Empirical study on the effect of population size on MAX-MIN ant system in dynamic environments

Abstract

Abstract-In this paper, the effect of the population size on the performance of the MAX -MIN ant system for dynamic optimization problems (DOPs) is investigated. DOPs are generated with the dynamic benchmark generator for permutationencoded problems. In particular, the empirical study investigates: a) possible dependencies of the population size parameter with the dynamic properties of DOPs; b) the effect of the population size with the problem size of the DOP; and c) whether a larger population size with less algorithmic iterations performs better than a smaller population size with more algorithmic iterations given the same computational budget for each environmental change. Our study shows that the population size is sensitive to the magnitude of change of the DOP and less sensitive to the frequency of change and the problem size. It also shows that a longer duration in terms of algorithmic iterations results in a better performance.

On the elusivity of dynamic optimisation problems

“…First, we replicate and evaluate the elusivity concept by a comprehensive and straightforward case study, where the landscape is rotated for the travelling salesman problem (DTSP) and the knapsack problem (DKP) [2,37,42]. In the case of DTSP, the instances 4 kroA100, kroA150 and kroA200 (containing 100, 150 and 200 cities) are used to insert dynamism, and their bestknown values are obtained from [47], used for the accuracy measure. For DKPs, three static instances have been constructed following the patterns presented in [22], and they are available online 5 repository as supplementary material.…”

Pre-scheduled Colony Size Variation in Dynamic Environments

“…A previous empirical study showed that the colony size of the MMAS algorithm, one of the best performing ACO algorithms, is sensitive to the properties of DOPs [18]. In particular, if for a given DOP only a certain computation budget, e.g., the maximum number of function evaluations, is available, then the colony size, i.e., the number of ants, is a very critical parameter.…”

“…When a given computational budget is available, e.g., the maximum number of function evaluations, a smaller number of ants will produce more algorithmic iterations whereas a larger number of ants less. Hence, the population size affects the duration of the learning reinforcement [18].…”