A multi-objective memetic inverse solver reinforced by local optimization methods

Gajda-Zagórska, Ewa; Schaefer, Robert; Smółka, Maciej; Pardo, David; Álvarez-Aramberri, Julen

doi:10.1016/j.jocs.2016.06.007

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…Such approach was already studied in our earlier papers, e.g. (Gajda-Zagórska et al, 2017;Gajda-Zagórska et al, 2015;Gajda-Zagórska, 2015).…”

Section: Solving Inverse Problems Expressed As a Multiobjective Optimizationmentioning

confidence: 97%

“…The global phase of our strategy is based on the Hierarchic Memetic Strategy (HMS) framework allowing the effective stochastic search for multimodal GOPs. The core of this framework was mentioned in (Gajda-Zagórska et al, 2017) and was broadly described in the monograph (Smołka, 2015), so we describe here only crucial HMS steps and ideas. The HMS creates and processes a tree of demes.…”

Section: Hierarchic Memetic Search As a Global Phasementioning

confidence: 99%

“…The first configuration was commonly used in our previous experiments (cf. Smołka et al, 2015a;Smołka et al, 2015b;Gajda-Zagórska et al, 2017), the second one engages the widely used multiobjective evolutionary algorithm -NSGA-II. Both configurations were executed 10 times on each benchmark.…”

Section: Strategy Configurationmentioning

confidence: 99%

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Handling insensitivity in multi-physics inverse problems using a complex evolutionary strategy

Sawicki¹,

Smółka²,

Łoś³

et al. 2019

cmms

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In this paper we present a complex strategy for the solution of ill posed, in-verse problems formulated as multiobjective global optimization ones. The strategy is capable of identifying the shape of objective insensitivity regions around connected components of Pareto set. The goal is reached in two phases. In the first, global one, the connected components of the Pareto set are localized and separated in course of the multi-deme, hierarchic memetic strategy HMS. In the second, local phase, the random sample uniformly spread over each Pareto component and its close neighborhood is obtained in the specially profiled evolutionary process using multiwinner selection. Finally, each local sample forms a base for the local approximation of a dominance function. Insensitivity region surrounding each connected component of the Pareto set is estimated by a sufficiently low level set of this approximation. Capabilities of the whole procedure was verified using specially-designed two-criterion benchmarks.

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“…Such approach was already studied in our earlier papers, e.g. (Gajda-Zagórska et al, 2017;Gajda-Zagórska et al, 2015;Gajda-Zagórska, 2015).…”

Section: Solving Inverse Problems Expressed As a Multiobjective Optimizationmentioning

confidence: 97%

Section: Hierarchic Memetic Search As a Global Phasementioning

confidence: 99%

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Handling insensitivity in multi-physics inverse problems using a complex evolutionary strategy

Sawicki¹,

Smółka²,

Łoś³

et al. 2019

cmms

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View full text Add to dashboard Cite

show abstract

“…The final solutions to the whole strategy are then discovered by the leaf demes. For the full description of the HGS and the HMS we refer the reader to papers [23,24,17,21,25,22].…”

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confidence: 99%

Using Covariance Matrix Adaptation Evolutionary Strategy to boost the search accuracy in hierarchic memetic computations

Sawicki

Łoś

Smółka

et al. 2019

Journal of Computational Science

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“…based on domination ranks (see [4]). The sets to be found in this case are associated with the connected components of a Pareto set.…”

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confidence: 99%

Approximation of the objective insensitivity regions using Hierarchic Memetic Strategy coupled with Covariance Matrix Adaptation Evolutionary Strategy

Sawicki,

Smołka,

Łoś

et al. 2019

Preprint

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One of the most challenging types of ill-posedness in global optimization is the presence of insensitivity regions in design parameter space, so the identification of their shape will be crucial, if ill-posedness is irrecoverable. Such problems may be solved using global stochastic search followed by post-processing of a local sample and a local objective approximation. We propose a new approach of this type composed of Hierarchic Memetic Strategy (HMS) powered by the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) well-known as an effective, self-adaptable stochastic optimization algorithm and we leverage the distribution density knowledge it accumulates to better identify and separate insensitivity regions. The results of benchmarks prove that the improved HMS-CMA-ES strategy is effective in both the total computational cost and the accuracy of insensitivity region approximation. The reference data for the tests was obtained by means of a well-known effective strategy of multimodal stochastic optimization called the Niching Evolutionary Algorithm 2 (NEA2), that also uses CMA-ES as a component.

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A multi-objective memetic inverse solver reinforced by local optimization methods

Cited by 6 publications

References 36 publications

Handling insensitivity in multi-physics inverse problems using a complex evolutionary strategy

Handling insensitivity in multi-physics inverse problems using a complex evolutionary strategy

Using Covariance Matrix Adaptation Evolutionary Strategy to boost the search accuracy in hierarchic memetic computations

Approximation of the objective insensitivity regions using Hierarchic Memetic Strategy coupled with Covariance Matrix Adaptation Evolutionary Strategy

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