Termination detection strategies in evolutionary algorithms

Liu, Yanfeng; Zhou, Aimin; Zhang, Hu

doi:10.1145/3205455.3205466

Cited by 13 publications

(3 citation statements)

References 47 publications

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“…In particular, two types of approaches seem to be worth exploring in this context: multimodal optimization methods (Li et al, 2017) and heuristic methods for constrained optimization (Kulkarni et al, 2021). On the other hand, optimization procedures that are employing different kinds of regularization (Liu et al, 2018) should be used very carefully. That is because their main strength, namely, to obtain any satisfactory solution as fast as possible, could do a disservice in the situation when we want to explore the variability of possible solutions.…”

Section: Figurementioning

confidence: 99%

U(VI), Np(V), Eu(III) sorption on goethite: A wide-ranging multiradionuclide dataset and uncertainty-aware parametrization of surface complexation models

Romanchuk¹,

Svitelman²,

Blinov³

et al. 2022

Front. Nucl. Eng.

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Obtaining reliable thermodynamic sorption data is essential for establishing databases that can be used for the numerical safety evaluations of radioactive waste storage and disposal facilities. For this purpose, new experimental data on the sorption of U(VI), Np(V), and Eu(III) onto synthesized goethite in a wide range of experimental conditions were collected. Thermodynamic surface complexation models based on different approaches were developed and parametrized to fit a large dataset, including experimental data from the current work as well as available literature data. The proposed heuristic optimization procedure allowed identifying and comparing different parametrization variants and their uncertainties for considered models. The developed models pass the test on additional Cd(II) and Zn(II) data, simultaneously providing a reliable description of the sorption process for five cations and could probably be extended using the obtained linear free energy relationship.

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Section: Figurementioning

confidence: 99%

U(VI), Np(V), Eu(III) sorption on goethite: A wide-ranging multiradionuclide dataset and uncertainty-aware parametrization of surface complexation models

Romanchuk¹,

Svitelman²,

Blinov³

et al. 2022

Front. Nucl. Eng.

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“…They are all shown in Table II. Stop criteria are non-trivial for these methods, however, they are always different [40]. Therefore a maximum fitness evaluation and a time budget are both used for the termination detection.…”

Section: A Implementation Detailsmentioning

confidence: 99%

Variable Division and Optimization for Constrained Multiobjective Portfolio Problems

Chen¹,

Zhou²

2021

Preprint

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Variable division and optimization (D&O) is a frequently utilized algorithm design paradigm in Evolutionary Algorithms (EAs). A D&O EA divides a variable into partial variables and then optimize them respectively. A complicated problem is thus divided into simple subtasks. For example, a variable of portfolio problem can be divided into two partial variables, i.e. the selection of assets and the allocation of capital. Thereby, we optimize these two partial variables respectively. There is no formal discussion about how are the partial variables iteratively optimized and why can it work for both single-and multi-objective problems in D&O. In this paper, this gap is filled. According to the discussion, an elitist selection method for partial variables in multiobjective problems is developed. Then this method is incorporated into the Decomposition-Based Multiobjective Evolutionary Algorithm (D&O-MOEA/D). With the help of a mathematical programming optimizer, it is achieved on the constrained multiobjective portfolio problems. In the empirical study, D&O-MOEA/D is implemented for 20 instances and recent Chinese stock markets. The results show the superiority and versatility of D&O-MOEA/D on large-scale instances while the performance of it on small-scale problems is also not bad. The former targets convergence towards the Pareto front and the latter helps promote diversity among the non-dominated solutions during the search process.

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“…The main iteration of the algorithm is described in lines 6-14. While the stopping criteria are not met [24], the new candidate solution is generated with a search operator (line 9…”

Section: Algorithm Frameworkmentioning

confidence: 99%

Utilizing Dependence among Variables in Evolutionary Algorithms for Mixed-Integer Programming: A Case Study on Multi-Objective Constrained Portfolio Optimization

Che

Zhou

Das

2019

Preprint

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Termination detection strategies in evolutionary algorithms

Cited by 13 publications

References 47 publications

U(VI), Np(V), Eu(III) sorption on goethite: A wide-ranging multiradionuclide dataset and uncertainty-aware parametrization of surface complexation models

U(VI), Np(V), Eu(III) sorption on goethite: A wide-ranging multiradionuclide dataset and uncertainty-aware parametrization of surface complexation models

Variable Division and Optimization for Constrained Multiobjective Portfolio Problems

Utilizing Dependence among Variables in Evolutionary Algorithms for Mixed-Integer Programming: A Case Study on Multi-Objective Constrained Portfolio Optimization

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