Distance-Based Subset Selection for Benchmarking in Evolutionary Multi/Many-Objective Optimization

Singh, Hemant Kumar; Bhattacharjee, Kalyan Shankar; Ray, Tapabrata

doi:10.1109/tevc.2018.2883094

Cited by 71 publications

(18 citation statements)

References 19 publications

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“…After the execution of the selected run, all non-dominated solutions are selected from the stored solutions. Then, a pre-specified number of non-dominated solutions are selected using a distancebased solution selection method of Singh et al [37]. In order to compare the selected solution sets with the results in Section 2, the number of solutions to be selected is specified as the population size in each EMO algorithm.…”

Section: Proposed Emo Frameworkmentioning

confidence: 99%

“…In order to compare the selected solution sets with the results in Section 2, the number of solutions to be selected is specified as the population size in each EMO algorithm. The solution selection method in [37] chooses one extreme non-dominated solution as the first solution, which has the best objective value for a randomly selected one objective function. The non-dominated solution with the largest distance from the first solution is selected as the second solution.…”

Section: Proposed Emo Frameworkmentioning

confidence: 99%

“…In our computational experiments in this paper, we used the distance-based solution selection method in [37]. Whereas it is efficient (i.e., it is fast even for a large solution set), it is not necessarily the best solution selection method with respect to the quality of selected solutions.…”

Section: Solution Selectionmentioning

confidence: 99%

See 2 more Smart Citations

A New Framework of Evolutionary Multi-Objective Algorithms with an Unbounded External Archive

Ishibuchi¹,

Pang²,

Shang³

2020

Preprint

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This paper proposes a new framework for the design of evolutionary multi-objective optimization (EMO) algorithms. The main characteristic feature of the proposed framework is that the optimization result of an EMO algorithm is not the final population but a subset of the examined solutions during its execution. As a post-processing procedure, a pre-specified number of solutions are selected from an unbounded external archive where all the examined solutions are stored. In the proposed framework, the final population does not have to be a good solution set. The point of the algorithm design is to examine a wide variety of solutions over the entire Pareto front and to select well-distributed solutions from the archive. In this paper, first we explain difficulties in the design of EMO algorithms in the existing two frameworks: non-elitist and elitist. Next, we propose the new framework of EMO algorithms. Then we demonstrate advantages of the proposed framework over the existing ones through computational experiments. Finally we suggest some interesting and promising future research topics.

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Section: Proposed Emo Frameworkmentioning

confidence: 99%

Section: Proposed Emo Frameworkmentioning

confidence: 99%

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A New Framework of Evolutionary Multi-Objective Algorithms with an Unbounded External Archive

Ishibuchi¹,

Pang²,

Shang³

2020

Preprint

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show abstract

“…In this problem, a subset is selected from a candidate solution set in order to maximize the hypervolume [11] of the selected subset. Another popular topic is the distancebased subset selection [12], which selects a subset in order to maximize the uniformity level of the selected subset [13]. There are also some other studies on subset selection based on clustering [14], reference vectors [15], and other performance indicators such as IGD [16], ε+ [11] and IGD+ [17].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization

Shang¹,

Shu²,

Ishibuchi³

et al. 2022

Preprint

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In the evolutionary multi-objective optimization (EMO) field, the standard practice is to present the final population of an EMO algorithm as the output. However, it has been shown that the final population often includes solutions which are dominated by other solutions generated and discarded in previous generations. Recently, a new EMO framework has been proposed to solve this issue by storing all the non-dominated solutions generated during the evolution in an archive and selecting a subset of solutions from the archive as the output. The key component in this framework is the subset selection from the archive which usually stores a large number of candidate solutions. However, most studies on subset selection focus on small candidate solution sets for environmental selection. There is no benchmark test suite for large-scale subset selection. This paper aims to fill this research gap by proposing a benchmark test suite for subset selection from large candidate solution sets, and comparing some representative methods using the proposed test suite. The proposed test suite together with the benchmarking studies provides a baseline for researchers to understand, use, compare, and develop subset selection methods in the EMO field.

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“…For instance, -dominance [7] and fuzzy dominance [8] employ modified definitions of dominance to maintain the selection pressure. Different distance measures are used to improve the performance of Pareto dominance-based MaOEAs [9], [10]. Zhang et al employ a knee point-based selection scheme to select nondominated solutions [11].…”

Section: Introductionmentioning

confidence: 99%

A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes

Zhang

et al. 2021

IEEE Trans. Evol. Computat.

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To handle different types of Many-Objective Optimization Problems (MaOPs), Many-Objective Evolutionary Algorithms (MaOEAs) need to simultaneously maintain convergence and population diversity in the high-dimensional objective space. In order to balance the relationship between diversity and convergence, we introduce a Kernel Matrix and probability model called Determinantal Point Processes (DPPs). Our Many-Objective Evolutionary Algorithm with Determinantal Point Processes (MaOEADPPs) is presented and compared with several state-of-the-art algorithms on various types of MaOPs with different numbers of objectives. The experimental results demonstrate that MaOEADPPs is competitive.

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Distance-Based Subset Selection for Benchmarking in Evolutionary Multi/Many-Objective Optimization

Cited by 71 publications

References 19 publications

A New Framework of Evolutionary Multi-Objective Algorithms with an Unbounded External Archive

A New Framework of Evolutionary Multi-Objective Algorithms with an Unbounded External Archive

Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization

A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes

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