Ma Rong-Jiang, Yu Nan-Yang, Hu Jun-Yi
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China.
ScientificWorldJournal. 2013 Jul 1;2013:718345. doi: 10.1155/2013/718345. Print 2013.
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.
基于生命周期成本（LCC）方法，本文针对供热系统规划（HSP）问题提出了一个完整的数学模型和粒子群优化（PSO）算法。所提出的数学模型以给定生命周期内供热系统成本最小化为目标。针对HSP问题的特殊性，对通用粒子群优化算法进行了改进。通过实际案例计算来检验其在实际应用中的可行性。结果表明，改进的粒子群优化（IPSO）算法比PSO算法能更优地解决HSP问题。此外，结果还表明在实际规划过程中进行决策时，该方法有提供有用信息的潜力。因此，可以认为，如果正确应用该方法并与其他要素相结合，它能够成为解决HSP问题的强大且有效的优化工具。
