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Application of particle swarm optimization algorithm in the heating system planning problem.

Ma RJ, Yu NY, Hu JY - ScientificWorldJournal (2013)

Bottom Line: An actual case study was calculated to check its feasibility in practical use.Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process.Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem.

View Article: PubMed Central - PubMed

Affiliation: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China. swjtumrj@139.com

ABSTRACT
Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem.

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Related in: MedlinePlus

Algorithm calculation in comparison with different parameters (6 heat sources).
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3713325&req=5

fig8: Algorithm calculation in comparison with different parameters (6 heat sources).

Mentions: Section 4.2 referred to the values of w, C1, and C2 which may influence the computational results. In Figures 8 and 9, the results obtained by IPSO were also proved. Algorithm calculation comparison with different parameters is shown in Figures 8 and 9, which is a visual display of the coordinates of heating sources with different parameters.


Application of particle swarm optimization algorithm in the heating system planning problem.

Ma RJ, Yu NY, Hu JY - ScientificWorldJournal (2013)

Algorithm calculation in comparison with different parameters (6 heat sources).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3713325&req=5

fig8: Algorithm calculation in comparison with different parameters (6 heat sources).
Mentions: Section 4.2 referred to the values of w, C1, and C2 which may influence the computational results. In Figures 8 and 9, the results obtained by IPSO were also proved. Algorithm calculation comparison with different parameters is shown in Figures 8 and 9, which is a visual display of the coordinates of heating sources with different parameters.

Bottom Line: An actual case study was calculated to check its feasibility in practical use.Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process.Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem.

View Article: PubMed Central - PubMed

Affiliation: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China. swjtumrj@139.com

ABSTRACT
Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem.

Show MeSH
Related in: MedlinePlus