@article {Ding:2012:1936-6612:322,
title = "Study on Portfolio Optimization of Small-Scale Project Grouping Based on Particle Swarm Optimization Algorithm",
journal = "Advanced Science Letters",
parent_itemid = "infobike://asp/asl",
publishercode ="asp",
year = "2012",
volume = "6",
number = "1",
publication date ="2012-03-15T00:00:00",
pages = "322-327",
itemtype = "ARTICLE",
issn = "1936-6612",
url = "https://www.ingentaconnect.com/content/asp/asl/2012/00000006/00000001/art00051",
doi = "doi:10.1166/asl.2012.2198",
keyword = "SMALL-SCALE, PREFERENCES COEFFICIENT, PARTICLE SWARM OPTIMIZATION ALGORITHM, EXPECTED UTILITY COEFFICIENT, PORTFOLIO OPTIMIZATION",
author = "Ding, Xian and Guo, Shuhang",
abstract = "To solve the current problems of portfolio optimization in small-scale project grouping, a model to measure the Decision-makers' Preferences Coefficient and a method to solve the Portfolio Optimization problem of small-scale project grouping based on an improved Particle Swarm Optimization
algorithm, which is added the Expected Utility coefficient into the projects, are proposed. First, using the traditional Particle Swarm Optimization algorithm solves the same problem among the different Decision-makers. Second, using the traditional Particle Swarm Optimization algorithm and
the improved one with the Expected Utility coefficient distinctly to solve the same problem is presented. As a result of the two groups of simulation experiments, the improved Particle Swarm Optimization algorithm is proved to be more objective and reliable to reflect the wishes of policy
makers and the intrinsic relationship between projects.",
}