Measuring the Performance of Ant Colony Optimization Algorithms for the Dynamic Traveling Salesman Problem

Abstract

Ant colony optimization (ACO) has proven its adaptation capabilities on optimization problems with dynamic environments. In this work, the dynamic traveling salesman problem (DTSP) is used as the base problem to generate dynamic test cases. Two types of dynamic changes for the DTSP are considered: (1) node changes and (2) weight changes. In the experiments, ACO algorithms are systematically compared in different DTSP test cases. Statistical tests are performed using the arithmetic mean and standard deviation of ACO algorithms, which is the standard method of comparing ACO algorithms. To complement the comparisons, the quantiles of the distribution are also used to measure the peak-, average-, and bad-case performance of ACO algorithms. The experimental results demonstrate some advantages of using quantiles for evaluating the performance of ACO algorithms in some DTSP test cases.

A Quantum-Inspired Ant Colony Optimization Algorithm for Parking Lot Rental to Shared E-Scooter Services

“…ACO draws inspiration from the behavior of ants searching for food and was proposed as a cooperative learning approach for solving hard combinatorial optimization problems [37]. Over the years, it has attracted a lot of attention and has been the subject of a huge number of theoretical and applied investigations (see, e.g., [38][39][40][41][42]).…”