A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP

2012

Swarm Intell

There have been several proposals on how to apply the ant colony optimization (ACO) metaheuristic to multi-objective combinatorial optimization problems (MOCOPs). This paper proposes a new formulation of these multiobjective ant colony optimization (MOACO) algorithms. This formulation is based on adding algorithm components specific for tackling multiple objectives to the basic ACO metaheuristic. Examples of these components are how to represent multiple objectives with pheromone and heuristic information, how to select the best solutions for updating the pheromone information, and how to define and use weights to aggregate the different objectives. This formulation reveals more similarities than previously thought in the design choices made in existing MOACO algorithms. The main contribution of this paper is an experimental analysis of how particular design choices affect the quality and the shape of the Pareto front approximations generated by each MOACO algorithm. This study provides general guidelines to understand how MOACO algorithms work, and how to improve their design.

Section: Multi-objective Ant Colony Optimizationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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An experimental analysis of design choices of multi-objective ant colony optimization algorithms

2012

Swarm Intell

“…An early example is VEGA [58]; other examples include the algorithms proposed by Ishibuchi and Murata [38] and MOGLS of Jaszkiewicz [39]. Also ACO algorithms frequently use some form of scalarized aggregation, for example, for combining pheromone (or heuristic) information specific to each objective [5,28,47]. However, an overview of such population-based methods is beyond the scope of this chapter.…”

Section: Scalarization-based Multi-objective Optimizationmentioning

confidence: 99%

“…We restrict our discussion to methods that are based on the iterative improvement of the set of non-dominated solutions by performing local search (or mutation) of solutions one at a time. We do not consider here population-based algorithms such as multiobjective evolutionary algorithms [10,8] or multi-objective ant colony optimization algorithms [5,28]. However, it should be noted that these algorithms also often make direct or indirect use of Pareto dominance for directing the search, in particular, in acceptance or selection decisions on solutions.…”

Section: Dominance-based Multi-objective Optimizationmentioning

confidence: 99%

Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search

Dubois-Lacoste

2013

Hybrid Metaheuristics

In this chapter, we review metaheuristics for solving multi-objective combinatorial optimization problems, when no information about the decision maker's preferences is available, that is, when problems are tackled in the sense of Pareto optimization. Most of these metaheuristics follow one of the two main paradigms to tackle such problems using metaheuristics. The first paradigm is to rely on Pareto dominance when exploring the search space. The second paradigm is to tackle several single-objective problems to find several solutions that are non-dominated for the original problem; in this case, one may exploit existing, efficient single-objective algorithms, but the performance depends on the definition of the set of scalarized problems. There are also a number of approaches in the literature that combine both paradigms. However, this is usually done in a relatively ad-hoc way. In this paper, we review two conceptually simple methods representative of each paradigm: Pareto local search and Two-phase Local Search. The hybridization of these two strategies provides a general framework for engineering stochastic local search algorithms that can be used to improve over the state-of-the-art for several, widely studied problems.

A Concise Overview of Applications of Ant Colony Optimization

Wiley Encyclopedia of Operations Research and Management Science

Dorigo

2011

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