基于改进粒子群算法的工程项目综合优化

王维博; 冯全源

doi:10.3969/j.issn.0258-2724.2011.01.012

基于改进粒子群算法的工程项目综合优化

doi: 10.3969/j.issn.0258-2724.2011.01.012

王维博,
冯全源^,

基金项目:

国家自然科学基金灢中国工程物理研究院联合基金资助项目(10876029)

国家863计划资助项目(2009AA01Z230)

详细信息

作者简介:
王维博(1977-),男,讲师,博士研究生,主要研究方向为智能计算、自适应信号处理等,E-mail:okwwb@163.com

通讯作者:
冯全源(1963-),男,教授,博士生导师,主要研究方向为移动天线与智能天线系统、电子器件等

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- 被引次数: 0
出版历程
- 收稿日期: 2009-05-04
- 刊出日期: 2011-02-02

Synthesis Optimization for Construction Project Based on Modified Particle Swarm Optimization Algorithm

WANG Weibo,
FENG Quanyuan^,

摘要

摘要: 为解决现有粒子群优化算法进化过程中早熟暠的问题,提出了一种改进的粒子群优化算法 HSPSO.算法采用多子群分层策略,以提高收敛速度和优化精度.为求解工程项目的综合优化问题,建立了工期灢成本灢质量的数学优化模型和多目标优化模型.通过实例对标准粒子群优化算法(SPSO)和差分进化(DE)算法进行了比较,并采用 HSPSO算法进行多目标优化.最后,用枚举法验证了模型的合理性和算法的有效性.与已有研究相比,HSPSO算法能在种群规模较小(20个粒子)的情况下,快速找到满意的解(平均迭代次数不超过20次).
- 粒子群优化算法-项目管理-多目标
Abstract: A modified PSO (particle swarm optimization) algorithm—hierarchical subpopulation PSO（HSPSO) was proposed to avoid the premature phenomenon of the PSO algorithm during evolution. By using the strategy of subpopulation hierarchy, the algorithm can improve the convergence speed and accuracy. For the synthesis optimization of a construction project, mathematical optimization models and a multi-objective optimization model of construction time, cost and quality were established. In a case study, the standard PSO (SPSO) and differential evolution (DE) algorithms were compared, and the HSPSO algorithm was utilized to its synthesis optimization. In addition, the exhaustive enumeration was used to verify the effectiveness of these models and the feasibility of the HSPSO algorithm. The result shows that the HSPSO algorithm can quickly obtain satisfied results with average iterative times of less than 20 under the condition of a swarm size of 20 particles.
- particle swarm optimization algorithm