A Comparison of Meta-Heuristic Algorithms in Multi-Objective Highway Maintenance and Time-Cost Tradeoff Problems

Abstract

This paper compares the robustness of two different meta-heuristic algorithms on multi-objective optimization. This was done on a highway maintenance problem for simultaneously optimizing four conflicting objectives of time, cost, level of service, and environmental impact, in addition to a construction time-cost tradeoff (TCT) problem. These previously developed frameworks respectively used ant colony optimization (ACO) and genetic algorithm (GA) with adaptive weights. By the aim of constructing the comparison methodology, the optimization functions for each case study were restructured and solved using a GA based optimization software (Evolver™). The comparison of the optimization results for the two previously developed algorithms with the new approach reveals that the presented methodology requires less computational time and can generate better optimal solutions.