On dominance-based multiobjective local search: design, implementation and experimental analysis on scheduling and traveling salesman problems

Liefooghe, Arnaud; Humeau, Jérémie; Mesmoudi, Salma; Jourdan, Laëtitia; Talbi, El‐Ghazali

doi:10.1007/s10732-011-9181-3

Cited by 81 publications

(92 citation statements)

References 42 publications

(99 reference statements)

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“…Liefooghe et al [44,43] study the performance of some variants of the PLS algorithm. They test variants of the selection step that are obtained by restricting the overall exploration with a limit on the number of solutions to be selected, and variants of the neighborhood exploration itself, combining different ways of scanning the neighborhood and different acceptance criteria.…”

Section: Dominance-based Multi-objective Optimizationmentioning

confidence: 99%

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

Dubois-Lacoste

López-Ibáñez

Stützle

2013

Hybrid Metaheuristics

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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.

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Section: Dominance-based Multi-objective Optimizationmentioning

confidence: 99%

“…A possibility is to keep the non-dominated solution found in an external archive and to restart PLS "from scratch" (as done in [43]). There were other extensions that aim at obtaining a more efficient search.…”

Section: Dominance-based Multi-objective Optimizationmentioning

confidence: 99%

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

Dubois-Lacoste

López-Ibáñez

Stützle

2013

Hybrid Metaheuristics

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“…Dominance-based multi-objective local search (DMLS) is a local search algorithm, based on a dominance relation [16]. It needs the definition of a neighborhood function that associates to each solution a set of solutions -called neighborsby applying a small modification on it.…”

Section: Dmls Algorithmmentioning

confidence: 99%

“…Most of multi-objective rule mining contributions presented in the review of Srinivasan and Ramkrishnan are based on the metaheuristic NSGA-II (genetic algorithm dedicated to multi-objective problems) [13]. DMLS has previously proven to give at least as good results as NSGA-II on several problems [16]. Moreover, DMLS is easier to parameter than a genetic algorithm as we only have to define a neighborhood operator.…”

Section: Dmls Algorithmmentioning

confidence: 99%

MOCA-I: Discovering Rules and Guiding Decision Maker in the Context of Partial Classification in Large and Imbalanced Datasets

Jacques

Taillard²,

Delerue³

et al. 2013

Lecture Notes in Computer Science

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“…Regarding stand-alone PLS, the authors identify studies focusing on time-limited experiments [10], on anytime behavior [6] and on how to restart or continue the search after PLS converges [1,9,4]. Regarding PLS as a post optimization procedure, DuboisLacoste et al [5] have proposed algorithms that are currently state-of-the-art for several bi-objective flowshop problems.…”

Section: Algorithm 1 Pareto Local Searchmentioning

confidence: 99%

An Analysis of Local Search for the Bi-objective Bidimensional Knapsack Problem

Bezerra

López-Ibáñez

Stützle

2013

Evolutionary Computation in Combinatorial Optimization

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Abstract. Local search techniques are increasingly often used in multiobjective combinatorial optimization due to their ability to improve the performance of metaheuristics. The efficiency of multi-objective local search techniques heavily depends on factors such as (i) neighborhood operators, (ii) pivoting rules and (iii) bias towards good regions of the objective space. In this work, we conduct an extensive experimental campaign to analyze such factors in a Pareto local search (PLS) algorithm for the bi-objective bidimensional knapsack problem (bBKP). In the first set of experiments, we investigate PLS as a stand-alone algorithm, starting from random and greedy solutions. In the second set, we analyze PLS as a post-optimization procedure.

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On dominance-based multiobjective local search: design, implementation and experimental analysis on scheduling and traveling salesman problems

Abstract: International audienc

Cited by 81 publications

References 42 publications

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

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

MOCA-I: Discovering Rules and Guiding Decision Maker in the Context of Partial Classification in Large and Imbalanced Datasets

An Analysis of Local Search for the Bi-objective Bidimensional Knapsack Problem

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