A Portfolio Approach to Algorithm Selection for Discrete Time-Cost Trade-off Problem
This work addresses algorithm selection for NP-hard optimization problems in construction project management, but it is incremental as it applies an existing portfolio method to a specific domain.
The authors tackled the discrete time-cost trade-off problem in construction projects by using a portfolio of multiple multi-objective evolutionary algorithms instead of a single algorithm, resulting in a computationally fast and qualitatively superior approach across all benchmark instances.
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.