common set technology based on their own needs.

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Section: Key Points Of the Algorithmmentioning

confidence: 99%

Research on the Carbon Reduction Technology Path of the Iron and Steel Industry Based on a Multi-Objective Genetic Algorithm

Xie,

Ma,

Wang

et al. 2024

Sustainability

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Multi-objective optimization for submarine optical cable route planning based on cross reinforcement learning

Zhao,

Gao,

Gan

et al. 2024

J. Opt. Commun. Netw.

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Submarine cable is a crucial infrastructure for international communications, and its cost and survivability are two key factors that must be considered at its design phase. In this paper, we propose a machine-learning-assisted submarine cable route planning algorithm for minimizing its accumulated cost and risk. The cost and risk distribution and the direction of the submarine cable route’s starting point and endpoint are used as prior data to initialize the state-action of reinforcement learning (RL). We also propose a multi-agent cross reinforcement learning (MA-XRL) framework composed of Q-learning and SARSA to improve the global optimization capability of RL in the case of multi-objective optimization. The results show that, compared to ant colony optimization (ACO), MA-XRL can reduce the accumulated cost by 26.87% under the same accumulated risk. The maximum accumulated cost of the Pareto solutions obtained by MA-XRL is lower than the minimum accumulated cost of that obtained by ACO. Meanwhile, the running time of MA-XRL is only 1.3‰ of that of ACO. Without prior data of cost and risk initialization, the accumulated cost and risk of the best submarine cable route obtained by MA-XRL is 1.84 times and 7.08 times those with cost and risk distribution initialization, respectively. The direction initialization can accelerate the agent to find the endpoint of the submarine cable route and double the search stability of MA-XRL. Compared to using Q-learning or SARSA alone, MA-XRL can respectively reduce the accumulated risk by 71.81% and 39.51% under the same accumulated cost and can reduce the accumulated cost by 16.65% and 11.99% under the same accumulated risk, respectively.

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Multi-Objective Optimization for Submarine Cable Route Planning Based on the Ant Colony Optimization Algorithm

Cited by 2 publications

References 14 publications

Research on the Carbon Reduction Technology Path of the Iron and Steel Industry Based on a Multi-Objective Genetic Algorithm

Research on the Carbon Reduction Technology Path of the Iron and Steel Industry Based on a Multi-Objective Genetic Algorithm

Multi-objective optimization for submarine optical cable route planning based on cross reinforcement learning

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