g-dominance: Reference point based dominance for multiobjective metaheuristics

Molina, Julián; Santana, Luis V.; Hernández-Díaz, Alfredo G.; Coello, Carlos A. Coello; Caballero, Rafael

doi:10.1016/j.ejor.2008.07.015

Cited by 219 publications

(129 citation statements)

References 27 publications

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“…They are simpler and require less expert effort than a priori methods, need moderate computational requirements, and the decision maker can effectively control the search process (Branke et al 2008). As already stated, one of the most extended methods to introduce preferences interactively in MOMHs is using reference points (Molina et al 2009;Ben Said et al 2010;Figueira et al 2010). The advantage of this approach is that it can return an approximation to the Pareto set and not simply projected solutions.…”

Section: The Need For Modelling Interactive Preferencesmentioning

confidence: 99%

“…In one of the latter works, Molina et al (2009) defined a new dominance relation, g-dominance, that can be easily incorporated into different MOMHs. Using g-dominance the search process works without any scalarisation function and the decision maker can interactively set her/his preferences by specifying a reference point.…”

Section: The Need For Modelling Interactive Preferencesmentioning

confidence: 99%

“…Our proposal allows decision makers to guide and interact with the algorithm during the optimisation process. g-dominance (Molina et al 2009) is the preference elicitation method of the framework which has shown a good behaviour in different real-world problems (Rodríguez et al 2012;Greiner et al 2011). In conjunction with g-dominance, we use an existing memetic multiobjective ant colony optimisation (MOACO) approach for the TSALBP.…”

Section: Introductionmentioning

confidence: 99%

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Interactive preferences in multiobjective ant colony optimisation for assembly line balancing

et al. 2014

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In this contribution, we propose an interactive multicriteria optimisation framework for the time and space assembly line balancing problem. The framework allows decision maker interaction by means of reference points to obtain the most interesting non-dominated solutions. The principal components of the framework are the g-dominance preference scheme and a state-of-the-art memetic multiobjective ant colony optimisation approach. In addition, the framework includes a novel adaptive multi-colony mechanism to be able to handle the preferences in an interactive way. Results show how the multiobjective framework can interactively obtain the most useful solutions with higher convergence than previous a priori methods. The experimentation also makes use of original data of the Nissan Pathfinder engine and practical bounds to define industrially feasible solutions in a set of scenarios. By solving the problem in these scenarios, we show the search guidance advantages of Communicated by V. Loia.

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Section: The Need For Modelling Interactive Preferencesmentioning

confidence: 99%

Section: The Need For Modelling Interactive Preferencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interactive preferences in multiobjective ant colony optimisation for assembly line balancing

et al. 2014

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“…As shown in Fig. 5.10, implemented criteria consist of Pareto-dominance, weak-dominance, strict-dominance, ε-dominance [24], and g-dominance [37]. Usually, an archive is used as an external storage only.…”

Section: Elitismmentioning

confidence: 99%

“…In the frame of dominance-based approaches, the most commonly used dominance relation is the Pareto-dominance relation given in Definition 1. But some recent techniques are based on other dominance operators such as ε-dominance in [15] or g-dominance in [37].…”

Section: Fitness Assignmentmentioning

confidence: 99%

ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization

Liefooghe

Jourdan

Legrand

et al. 2010

Advances in Multi-Objective Nature Inspired Computing

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This chapter presents ParadisEO-MOEO, a white-box object-oriented software framework dedicated to the flexible design of metaheuristics for multi-objective optimization. This paradigm-free software proposes a unified view for major evolutionary multi-objective metaheuristics. It embeds some features and techniques for multi-objective resolution and aims to provide a set of classes allowing to ease and speed up the development of computationally efficient programs. It is based on a clear conceptual distinction between the solution methods and the problems they are intended to solve. This separation confers a maximum design and code reuse. This general-purpose framework provides a broad range of fitness assignment strategies, the most common diversity preservation mechanisms, some elitistrelated features as well as statistical tools. Furthermore, a number of state-of-the-art search methods, including NSGA-II, SPEA2 and IBEA, have been implemented in a user-friendly way, based on the fine-grained ParadisEO-MOEO components

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Indicator‐based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index

Trautmann

Wagner

Biermann

et al. 2013

Multi Criteria Decision Anal

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In multiobjective optimization, the identification of practically relevant solutions on the Pareto‐optimal front is an important research topic. Desirability functions (DFs) allow the preferences of the decision maker to be specified in an intuitive way. Recently, it has been shown for continuous optimization problems that an a priori transformation of the objectives by means of DFs can be used to focus the search of a hypervolume‐based evolutionary algorithm on the desired part of the front. In many‐objective optimization, however, the computational complexity of the hypervolume can become a crucial part. Thus, an alternative to this approach will be presented in this paper. The new algorithm operates in the untransformed objective space, but the desirability index (DI), that is, a DF‐based scalarization, will be used as the second‐level selection criterion in the non‐dominated sorting. The diversity and uniform distribution of the resulting approximation are ensured by the use of an external archive. In the experiments, different preferences are specified as DFs, and their effects are investigated. It is shown that trade‐off solutions are generated in the desired regions of the Pareto‐optimal front and with a density adaptive to the DI. The efficiency of the approach with respect to increasing objective space dimension is also analysed using scalable test functions. The convergence speed is superior to other set‐based and preference‐based evolutionary multiobjective algorithms while the approach is of low computational complexity due to cheap DI evaluations. Copyright © 2013 John Wiley & Sons, Ltd.

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g-dominance: Reference point based dominance for multiobjective metaheuristics

Cited by 219 publications

References 27 publications

Interactive preferences in multiobjective ant colony optimisation for assembly line balancing

Interactive preferences in multiobjective ant colony optimisation for assembly line balancing

ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization

Indicator‐based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index

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