Optimized layout methods based on optimization algorithms for DPOS

Gong, Xiaolin; Zheng, Xiaorui; Fang, Jiancheng; Liu, Gang

doi:10.1016/j.ast.2018.10.034

Cited by 17 publications

(8 citation statements)

References 18 publications

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“…For the elite particles (learning samples) stored in the external file, their quality will directly affect the execution efficiency of the algorithm [9]. erefore, in order to make the distribution of nondominated solutions as uniform as possible and minimize the distance from the real Pareto front, this study obtains the density information of nondominated solutions in the external file through analysis and calculation, which can be used as the basis to measure the advantages and disadvantages of nondominated solutions and store elite particles [10].…”

Section: Multiobjective Particle Swarm Optimizationmentioning

confidence: 99%

Multiobjective Algorithm for Urban Land Spatial Layout Optimization

Zhang²

2022

Security and Communication Networks

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In order to explore a quantitative and multiobjective optimization method of land use spatial allocation, this paper proposes a multiobjective algorithm for urban land spatial layout optimization. In this paper, the optimal multiobjective particle swarm optimization (MSO) algorithm is used to construct the optimal land use allocation model, and the variation characteristics of the optimized land use allocation scheme in quantity structure and spatial layout are analyzed. The results show that the total running time of the MSO model and the ordinary genetic algorithm spatial optimal allocation model is 8.57 h and 3.31 h, respectively, and the running efficiency of the mosolua model is 61.38% higher than that of the ordinary genetic algorithm spatial optimal allocation model. The configuration was optimized by using the model of land use spatial pattern from the plaque compactness, adjacency, aggregation degree, environmental compatibility, and the overall degree of resource-saving and environmental friendliness than the ordinary genetic algorithm model of optimal configuration results, and the model of overall fitness model compared with the ordinary genetic algorithm improved by 12.57%.

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Section: Multiobjective Particle Swarm Optimizationmentioning

confidence: 99%

Multiobjective Algorithm for Urban Land Spatial Layout Optimization

Zhang²

2022

Security and Communication Networks

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“…It provides a powerful method and tool for multiobjective nonlinear structural optimization design. Gong et al introduced an immune algorithm to improve the genetic algorithm [8]. In solving the complex problem of too many iterations of a population in TSP application, the probability of algorithm degradation was reduced by a vaccine test and annealing selection, and the phenomenon of population degradation was avoided.…”

Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Computer Mathematical Modeling Based on the Improved Genetic Algorithm and Mobile Computing

Wei

Liu

2021

Wireless Communications and Mobile Computing

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In order to change the problem of data redundancy in a genetic algorithm, this paper proposes a computer mathematical model based on the combination of an improved genetic algorithm and mobile computing. Combined with the least square method, MATLAB software is used to solve the equations, determine the range of parameters, and solve the estimation parameter range and identification problems. The improved genetic algorithm combined with mobile computing and least square method to establish a mathematical model greatly increased the individual search space and increased the operation rate of 90% compared to the basic genetic algorithm or mobile computing. The results show that the improved genetic algorithm and mobile computing have a certain ability to identify the optimal solution and greatly improve the work efficiency.

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“…As shown in Table 6, Tianjin Da-gang Power Plant 4 sets of 32,800 thousand coal-fired thermal power units, from 00:00 on 25 June 2018 to 24:00 on 25 August 2018, 60 days (the peak of summer heavy load), generating 1.513 billion kWh, with an average daily power generation is 25.22 million degrees. Based on the optimization decision-making unit, the proposed algorithm uses the full information interaction of the knowledge matrix to achieve accurate calculation of the decision-making output and efficient configuration of the controllable load, alleviating the complicated information and improving the real-time performance [35][36][37][38][39][40][41][42]. At the same time, the peak power consumption can be reduced by 21.7%, which is 6.1% better than that of multi-agent alone.…”

Section: Validity Testmentioning

confidence: 99%

Research on Micro-Grid Group Intelligent Decision Mechanism under the Mode of Block-Chain and Multi-Agent Fusion

Wang

et al. 2019

Energies

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This paper aims to study the problems of surplus interaction, poor real-time performance, and excessive processing of information in the micro-grid scheduling and decision-making process. Firstly, the micro-grid dual-loop mobile topology structure is designed by using the method of block-chain and multi-agent fusion, realizing the real-time update of the decision-making body. Secondly, on the basis of optimizing the decision-making body, a two-layer model of intelligent decision-making under the decentralized mechanism is established. Aiming at the upper model, based on the theory of block-chain consensus mechanism, this paper proposes an improved evolutionary game algorithm. The maximum risk-benefit in the decision-making process is the objective function, which realizes the evaluation and optimization of decision tasks. For the lower layer model, based on the block-chain distributed ledger theory, this paper proposes an improved hybrid game reinforcement learning algorithm, with the maximum controllable load participation as the objective function, and realizes the optimal configuration of distributed energy in the micro-grid. This paper reveals the rules of group intelligent decision making in micro-grid under multi-task. Finally, the effectiveness of the proposed algorithm is verified by using Beijing Jin-feng Energy Internet Park data.

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Optimized layout methods based on optimization algorithms for DPOS

Cited by 17 publications

References 18 publications

Multiobjective Algorithm for Urban Land Spatial Layout Optimization

Multiobjective Algorithm for Urban Land Spatial Layout Optimization

[Retracted] Computer Mathematical Modeling Based on the Improved Genetic Algorithm and Mobile Computing

Research on Micro-Grid Group Intelligent Decision Mechanism under the Mode of Block-Chain and Multi-Agent Fusion

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