Constrained Bi-objective Surrogate-Assisted Optimization of Problems with Heterogeneous Evaluation Times: Expensive Objectives and Inexpensive Constraints

Blank, Julian; Deb, Kalyanmoy

doi:10.1007/978-3-030-72062-9_21

Cited by 16 publications

(5 citation statements)

References 21 publications

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“…In these experiments, two variants of the SAMO-COBRA algorithm are tested, one without the uncertainty quantification method (PHV), and one with the uncertainty quantification method (SMS). The performance of the two variants are compared to the performance of the following algorithms: CEGO [42], IC-SA-NSGA-II [8], SA-NSGA-II [8], NSGA-II [14], NSGA-III [24], and SMES-RBF [11]. The performance of the algorithms except for SMES-RBF are assessed on 18 academic benchmark functions.…”

Section: Methodsmentioning

confidence: 99%

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Constrained Multi-Objective Optimization with a Limited Budget of Function Evaluations

Winter

Bronkhorst²,

Stein

et al. 2022

Memetic Comp.

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This paper proposes the Self-Adaptive algorithm for Multi-Objective Constrained Optimization by using Radial Basis Function Approximations, SAMO-COBRA. This algorithm automatically determines the best Radial Basis Function-fit as surrogates for the objectives as well as the constraints, to find new feasible Pareto-optimal solutions. SAMO-COBRA is compared to a wide set of other state-of-the-art algorithms (IC-SA-NSGA-II, SA-NSGA-II, NSGA-II, NSGA-III, CEGO, SMES-RBF) on 18 constrained multi-objective problems. In the first experiment, SAMO-COBRA outperforms the other algorithms in terms of achieved Hypervolume (HV) after being given a fixed small evaluation budget on the majority of test functions. In the second experiment, SAMO-COBRA outperforms the majority of competitors in terms of required function evaluations to achieve $$95\%$$ 95 % of the maximum achievable Hypervolume. In addition to academic test functions, SAMO-COBRA has been applied on a real-world ship design optimization problem with three objectives, two complex constraints, and five decision variables.

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Section: Methodsmentioning

confidence: 99%

“…Only very occasionally a surrogate-based algorithm is published that deals with both constraints and multiple objectives in an effective manner without using a Kriging surrogate (e.g., Datta's and Regis' SMES-RBF [11], Blank and Deb's SA-NSGA-II [8], and Blank's and Deb's IC-SA-NSGA-II [8]).…”

Section: Related Workmentioning

confidence: 99%

Constrained Multi-Objective Optimization with a Limited Budget of Function Evaluations

Winter

Bronkhorst²,

Stein

et al. 2022

Memetic Comp.

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“…However, real-world problems might exhibit a significant variability in the objectives' characteristics, and recent research has addressed the question of heterogeneous objectives and proposed multi-objective approaches to deal with them [6]. Much of this previous research has been, however, focused on problems where the heterogeneity arises in evaluation times or latencies, that is, when each objective takes a different amount of time to be evaluated [3,5,9,11].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective NK landscapes with heterogeneous objectives

Cosson

Santana

Derbel

et al. 2022

Proceedings of the Genetic and Evolutionary Computation Conference

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…The calculation of the volumetric objective and constraints are inexpensive to evaluate, while the regulatory damage stability constraint and objective are time-consuming to evaluate. To speed up this process, three design variants can be evaluated in parallel with the commercial DELFTship software.Problems like these are typically optimized with surrogate-assisted genetic algorithms like Surrogate Assisted Non-Dominated Sorting Genetic Algorithm (SA-NSGA-II) [2] or with Bayesian optimization algorithms like the Self Adapting Multi-Objective Constraint Optimization algorithm by using Radial Basis function Approximations (SAMO-COBRA) [3]. However, these algorithms by default use surrogates for both the inexpensive and the expensive functions.…”

mentioning

confidence: 99%

“…Problems like these are typically optimized with surrogate-assisted genetic algorithms like Surrogate Assisted Non-Dominated Sorting Genetic Algorithm (SA-NSGA-II) [2] or with Bayesian optimization algorithms like the Self Adapting Multi-Objective Constraint Optimization algorithm by using Radial Basis function Approximations (SAMO-COBRA) [3]. However, these algorithms by default use surrogates for both the inexpensive and the expensive functions.…”

mentioning

confidence: 99%

Parallel Constrained Multi-Objective Optimization for Ship Design Damage Stability Problem with (In)Expensive Function Evaluations

Winter¹

2023

10th Conference on Computational Methods in Marine Engineering

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Parametric ship design optimization is seen as a challenging task due to complex problem characteristics. Problems often have multiple decision variables, multiple expensive to evaluate conflicting objectives, and are subject to regulatory and volumetric constraints. One example of such a problem has been encountered at C-Job Naval Architects when optimizing the internal layout of a single-hold general cargo vessel [1]. The two objectives of the study are maximization of the survivability of the vessel while simultaneously maximizing the cargo hold volume. The problem has three volumetric constraints, and one regulatory constraint for the required damage stability index. The internal layout of the vessel is modified with 17 variables that influence the positioning of the watertight bulkheads in the transverse, longitudinal, and vertical directions and the location of openings/hatches/vents. The calculation of the volumetric objective and constraints are inexpensive to evaluate, while the regulatory damage stability constraint and objective are time-consuming to evaluate. To speed up this process, three design variants can be evaluated in parallel with the commercial DELFTship software.Problems like these are typically optimized with surrogate-assisted genetic algorithms like Surrogate Assisted Non-Dominated Sorting Genetic Algorithm (SA-NSGA-II) [2] or with Bayesian optimization algorithms like the Self Adapting Multi-Objective Constraint Optimization algorithm by using Radial Basis function Approximations (SAMO-COBRA) [3]. However, these algorithms by default use surrogates for both the inexpensive and the expensive functions. Since surrogates are approximations of the real objective or constraint functions they make mistakes. To deal with this issue, the SAMO-COBRA algorithm and the SA-NSGA-II algorithm have been extended with an option to directly use the inexpensive functions from the optimization problem.In this study, SAMO-COBRA and SA-NSGA-II with their extensions, have been tested and compared on a variety of artificially created and real-world-like test functions. After finding a good strategy, the algorithms have been used to optimize the damage stability optimization problem of C-Job Naval Architects. The results show that with the new strategy, the algorithms are faster, propose fewer infeasible designs, and find better Pareto-frontier approximations.

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Constrained Bi-objective Surrogate-Assisted Optimization of Problems with Heterogeneous Evaluation Times: Expensive Objectives and Inexpensive Constraints

Cited by 16 publications

References 21 publications

Constrained Multi-Objective Optimization with a Limited Budget of Function Evaluations

Constrained Multi-Objective Optimization with a Limited Budget of Function Evaluations

Multi-objective NK landscapes with heterogeneous objectives

Parallel Constrained Multi-Objective Optimization for Ship Design Damage Stability Problem with (In)Expensive Function Evaluations

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