Analyzing Limited Size Archivers of Multi-objective Optimizers

Medeiros, Hudson Geovane de; Goldbarg, Elizabeth Ferreira Gouvêa; Goldbarg, Marco César

doi:10.1109/bracis.2014.26

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…This study investigated methods to recycle nondominated solutions discarded by multi-objective optimizers. This investigation was motivated by the conclusions of previous studies presented by [8] and [9] which concluded that most filtering techniques used by multi-objective algorithms might deteriorate (or present other significant problems). Thus, this paper presented methods to prevent deterioration and improve the quality of the approximation sets generated by multiobjective optimizers.…”

Section: Discussionmentioning

confidence: 99%

“…Many techniques for f ilter() were proposed in the literature. They were firstly analyzed by [8] and later by [9]. These analyses were concerned about desirable properties of the archivers and experimental results regarding quality indicators.…”

Section: Algorithm 1 Basic Archiver Templatementioning

confidence: 99%

“…Distance Archiver had poor results for this indicator, as well. Its strategy may lead the archive to -deteriorate easily, as shown by [9]. Table 5 presents the times spent by each archiving technique to process the input sequences.…”

Section: Experiments With Fixed Input Sequences Of Pointsmentioning

confidence: 99%

“…We investigate three recycling methods which were tested with three evolutionary algorithms proposed for multiobjective problems: NSGAII, SPEA2, and PAES. These algorithms were chosen because they are widely used in the literature, and their archiving techniques were analyzed previously on [8] and [9]. These optimizers and their versions implemented with the recycling techniques are applied to discrete and continuous problems.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of Archiving Techniques for Evolutionary Multi-objective Optimizers

Medeiros

Goldbarg

2018

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The optimization of multi-objective problems from the Pareto dominance viewpoint can lead to huge sets of incomparable solutions. Many heuristic techniques proposed to these problems have to deal with approximation sets that can be limited or not. Usually, a new solution generated by a heuristic is compared with other archived non-dominated solutions generated previously. Many techniques deal with limited size archives, since comparisons within unlimited archives may require significant computational effort. To maintain limited archives, solutions need to be discarded. Several techniques were proposed to deal with the problem of deciding which solutions remain in the archive and which are discarded. Previous investigations showed that those techniques might not prevent deterioration of the archives. In this study, we propose to store discarded solutions in a secondary archive and, periodically, recycle them, bringing them back to the optimization process. Three recycling techniques were investigated for three known methods. The datasets for the experiments consisted of 91 instances of discrete and continuous problems with 2, 3 and 4 objectives. The results showed that the recycling method can benefit the tested optimizers on many problem classes.

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Section: Discussionmentioning

confidence: 99%

Section: Algorithm 1 Basic Archiver Templatementioning

confidence: 99%

Section: Experiments With Fixed Input Sequences Of Pointsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigation of Archiving Techniques for Evolutionary Multi-objective Optimizers

Medeiros

Goldbarg

2018

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“…More recent studies have focused on the convergence of archiving algorithms with respect to specific quality indicators such as the hypervolume [14,44], ϵ-box [45] and Hausdorff metrics [46][47][48] (see [49] for a review study). In addition, several studies have empirically investigated archivers in isolation using artificial sequences of solutions [4,10,50], using solution neighborhoods in combinatorial problems [51] and the practical effectiveness of satisfying theoretical properties [19].…”

Section: A Brief Historymentioning

confidence: 99%