A Survey of Diversity Oriented Optimization: Problems, Indicators, and Algorithms

Basto‐Fernandes, Vitor; Yevseyeva, Iryna; Deutz, André H.; Emmerich, Michael

doi:10.1007/978-3-319-49325-1_1

Cited by 15 publications

(9 citation statements)

References 34 publications

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“…Moreover, we implement IMIA using the multi-core parallel scheme to reduce its computational cost. 2) We show that due to the cooperation of multiple IB-MOEAs, IMIA can perform more robustly (under seven quality indicators: HV, R2, IGD + , + , ∆ p , Riesz senergy, and the Solow-Polasky Diversity [26]) than the panmictic versions of its baseline IB-MOEAs. In this regard, we define a robust performance as the capacity of a MOEA to consistently obtain the best results under several QIs (measuring convergence and diversity) for MOPs with different Pareto front shapes and scaling the dimensionality of the objective space.…”

Section: Introductionmentioning

confidence: 85%

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On the Effect of the Cooperation of Indicator-Based Multiobjective Evolutionary Algorithms

Falcón-Cardona

Ishibuchi

Coello

et al. 2021

IEEE Trans. Evol. Computat.

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For almost 20 years, quality indicators (QIs) have promoted the design of new selection mechanisms of multiobjective evolutionary algorithms (MOEAs). Each indicatorbased MOEA (IB-MOEA) has specific search preferences related to its baseline QI, producing Pareto front approximations with different properties. In consequence, an IB-MOEA based on a single QI has a limited scope of multi-objective optimization problems (MOPs) in which it is expected to have a good performance. This issue is emphasized when the associated Pareto front geometries are highly irregular. In order to overcome these issues, we propose here an island-based multi-indicator algorithm (IMIA) that takes advantage of the search biases of multiple IB-MOEAs through a cooperative scheme. Our experimental results show that the cooperation of multiple IB-MOEAs allows IMIA to perform more robustly (considering several QIs) than the panmictic versions of its baseline IB-MOEAs as well as several stateof-the-art MOEAs. Additionally, IMIA shows a Pareto-frontshape invariance property which makes it a remarkable optimizer when tackling MOPs with complex Pareto front geometries.

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Section: Introductionmentioning

confidence: 85%

“…According to Basto-Fernandes et al [26], SPD(A) tends to N if the distance between all species tends to be very large. In contrast, SPD(A) tends to one if species are very similar with respect to each other.…”

Section: Introductionmentioning

confidence: 99%

On the Effect of the Cooperation of Indicator-Based Multiobjective Evolutionary Algorithms

Falcón-Cardona

Ishibuchi

Coello

et al. 2021

IEEE Trans. Evol. Computat.

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“…It is focused on describing the properties of algorithms, which can be especially helpful for teaching EA-related topics. In the domain of multiobjective optimization, the Diversity-Oriented Optimization Ontology has been developed, including a taxonomy of algorithms concerning the diversity concept in different search operators [32]. Complementary to the diversity concept, the Preference-based Multi-Objective Ontology (PMOEAs) has also been proposed to model the knowledge about preferencebased multi-objective evolutionary algorithms [33].…”

Section: Ontologies For Optimization and Evolutionary Computingmentioning

confidence: 99%

OPTION: OPTImization Algorithm Benchmarking ONtology

Kostovska

Vermetten²,

Doerr³

et al. 2023

IEEE Trans. Evol. Computat.

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Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research. However, different platforms use different data models and formats, which drastically complicates the identification of relevant datasets, their interpretation, and their interoperability. Therefore, a semantically rich, ontology-based, machine-readable data model that can be used by different platforms is highly desirable. In this paper, we report on the development of such an ontology, which we call OPTION (OPTImization algorithm benchmarking ONtology). Our ontology provides the vocabulary needed for semantic annotation of the core entities involved in the benchmarking process, such as algorithms, problems, and evaluation measures. It also provides means for automatic data integration, improved interoperability, and powerful querying capabilities, thereby increasing the value of the benchmarking data. We demonstrate the utility of OPTION, by annotating and querying a corpus of benchmark performance data from the BBOB collection of the COCO framework and from the Yet Another Black-Box Optimization Benchmark (YABBOB) family of the Nevergrad environment. In addition, we integrate features of the BBOB functional performance landscape into the OPTION knowledge base using publicly available datasets with exploratory landscape analysis. Finally, we integrate the OPTION knowledge base into the IOHprofiler environment and provide users with the ability to perform meta-analysis of performance data.

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“…The first difficulty also leads to the diversity evaluation becoming computationally expensive and the recombination operator becoming inefficient. To address the diversity issue many diversity-oriented algorithms have been developed [5].…”

Section: A2 Multi-objective and Many-objective Optimizationmentioning

confidence: 99%

Designing Strong Privacy Metrics Suites Using Evolutionary Optimization

Wagner

Yevseyeva

2021

ACM Trans. Priv. Secur.

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The ability to measure privacy accurately and consistently is key in the development of new privacy protections. However, recent studies have uncovered weaknesses in existing privacy metrics, as well as weaknesses caused by the use of only a single privacy metric. Metrics suites, or combinations of privacy metrics, are a promising mechanism to alleviate these weaknesses, if we can solve two open problems: which metrics should be combined and how. In this article, we tackle the first problem, i.e., the selection of metrics for strong metrics suites, by formulating it as a knapsack optimization problem with both single and multiple objectives. Because solving this problem exactly is difficult due to the large number of combinations and many qualities/objectives that need to be evaluated for each metrics suite, we apply 16 existing evolutionary and metaheuristic optimization algorithms. We solve the optimization problem for three privacy application domains: genomic privacy, graph privacy, and vehicular communications privacy. We find that the resulting metrics suites have better properties, i.e., higher monotonicity, diversity, evenness, and shared value range, than previously proposed metrics suites.

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A Survey of Diversity Oriented Optimization: Problems, Indicators, and Algorithms

Cited by 15 publications

References 34 publications

On the Effect of the Cooperation of Indicator-Based Multiobjective Evolutionary Algorithms

On the Effect of the Cooperation of Indicator-Based Multiobjective Evolutionary Algorithms

OPTION: OPTImization Algorithm Benchmarking ONtology

Designing Strong Privacy Metrics Suites Using Evolutionary Optimization

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