Solving Multi-criteria Optimization Problems with Population-Based ACO

Guntsch, Michael; Middendorf, Martin

doi:10.1007/3-540-36970-8_33

Cited by 44 publications

(37 citation statements)

References 11 publications

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“…We focus on MOACO algorithms that have been designed with Pareto optimization in mind. Hence, we exclude from our analysis a number of algorithms that were designed for lexicographic optimization (Mariano and Morales, 1999;Gambardella et al, 1999;Gravel et al, 2002), or algorithms that diverge from the basic structure of the ACO metaheuristic, such as population-based ACO (Guntsch and Middendorf, 2003;Angus, 2007). We also exclude from our review the adaptation of MOAQ to the bTSP (García-Martínez et al, 2007), since we have already shown that its results are extremely poor in comparison with the other algorithms (López-Ibáñez and Stützle, 2010b), and it does not contribute to our discussion.…”

Section: Moaco Algorithmsmentioning

confidence: 99%

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

López-Ibáñez

Stützle

2012

Swarm Intell

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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.

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Section: Moaco Algorithmsmentioning

confidence: 99%

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

López-Ibáñez

Stützle

2012

Swarm Intell

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“…So far, few approaches of ACO algorithms to MCOPs defined in terms of Pareto optimality have been proposed [2,3,4]. (For a concise overview of ACO approaches to MCOPs we refer to [1].)…”

Section: Introductionmentioning

confidence: 99%

On the Design of ACO for the Biobjective Quadratic Assignment Problem

López-Ibáñez

Paquete

Stützle

2004

Ant Colony Optimization and Swarm Intelligence

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Abstract. Few applications of ACO algorithms to multiobjective problems have been presented so far and it is not clear how to design an effective ACO algorithms for such problems. In this article, we study the performance of several ACO variants for the biobjective Quadratic Assignment Problem that are based on two fundamentally different search strategies. The first strategy is based on dominance criteria, while the second one exploits different scalarizations of the objective function vector. Further variants differ in the use of multiple colonies, the use of local search, and the pheromone update strategy. The experimental results indicate that the use of local search procedures and the correlation between objectives play an essential role in the performance of the variants studied in this paper.

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“…In the models proposed in [106], it is found that a factorization based on clusters in the objective space is necessary to obtain a good spread across the Pareto front. This results in an algorithm that is quite similar to the population-based ACO [47], described below, except that here the model is based only on the current population and not on a selection from a store of all nondominated solutions. The approach of [80] is a little different: instead of a mixture of clustered univariate distributions, a binary decision tree is used to model the conditional probabilities of good solution components.…”

Section: Model-based Searchers Using Dominance Rankingmentioning

confidence: 96%

“…Whenever a solution in the population is replaced by a new one, the pheromone trails associated with the old one are entirely removed from the construction graph, and the new member of the population deposits its pheromone instead. In [47], population-based ACO is adapted to the multiobjective case. This is achieved by making use of a store of all nondominated solutions found, and periodically choosing a subset of this to act as a temporary population.…”

Section: Model-based Searchers Using Dominance Rankingmentioning

confidence: 99%

Memetic Algorithms for Multiobjective Optimization: Issues, Methods and Prospects

Knowles¹,

Corne

Studies in Fuzziness and Soft Computing

100

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Summary. The concept of optimization-finding the extrema of a function that maps candidate 'solutions' to scalar values of 'quality'-is an extremely general and useful idea that can be, and is, applied to innumerable problems in science, industry, and commerce. However, the vast majority of 'real' optimization problems, whatever their origins, comprise more than one objective; that is to say, 'quality' is actually a vector, which may be composed of such distinct attributes as cost, performance, profit, environmental impact, and so forth, which are often in mutual conflict. Until relatively recently this uncomfortable truth has been (wilfully?) overlooked in the sciences dealing with optimization, but now, increasingly, the idea of multiobjectiue optimization is taking over the centre ground. Multiobjective optimization takes seriously the fact that in most problems the different components that describe the quality of a candidate solution cannot be lumped together into one representative, overall measure, a t least not easily, and not before some understanding of the possible 'tradeoffs' available has been established. Hence a multiobjective optimization algorithm is one which deals directly with a vector objective function and seeks to find multiple solutions offering different, optimal tradeoffs of the multiple objectives. This approach raises several unique issues in optimization algorithm design, which we consider in this article, with a particular focus on memetic algorithms (MAS). We summarize much of the relevant literature, attempting to be inclusive of relatively unexplored ideas, highlight the most important considerations for the design of multiobjective MAS, and finally outline our visions for future research in this exciting area.

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Solving Multi-criteria Optimization Problems with Population-Based ACO

Cited by 44 publications

References 11 publications

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

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

On the Design of ACO for the Biobjective Quadratic Assignment Problem

Memetic Algorithms for Multiobjective Optimization: Issues, Methods and Prospects

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