An Evolutionary Strategy for Surrogate-Based Multiobjective Optimization

Pilát, Martin; Neruda, Roman

doi:10.1109/cec.2012.6256450

Cited by 13 publications

(13 citation statements)

References 23 publications

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“…Table 1 shows the results of our algorithm compared to original NSGA-II [1], and -IBEA [4], and SBMO-ES [2]. In the table NSGA means the original NSGA-II and IBEA is the -IBEA.…”

Section: Test Setupmentioning

confidence: 95%

“…The proposed algorithm is based on SBMO-ES [2]. It uses the same distance based aggregate meta-model to assess the quality of individuals and during the local search (a different model is used during pre-selection).…”

Section: Algorithm Descriptionmentioning

confidence: 99%

“…Recently, we proposed a surrogate based evolution strategy SBMO-ES [2], and showed that is greatly reduces the number of objective function evaluations needed to reach a specified quality of the solution expressed in terms of the hypervolume. Its main disadvantage is that it is an (λ + 1) evolution strategy, and thus it provides only one individual for evaluation in each generation.…”

Section: Introductionmentioning

confidence: 99%

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A surrogate multiobjective evolutionary strategy with local search and pre-selection

Pilát

Neruda

2012

Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation

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“…Table 1 shows the results of our algorithm compared to original NSGA-II [1], and -IBEA [4], and SBMO-ES [2]. In the table NSGA means the original NSGA-II and IBEA is the -IBEA.…”

Section: Test Setupmentioning

confidence: 95%

Section: Algorithm Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A surrogate multiobjective evolutionary strategy with local search and pre-selection

Pilát

Neruda

2012

Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation

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“…Also within a framework of memetic algorithms, Georgopoulou and Giannakoglou [49] proposed to perform a low-cost pre-evaluation of candidate solutions using RBF networks in global search and the gradientbased refinement of promising solutions during the local search. In [50], a global surrogate model was proposed for better pre-offspring selection, and a local surrogate model was used to approximate the fitness in local search. Zhou et al [51], [26] proposed a hierarchical surrogate-assisted evolutionary algorithm in which a Gaussian Process was used as a global surrogate to pre-screen promising individuals and an RBF network was utilized as the local surrogate to assist the trustregion enabled gradient-based search strategy to accelerate convergence.…”

Section: ) Local-surrogate Assisted Metaheuristic Algorithmsmentioning

confidence: 99%

Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

Sun

Jin

Cheng

et al. 2017

IEEE Trans. Evol. Computat.

340

131

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Abstract-Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization algorithm and a surrogate-assisted social learning based particle swarm optimization algorithm cooperatively search for the global optimum. The cooperation between the particle swarm optimization and the social learning based particle swarm optimization consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the social learning based particle swarm optimization focuses on exploration while the particle swarm optimization concentrates on local search. Empirical studies on six 50-dimensional and six 100-dimensional benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.

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“…The idea of maintaining multiple metamodels or ensemble of metamodels where the best metamodel can be adaptively selected during the optimization run was adopted also, for example, in other studies (Gorissen, Dhaene & Turck, ; Jin, ; Le, Ong, Menzel, Jin, & Sendhoff, ; Yang, Yeun, & Ruy, ). In one study (Pilat & Neruda, ), a surrogate is used to approximate a distance metric for selection while a second surrogate is used to approximate the objectives separately for local search.…”

Section: State‐of‐the‐art In Surrogate‐assisted Multicriteria Optimizmentioning

confidence: 99%

Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

Allmendinger

Emmerich

Hakanen

et al. 2017

J. Multi-Crit. Decis. Anal.

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Complexity in solving real-world multicriteria optimization problems often stems from the fact that complex, expensive, and/or time-consuming simulation tools or physical experiments are used to evaluate solutions to a problem. In such settings, it is common to use efficient computational models, often known as surrogates or metamodels, to approximate the outcome (objective or constraint function value) of a simulation or physical experiment. The presence of multiple objective functions poses an additional layer of complexity for surrogate-assisted optimization.For example, complexities may relate to the appropriate selection of metamodels for the individual objective functions, extensive training time of surrogate models, or the optimal use of many-core computers to approximate efficiently multiple objectives simultaneously. Thinking out of the box, complexity can also be shifted from approximating the individual objective functions to approximating the entire Pareto front. This leads to further complexities, namely, how to validate statistically and apply the techniques developed to real-world problems. In this paper, we discuss emerging complexity-related topics in surrogate-assisted multicriteria optimization that may not be prevalent in nonsurrogate-assisted single-objective optimization. These complexities are motivated using several real-world problems in which the authors were involved. We then discuss several promising future research directions and prospective solutions to tackle emerging complexities in surrogate-assisted multicriteria optimization. Finally, we provide insights from an industrial point of view into how surrogate-assisted multicriteria optimization techniques can be developed and applied within a collaborative business environment to tackle real-world problems.

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An Evolutionary Strategy for Surrogate-Based Multiobjective Optimization

Cited by 13 publications

References 23 publications

A surrogate multiobjective evolutionary strategy with local search and pre-selection

A surrogate multiobjective evolutionary strategy with local search and pre-selection

Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

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