A review of multiobjective test problems and a scalable test problem toolkit

Huband, S.; Hingston, Philip; Barone, Luigi; While, Lyndon

doi:10.1109/tevc.2005.861417

Cited by 1,702 publications

(853 citation statements)

References 42 publications

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“…To evaluate the performance of the EMO algorithm using the distance metric, we used following test problem suits; ZDT [11] for two-objective problems, WFG [12] and DTLZ [13] for two and up to ten objective problems. These test problem suites contain many varieties of multi-objective problems including some with many local optima fronts (multi-modal).…”

Section: Methodsmentioning

confidence: 99%

A Distance Metric for Evolutionary Many-Objective Optimization Algorithms Using User-Preferences

Wickramasinghe

2009

AI 2009: Advances in Artificial Intelligence

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Abstract. In this paper we propose to use a distance metric based on user-preferences to efficiently find solutions for many-objective problems. In a user-preference based algorithm a decision maker indicates regions of the objective-space of interest, the algorithm then concentrates only on those regions to find solutions. Existing user-preference based evolutionary many-objective algorithms rely on the use of dominance comparisons to explore the search-space. Unfortunately, this is ineffective and computationally expensive for many-objective problems. The proposed distance metric allows an evolutionary many-objective algorithm's search to be focused on the preferred regions, saving substantial computational cost. We demonstrate how to incorporate the proposed distance metric with a user-preference based genetic algorithm, which implements the reference point and light beam search methods. Experimental results suggest that the distance metric based algorithm is effective and efficient, especially for difficult many-objective problems.

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Section: Methodsmentioning

confidence: 99%

A Distance Metric for Evolutionary Many-Objective Optimization Algorithms Using User-Preferences

Wickramasinghe

2009

AI 2009: Advances in Artificial Intelligence

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“…We perform experiments on two well-known sets of benchmark problems: DTLZ1-DTLZ4 [10] and WFG1-WFG4 [11]. For all problems, the number of decision variables is scaled as l = 40, 80, 160, and 320.…”

Section: Benchmark and Experiments Setting 41 Benchmark Problemsmentioning

confidence: 99%

“…WFG2-3 are non-separable problems [11], i.e., their decision variables exhibit certain dependencies that need to be properly handled during solution variations so that the problem instances…”

Section: Moea/d-2tchmfimentioning

confidence: 99%

Improving the performance of MO-RV-GOMEA on problems with many objectives using tchebycheff scalarizations

Luong

Alderliesten

Bosman

2018

Proceedings of the Genetic and Evolutionary Computation Conference

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The Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) has been shown to exhibit excellent performance in solving various bi-objective benchmark and real-world problems. We assess the competence of MO-RV-GOMEA in tackling many-objective problems, which are normally defined as problems with at least four conflicting objectives. Most Pareto dominance-based Multi-Objective Evolutionary Algorithms (MOEAs) typically diminish in performance if the number of objectives is more than three because selection pressure toward the Pareto-optimal front is lost. This is potentially less of an issue for MO-RV-GOMEA because its variation operator creates each offspring solution by iteratively altering a currently existing solution in a few decision variables each time, and changes are only accepted if they result in a Pareto improvement. For most MOEAs, integrating scalarization methods is potentially beneficial in the many-objective context. Here, we investigate the possibility of improving the performance of MO-RV-GOMEA by further guiding improvement checks during solution variation in MO-RV-GOMEA with carefully constructed Tchebycheff scalarizations. Results obtained from experiments performed on a selection of well-known problems from the DTLZ and WFG test suites show that MO-RV-GOMEA is by design already well-suited for many-objective problems. Moreover, by enhancing it with Tchebycheff scalarizations, it outperforms MOEA/D-2TCHMFI, a state-of-the-art decompositionbased MOEA. CCS CONCEPTS

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“…The Walking Fish Group (WFG) problem toolkit [37] is a toolkit for creating complex synthetic multi-objective test problems. The WFG test suite exceeds the functionality of previous existing test suites.…”

Section: Experimental Studymentioning

confidence: 99%

Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

Martí

García

Berlanga

et al. 2011

Lecture Notes in Computer Science

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Abstract. The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have a intrinsic shortcoming in their model-building algorithms that hamper their performance.In this work we argue that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and an hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.

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A review of multiobjective test problems and a scalable test problem toolkit

Cited by 1,702 publications

References 42 publications

A Distance Metric for Evolutionary Many-Objective Optimization Algorithms Using User-Preferences

A Distance Metric for Evolutionary Many-Objective Optimization Algorithms Using User-Preferences

Improving the performance of MO-RV-GOMEA on problems with many objectives using tchebycheff scalarizations

Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

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