Chaotic particle swarm algorithm-based optimal scheduling of integrated energy systems

Zheng, Qingshuai; Gu, Yujiong; Liu, Yuhang; Ma, Jun; Peng, Maofeng

doi:10.1016/j.epsr.2022.108979

Cited by 13 publications

(2 citation statements)

References 5 publications

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“…The PSO algorithm constructs a population of particles with random values and then finds the optimal solution through subsequent iterations. In each iteration, individuals in the population update their orientation and rate by two poles [25][26][27]. The first pole is the local optimal solution searched by the individual particle, which is called the individual pole.…”

Section: Particle Swarm Algorithmmentioning

confidence: 99%

Emergency Resource Layout Planning Methodology with Multiple Constraints

Li,

Zhao

2023

Applied Mathematics and Nonlinear Sciences

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This paper first analyzes the characteristics and principles of the layout planning of emergency resources, explores the problems of emergency resource layout and distribution planning, and mentions the multi-constraint conditions of layout planning. Then, by describing the layout planning problem of emergency resources under multi-constraints and the related variable symbols, we constructed a two-layer layout planning model of emergency resources under multi-constraints, and after analyzing the particle swarm algorithm, we designed the layout planning model solving process based on particle swarm optimization. Finally, by constructing an emergency resource layout case, the centrality of the emergency resource layout network is explored, and the shortest distance and the best site selection of each emergency resource point corresponding to the demand point are divided. The results show that the structural degree centrality is between [0,0.78], the mileage degree centrality is between [0,1], the flow degree centrality is between [0.1,1], the structural median is between [0,0.32], the mileage median is between [0,2], and the structural proximity centrality and the mileage proximity centrality scores are both within the range of [0,1]. The shortest distribution distance of A, B, C, D, and E is selected to be only 393886m, and the error with the actual is around 0.009, which is able to carry out the layout planning effectively.

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Section: Particle Swarm Algorithmmentioning

confidence: 99%

Emergency Resource Layout Planning Methodology with Multiple Constraints

Li,

Zhao

2023

Applied Mathematics and Nonlinear Sciences

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“…In algorithm research, researchers usually use the fitness function to judge the performance of particles, and the particles with strong fitness will be preferentially retained and inherited, while the particles with poor fitness will naturally be eliminated. In the particle swarm algorithm discussed in this thesis, the absolute error integral is used as the algorithm's fitness function [18].…”

Section: The Fitness Function Of the Particle Swarm Algorithmmentioning

confidence: 99%

The influence of social network on innovation and entrepreneurship of economic and management college students based on adaptive particle swarm algorithm

Huo

2023

Applied Mathematics and Nonlinear Sciences

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Exploring the influence of social networks on innovation and entrepreneurship of economics and management college students to help them better enhance their innovation consciousness to face the future social workplace. In this paper, the particle swarm algorithm is optimized according to the strategy and parameter adaptive mechanism, and the SPS-PSO algorithm is used to initialize and update the population. The social network and innovation and entrepreneurship ability of university students in economics and management are introduced, and feature selection based on strategy and parameter adaptive particle swarm algorithm is utilized. The index mining of social network and innovation and entrepreneurship ability of college students is conducted, and the data analysis of innovation and entrepreneurship ability of college students in economics and management is carried out with the example of the University of S. From the social network, the percentages of those who agree with the validity of social network centrality, heterogeneity, dynamics and linkage strength are 71.56, 71.69%, 72.55% and 71.16%, respectively. From the perspective of innovation and entrepreneurship, the overall percentage of college students in economics and management who agreed was 69.27%. This indicates that the higher the integrity of the social network structure of economics and management college students, the more it can enhance innovation and entrepreneurship abilities and promote the cultivation of observation and judgment ability and divergent thinking abilities of economics and management college students.

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