A Portfolio Approach to Algorithm Selection for Discrete Time-Cost Trade-off Problem

TL;DR

This paper demonstrates that a portfolio of multiple multi-objective evolutionary algorithms outperforms individual algorithms in solving the discrete time-cost trade-off problem, offering faster solutions and better quality across benchmark instances.

Contribution

It introduces a portfolio approach combining four MOEAs for DTCTP, showing improved performance and providing insights into algorithm selection.

Findings

Portfolio approach is faster and qualitatively better than individual algorithms.

It effectively solves all benchmark instances of DTCTP.

Provides guidance for selecting the best algorithm per instance.

Abstract

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.