The r-Dominance: A New Dominance Relation for Interactive Evolutionary Multicriteria Decision Making

Saïd, Lamjed Ben; Bechikh, Slim; Ghédira, Khaled

doi:10.1109/tevc.2010.2041060

Cited by 235 publications

(59 citation statements)

References 23 publications

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“…Although, as the authors state, sharing can be performed based on the objective vectors (Srinivas and Deb 1994), they do not mention how σ share would be selected in that case. Non-dominated sorting is the basis of an improved version of NSGA, namely NSGA-II , which is actively used for benchmaring and improved upon to this day, see (Zhang and Li 2007, Ben Said et al 2010, Bui et al 2012. One problem identified for Pareto-based methods for multi-objective problems, was that good solutions could be lost if they were not retained using some mechanism and the cost to rediscover them can potentially be prohibitive.…”

Section: Pareto-based Methodsmentioning

confidence: 99%

An overview of population-based algorithms for multi-objective optimisation

Giagkiozis

Purshouse

Fleming

2013

International Journal of Systems Science

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In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognized that population-based multi-objective optimization techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimization methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelizable and can generate an approximation of the entire Pareto front (PF) at each iteration. A critique of their capabilities is also provided.

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Section: Pareto-based Methodsmentioning

confidence: 99%

An overview of population-based algorithms for multi-objective optimisation

Giagkiozis

Purshouse

Fleming

2013

International Journal of Systems Science

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“…This means that the DM has to inspect a large set of solutions to find the most preferred solution, requiring both high computational and cognitive efforts. 1 Besides, in EMO algorithms based on the Pareto dominance, the proportion of nondominated objective vectors in the population increases considerably in the presence of many objectives. This fact does not leave much room for new solutions to be included in the population, what may slow down the convergence of the algorithm and may decrease the diversity of the solutions [8,16].…”

Section: Introductionmentioning

confidence: 99%

“…Besides, the DM only has to compare the trade-offs between nondominated objective vectors that are supposed to please him/her better, avoiding to analyse undesirable solutions and reducing the cognitive burden. Surveys of preference-based EMO algorithms can be seen in [7,41], and more recent ones are given in [1,2,28].…”

Section: Introductionmentioning

confidence: 99%

A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm

2014

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When solving multiobjective optimization problems, preference-based evolutionary multiobjective optimization (EMO) algorithms introduce preference information into an evolutionary algorithm in order to focus the search for objective vectors towards the region of interest of the Pareto optimal front. In this paper, we suggest a preference-based EMO algorithm called weighting achievement scalarizing function genetic algorithm (WASF-GA), which considers the preferences of the decision maker (DM) expressed by means of a reference point. The main purpose of WASF-GA is to approximate the region of interest of the Pareto optimal front determined by the reference point, which contains the Pareto optimal objective vectors that obey the preferences expressed by the DM in the best possible way. The proposed approach is based on the use of an achievement scalarizing function (ASF) and on the classification of the individuals into several fronts. At each generation of WASF-GA, this classification is done according to the values that each solution takes on the ASF for the reference point and using different weight vectors. These vectors of weights are selected so that the vectors formed by their inverse components constitute a well-distributed representation of the weight vectors space. The efficiency and usefulness of WASF-GA is shown in several test problems in comparison to other preference-based EMO algorithms. Regarding a metric based on the hypervolume, we can say that WASF-GA has outperformed the other algorithms considered in most of the problems.

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“…Preferences are often expressed as reference points, goal vectors or aspiration levels corresponding to desired levels of objective values. The dominance relation is modified according to the distance to the reference point in [19] or aspiration level satisfaction in [20]. An achievement scalarizing function taking into account reference point is used to prefer some solutions closer to RoI in [21]- [24].…”

Section: Introductionmentioning

confidence: 99%

Preference-Based Evolutionary Algorithm for Airport Runway Scheduling and Ground Movement Optimisation

Weiszer

Stewart

2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

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Abstract-As airports all over the world are becoming more congested together with stricter environmental regulations put in place, research on optimisation of airport surface operations started to consider both time and fuel related objectives. However, as both time and fuel can have a monetary cost associated with them, this information can be utilised as preference during the optimisation to guide the search process to a region with the most cost efficient solutions. In this paper, we solve the integrated optimisation problem combining runway scheduling and ground movement problem by using a multi-objective evolutionary framework. The proposed evolutionary algorithm is based on modified crowding distance and outranking relation which considers cost of delay and price of fuel. Moreover, the preferences are expressed in a such way, that they define a certain range in prices reflecting uncertainty. The preliminary results of computational experiments with data from a major airport show the efficiency of the proposed approach.

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The r-Dominance: A New Dominance Relation for Interactive Evolutionary Multicriteria Decision Making

Cited by 235 publications

References 23 publications

An overview of population-based algorithms for multi-objective optimisation

An overview of population-based algorithms for multi-objective optimisation

A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm

Preference-Based Evolutionary Algorithm for Airport Runway Scheduling and Ground Movement Optimisation

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