A Concise Overview of Applications of Ant Colony Optimization

Abstract

Ant colony optimization (ACO) [1-3] is a metaheuristic for solving hard combinatorial optimization problems inspired by the indirect communication of real ants. In ACO algorithms, (artificial) ants construct candidate solutions to the problem being tackled, making decisions that are stochastically biased by numerical information based on (artificial) pheromone trails and available heuristic information. The pheromone trails are updated during algorithm execution to bias the ants search toward promising decisions previously found. The article titled Ant Colony Optimization gives a detailed overview of the main concepts of ACO.Despite being one of the youngest metaheuristics, the number of applications of ACO algorithms is very large. In principle, ACO can be applied to any combinatorial optimization problem for which some iterative solution construction mechanism can be conceived. Most applications of ACO deal with NP-hard combinatorial optimization problems, that is, with problems for which no polynomial time algorithms are known. ACO algorithms have also been extended to handle problems with multiple objectives, stochastic data, and dynamically changing problem information. There are extensions of the ACO metaheuristic for dealing with problems with continuous decision variables, as well.This article provides a concise overview of several noteworthy applications of ACO algorithms. This overview is necessarily incomplete because the number of currently available ACO applications goes into the hundreds. Our description of the applications

The Automatic Design of Multiobjective Ant Colony Optimization Algorithms

“…These test instances are taken from Luis Paquete's webpage at http: //eden.dei.uc.pt/ ∼ paquete/tsp. 2 Each experiment, that is, each run of the MOACO framework on each instance, is stopped after 300 · (n/100) 2 CPU-seconds. As the underlying ACO algorithm, we use MMAS as defined for the TSP [11].…”

“…ACO was originally designed for solving single-objective combinatorial optimization problems. Due to notable results on these problems, ACO algorithms were soon extended to tackle problems with more complex features [2] and, in particular, multiple objective functions [3,4]. The majority of these multi-objective ACO (MOACO) algorithms focus on problems in terms of Pareto optimality, that is, they do not make a priori assumptions about the decision maker's preferences.…”

Towards an Integration of Systems Engineering and Project Management Processes for a Decision Aiding Purpose

“…This section gives a brief description of the multi-objective ACO (MOACO) (Dorigo et al (2006) and Stützle et al (2011)) algorithm that constitutes our decision-aided tool (Fig. 1).…”

A risk-based approach applied to system engineering projects: A new learning based multi-criteria decision support tool based on an Ant Colony Algorithm

“…It allows the generation of Pareto-optimal scenarios from the resulting integrated project graph that encompasses all the design and the project alternative choices of a new system to conceive and realize. For this matter, the standard Ant Colony Optimization (ACO) meta-heuristic (Dorigo and Stützle, 2010;Stützle et al, 2011) was adopted and adapted to develop a multi-objective new ant colony algorithm based on a learning mechanism denoted as MONACO algorithm.…”