Ant system: optimization by a colony of cooperating agents

TLDR

Ant colonies inspired a new computational paradigm called ant system (AS). The authors propose AS as a viable approach to stochastic combinatorial optimization. AS employs positive feedback, distributed computation, and a constructive greedy heuristic to rapidly discover good solutions, avoid premature convergence, and enable early acceptable solutions, with parameter tuning and early setup compared to tabu search and simulated annealing, and is applicable to various optimization problems through global data structure revision, distributed communication, and probabilistic transitions. Simulation results on the traveling salesman problem demonstrate the effectiveness of AS, and its robustness is shown by successful application to other problems such as asymmetric TSP, quadratic assignment, and job‑shop scheduling.

Abstract

An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.