A New Repair Method For Constrained Optimization

Koch, Peter; Bagheri, Samineh; Konen, Wolfgang; Foussette, Christophe; Krause, Peter; Bäck, Thomas

doi:10.1145/2739480.2754658

Cited by 19 publications

(18 citation statements)

References 20 publications

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“…Here, the surrogates are used to approximate the objective function and the inequality constraint functions. These include the ConstrLMSRBF algorithm (Regis [5]), constrained extensions of EGO (e.g., Basudhar et al [37], COBRA (Regis [38]) and its extensions (e.g., Koch et al [39]), and TRICEPS (Regis [40]). …”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective constrained black-box optimization using radial basis function surrogates

Regis

2016

Journal of Computational Science

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Section: Literature Reviewmentioning

confidence: 99%

Multi-objective constrained black-box optimization using radial basis function surrogates

Regis

2016

Journal of Computational Science

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“…Basudhar et al [18] enhanced the ability of Efficient Global Optimization (EGO) for solving constrained problems by using SVR to approximate the boundary of the feasible domain. Koch et al [19] proposed a new mechanism for Constrained Optimization by Radial Basis Function Approximation (COBRA) to repair violated designs and this technique was further developed by Bagheri [20].…”

Section: Introductionmentioning

confidence: 99%

Efficient strategies for constrained black-box optimization by intrinsically linear approximation (CBOILA)

Liu

Wang

et al. 2020

Engineering with Computers

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In this paper, a novel trust-region based surrogate-assisted optimization method, called CBOILA (Constrained Black-box Optimization by Intrinsically Linear Approximation), has been proposed to reduce the number of black-box function evaluations and enhance the efficient performance for solving complex optimization problems. This developed optimization approach utilizes an assembly of intrinsically linear approximations to seek the optimum with incorporation of three strategies: (1) extended-box selection strategy (EBS), ( 2) global intelligence selection strategy (GIS) and ( 3) balanced trust-region strategy. EBS aims at reducing the number of function evaluations in current iteration by selecting points close to the given trust region boundary. Whilst, GIS is designed to improve the exploration performance by adaptively choosing points outside the trust region. The balanced trust-region strategy works with four indicators, which will be triggered by the quality of the approximation, the movement direction of the search, the location of the sub-optimum, and the condition of the termination, respectively. By modifying the move limit of each dimension accordingly, CBOILA is capable of attaining a balanced search between exploitation and exploration for the optimal solutions. To demonstrate the potentials of the proposed optimization method, three widely used benchmark problems have been examined and the results have also been compared with solutions by other metamodel-based algorithms in published works. Results show that the proposed method can efficiently and robustly solve constrained black-box optimization problems within an acceptable computational time.

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“…So far most SAEAs have been developed to tackle small to medium-sized (up to 30 decision variables) expensive optimization problems with few exceptions [29], [30], [31], [32], partly because a majority of real-world expensive optimization problems have a medium-sized decision variables [11], and partly because most surrogates are not able to perform well for high-dimensional problems with limited training data. Nevertheless, existing SAEAs typically still require a large number of expensive FEs to obtain an acceptable solution.…”

Section: Introductionmentioning

confidence: 99%

Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems

Wang

Jin

Doherty

2017

IEEE Trans. Cybern.

329

132

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Function evaluations (FEs) of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to solve these problems. To address this challenge, the research on surrogate-assisted EAs has attracted increasing attention from both academia and industry over the past decades. However, most existing surrogate-assisted EAs (SAEAs) either still require thousands of expensive FEs to obtain acceptable solutions, or are only applied to very low-dimensional problems. In this paper, a novel surrogate-assisted particle swarm optimization (PSO) inspired from committee-based active learning (CAL) is proposed. In the proposed algorithm, a global model management strategy inspired from CAL is developed, which searches for the best and most uncertain solutions according to a surrogate ensemble using a PSO algorithm and evaluates these solutions using the expensive objective function. In addition, a local surrogate model is built around the best solution obtained so far. Then, a PSO algorithm searches on the local surrogate to find its optimum and evaluates it. The evolutionary search using the global model management strategy switches to the local search once no further improvement can be observed, and vice versa. This iterative search process continues until the computational budget is exhausted. Experimental results comparing the proposed algorithm with a few state-of-the-art SAEAs on both benchmark problems up to 30 decision variables as well as an airfoil design problem demonstrate that the proposed algorithm is able to achieve better or competitive solutions with a limited budget of hundreds of exact FEs.

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A New Repair Method For Constrained Optimization

Cited by 19 publications

References 20 publications

Multi-objective constrained black-box optimization using radial basis function surrogates

Multi-objective constrained black-box optimization using radial basis function surrogates

Efficient strategies for constrained black-box optimization by intrinsically linear approximation (CBOILA)

Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems

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