Affiliations: Department of Computer Science and Information Systems, Bradley University, Peoria, IL, USA
Correspondence:
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Corresponding author: Adam Byerly, Department of Computer Science and Information Systems, Bradley University, 1501 W Bradley Ave, Peoria, IL 61625, USA. Tel.: +1 309 369 9257; E-mail:abyerly@bradley.edu
Abstract: This paper introduces a novel approach to avoiding early
stagnation in Ant Colony Optimization algorithms. The approach involves
oscillating the α and β parameters out of phase with each
other according to an offline adaptation formula triggered by the online
signal of stagnation in improved solutions across iterations. Further, in
this paper, we present the experimental results obtained from applying this
method to solving the Traveling Salesman Problem across eight fully
connected, symmetric maps of sizes ranging from 51 to 1,400 cities, and show
that a marginal improvement is achieved even with relatively constrained
amounts of computation time and in the absence of fine-tuning of the ACO
parameters towards each specific instance of the problem.
