On the Design of ACO for the Biobjective Quadratic Assignment Problem

López-Ibáñez, Manuel; Paquete, Luís; Stützle, Thomas

doi:10.1007/978-3-540-28646-2_19

Cited by 44 publications

(17 citation statements)

References 16 publications

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“…Moreover, all the algorithms reviewed above use all weights available in each iteration (allweights-per-iteration). In our earlier work for the bi-objective QAP [8], we proposed that all ants use the same weight in one iteration, and the next weight in the sequence in the next iteration (one-weight-per-iteration). Therefore, we can easily construct new variants of most algorithms in Table II.…”

Section: J New Design Alternativesmentioning

confidence: 99%

“…2011.2182651 Compared to the substantial amount of research on evolutionary computation and local search algorithms for tackling multi-objective optimization problems, there are relatively few works on MOACO algorithms. Most articles propose only one specific MOACO algorithm [5,6]; rare are studies that compare a few MOACO design alternatives [7][8][9]. A first review of existing MOACO algorithms included about ten MOACO algorithms [4].…”

Section: Introductionmentioning

confidence: 99%

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The Automatic Design of Multiobjective Ant Colony Optimization Algorithms

López-Ibáñez

Stützle

2012

IEEE Trans. Evol. Computat.

166

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Abstract-Multi-objective optimization problems are problems with several, typically conflicting criteria for evaluating solutions. Without any a priori preference information, the Pareto optimality principle establishes a partial order among solutions, and the output of the algorithm becomes a set of nondominated solutions rather than a single one. Various ant colony optimization (ACO) algorithms have been proposed in recent years for solving such problems. These multi-objective ACO (MOACO) algorithms exhibit different design choices for dealing with the particularities of the multi-objective context. This paper proposes a formulation of algorithmic components that suffices to describe most MOACO algorithms proposed so far. This formulation also shows that existing MOACO algorithms often share equivalent design choices but they are described in different terms. Moreover, this formulation is synthesized into a flexible algorithmic framework, from which not only existing MOACO algorithms may be instantiated, but also combinations of components that were never studied in the literature. In this sense, this paper goes beyond proposing a new MOACO algorithm, but it rather introduces a family of MOACO algorithms.The flexibility of the proposed MOACO framework facilitates the application of automatic algorithm configuration techniques. The experimental results presented in this paper show that the automatically configured MOACO framework outperforms the MOACO algorithms that inspired the framework itself. This paper is also among the first to apply automatic algorithm configuration techniques to multi-objective algorithms.

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Section: J New Design Alternativesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Automatic Design of Multiobjective Ant Colony Optimization Algorithms

López-Ibáñez

Stützle

2012

IEEE Trans. Evol. Computat.

166

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“…Few of them examine alternative design choices for the algorithm components of the proposed MOACO algorithms (Iredi et al, 2001;López-Ibáñez et al, 2004;Alaya et al, 2007). In addition, two articles that review MOACO algorithms from different angles have been published.…”

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confidence: 99%

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

López-Ibáñez

Stützle

2012

Swarm Intell

Self Cite

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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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“…Famous NP-hard problems that are frequently studied in researches include the travelling salesman problem (TSP) [2], the assignment problem (AP) [54] or the knapsack problem (KP) [76].…”

Section: Solving Combinatorial Optimisation Problemsmentioning

confidence: 99%

Multi-objective Ant Colony Optimisation in Wireless Sensor Networks

Kellner

2017

Nature-Inspired Computing and Optimization

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Biologically inspired ant colony optimisation (ACO) has been used in several applications to solve NP-hard combinatorial optimisation problems. An interesting area of application for ACO-based algorithms is their use in wireless sensor networks (WSNs). Due to their robustness and self-organisation, ACO-based algorithms are well-suited for the distributed, autonomous and self-organising structure of WSNs. While the original ACO-based algorithm and its direct descendants can take only one objective into account, multi-objective ant colony optimisation (MOACO) is capable of considering multiple (conflicting) objectives simultaneously. In this chapter, a detailed review and summary of MOACO-based algorithms and their applications in WSNs is given. In particular, a taxonomy of MOACO-based algorithms is presented and their suitability for multi-objective combinatorial optimisation problems in WSNs is highlighted.

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On the Design of ACO for the Biobjective Quadratic Assignment Problem

Cited by 44 publications

References 16 publications

The Automatic Design of Multiobjective Ant Colony Optimization Algorithms

The Automatic Design of Multiobjective Ant Colony Optimization Algorithms

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

Multi-objective Ant Colony Optimisation in Wireless Sensor Networks

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