An intelligent scheduling method based on improved particle swarm optimization algorithm for drainage pipe network

Luo, Yaqi; Zeng, Bi

doi:10.1063/1.4992854

Cited by 5 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The goal of the support vector regression algorithm is to determine the regression function f (x) so that all sample data are near the regression function f (x), allowing some sample data to deviate from f (x) by a certain distance e, which has a good fit [ 3 ]. In the two-dimensional space, the regression function of the sample data is shown in Fig.…”

Section: Improved Particle Swarm Optimization For Network Informationmentioning

confidence: 99%

Research on Information Security Monitoring and Early Warning Mechanism of Internet Application Network Based on Particle Swarm Optimization

Chen

Zou

Li³

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Due to the frequent occurrence of network security incidents, causing unnecessary losses to people, frequent network security incidents are worrying. For the problems of Internet application network information security, attackers use attacks to continuously threaten them. This paper studies the method of information security monitoring and early warning mechanism for Internet application network based on particle swarm optimization. Based on the support vector regression machine, a network security prediction model with multi-group chaotic particle optimization is established. The prediction results are obtained through the network information security monitoring and early warning mechanism, and the prediction results are analyzed and summarized. The results show that the Internet application network information security prediction model based on particle swarm optimization algorithm can provide guidance for the development of network security solutions and strategies, enhance the initiative of network security defense, reduce the losses caused by network attacks, and have better practicality Sex.

show abstract

Section: Improved Particle Swarm Optimization For Network Informationmentioning

confidence: 99%

Research on Information Security Monitoring and Early Warning Mechanism of Internet Application Network Based on Particle Swarm Optimization

Chen

Zou

Li³

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

show abstract

“…Above all, a mathematical model of the microgrid is built. A multi-objective optimisation microgrid operating model was created for several microgrid states, including the grid-connected state and the island state, building on the previously produced microgrid mathematical model [17].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty

Mquqwana,

Krishnamurthy

2024

Energies

View full text Add to dashboard Cite

Microgrids can assist in managing power supply and demand, increase grid resilience to adverse weather, increase the deployment of zero-emission energy sources, utilise waste heat, and reduce energy wasted through transmission lines. To ensure that the full benefits of microgrid use are realised, hybrid renewable energy-based microgrids must operate at peak efficiency. To offer an optimal solution for managing microgrids with hybrid renewable energy sources (HRESs) while taking microgrid reserve margins into account, the particle swarm optimisation (PSO) method is suggested. The suggested approach demonstrated good performance in terms of charging and discharging BESS and maintaining the necessary reserve margins to supply critical loads if the grid and renewable energy sources are unavailable. On a clear day, the amount of electricity sold to the grid increased by 58%, while on a partially overcast day, it increased by 153%. Microgrids provide a good return on investment for their operators when they are run at peak efficiency. This is because the BESS is largely charged during off-peak hours or with excess renewable energy, and power is only purchased during less expensive off-peak hours.

show abstract

“…As pipes are necessary parts of transport systems, hydraulic systems, pneumatic circuits, and auxiliary systems, pipe routing design (PRD) is vital for the detailed design of many industry equipment and system (Kim et al, 2013). Therefore, PRD has gained huge attention in many industries, such as aircraft (Yin et al, 2013), ship system (Jiang et al, 2015) and drainage network (Luo and Zeng, 2017). The purpose of PRD is to figure out an optimal route from a start location to a terminal location in an environment with obstacles under various kinds of constraints (Jiang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Improved ant colony optimization algorithm and its application to solve pipe routing design

Tian

Wang

et al. 2019

View full text Add to dashboard Cite

Purpose As a kind of NP-hard combinatorial optimization problem, pipe routing design (PRD) is applied widely in modern industries. In the offshore oil and gas industry, a semi-submersible production platform is an important equipment for oil exploitation and production. PRD is one of the most key parts of the design of semi-submersible platform. This study aims to present an improved ant colony algorithm (IACO) to address PRD for the oil and gas treatment system when designing a semi-submersible production platform. Design/methodology/approach First, to simplify PRD problem, a novel mathematical model is built according to real constraints and rules. Then, IACO, which combines modified heuristic function, mutation mechanism and dynamical parameter mechanism, is introduced. Findings Based on a set of specific instances, experiments are carried out, and the experimental results show that the performance of IACO is better than that of two variants of ACO, especially in terms of the convergence speed and swarm diversity. Finally, IACO is used to solve PRD for the oil and gas treatment system of semi-submersible production platform. The simulation results, which include nine pipe paths, demonstrate the practicality and high-efficiency of IACO. Originality/value The main contribution of this study is the development of method for solving PRD of a semi-submersible production platform based on the novel mathematical model and the proposed IACO.

show abstract

An intelligent scheduling method based on improved particle swarm optimization algorithm for drainage pipe network

Cited by 5 publications

References 0 publications

Research on Information Security Monitoring and Early Warning Mechanism of Internet Application Network Based on Particle Swarm Optimization

Research on Information Security Monitoring and Early Warning Mechanism of Internet Application Network Based on Particle Swarm Optimization

Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty

Improved ant colony optimization algorithm and its application to solve pipe routing design

Contact Info

Product

Resources

About