A Portfolio Approach to Algorithm Selection for Discrete Time-Cost
  Trade-off Problem
  It is a known fact that the performance of optimization algorithms for
NP-Hard problems vary from instance to instance. We observed the same trend
when we comprehensively studied multi-objective evolutionary algorithms (MOEAs)
on a six benchmark instances of discrete time-cost trade-off problem (DTCTP) in
a construction project. In this paper, instead of using a single algorithm to
solve DTCTP, we use a portfolio approach that takes multiple algorithms as its
constituent. We proposed portfolio comprising of four MOEAs, Non-dominated
Sorting Genetic Algorithm II (NSGA-II), the strength Pareto Evolutionary
Algorithm II (SPEA-II), Pareto archive evolutionary strategy (PAES) and Niched
Pareto Genetic Algorithm II (NPGA-II) to solve DTCTP. The result shows that the
portfolio approach is computationally fast and qualitatively superior to its
constituent algorithms for all benchmark instances. Moreover, portfolio
approach provides an insight in selecting the best algorithm for all benchmark
instances of DTCTP.
