IDA - Intelligent Data Analysis Research Group

BibTeX Entry

  category = {ida-publications},
  author = {Kremmel T. and Kubal{\'i}k J. and Biffl S.},
  title = {Software project portfolio optimization with advanced multiobjective evolutionary algorithms},
  journal = {Applied Soft Computing},
  year = {2010},
  publisher = {Elsevier},
  issn = {1568-4946},
  keywords = {Evolutionary computing, Decision support, Multiobjective optimization, Project portfolio management},
  note = {doi:10.1016/j.asoc.2010.04.013},
  abstract = {Large software companies have to plan their project portfolio to maximize potential portfolio return and strategic alignment, while balancing various preferences, and considering limited resources. Project portfolio managers need methods and tools to find a good solution for complex project portfolios and multiobjective target criteria efficiently. However, software project portfolios are challenging to describe for optimization in a practical way that allows efficient optimization. In this paper we propose an approach to describe software project portfolios with a set of multiobjective criteria for portfolio managers using the COCOMO II model and introduce a multiobjective evolutionary approach, mPOEMS, to find the Pareto-optimal front efficiently. We evaluate the new approach with portfolios choosing from a set of 50 projects that follow the validated COCOMO II model criteria and compare the performance of the mPOEMS approach with state-of-the-art multiobjective optimization evolutionary approaches. Major results are as follows: the portfolio management approach was found usable and useful; the mPOEMS approach outperformed the other approaches.},
  vvvs = {0},
  obory = {JC},
  projnum = {MSM 6840770012},

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