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Structural optimization of different truss designs using two archive mult objective crystal structure optimization algorithm. [PDF]

Sci Rep
Mehta P, Tejani GG, Mousavirad SJ.
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Computational Homogenisation and Identification of Auxetic Structures with Interval Parameters. [PDF]

Materials (Basel)
Beluch W, Hatłas M, Ptaszny J, Kloc-Ptaszna A.   +3 more
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Transformation-based Hypervolume Indicator: A Framework for Designing Hypervolume Variants

2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020
The hypervolume indicator is a popular performance indicator in the field of Evolutionary Multi-objective optimization (EMO). However, there are two issues associated with it in addition to its large calculation cost for many-objective problems. The first issue is that the maximization of the hypervolume indicator leads to a non-uniform solution set on
Ke Shang, Hisao Ishibuchi, Yang Nan, Weiyu Chen   +3 more
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The logarithmic hypervolume indicator

Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms, 2011
It was recently proven that sets of points maximizing the hypervolume indicator do not give a good multiplicative approximation of the Pareto front. We introduce a new "logarithmic hypervolume indicator" and prove that it achieves a close-to-optimal multiplicative approximation ratio.
Friedrich, Tobias, Bringmann, Karl, Voß, Thomas, Igel, Christian   +3 more
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Approximating Hypervolume and Hypervolume Contributions Using Polar Coordinate

IEEE Transactions on Evolutionary Computation, 2019
The hypervolume and hypervolume contributions are widely used in multiobjective evolutionary optimization. However, their exact calculation is NP-hard. By definition, hypervolume is an ${m}$ -D integral (where ${m}$ is the number of objectives).
Jingda Deng, Qingfu Zhang
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Computing representations using hypervolume scalarizations

Computers & Operations Research, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Luís Paquete, Britta Schulze, Michael Stiglmayr, Ana C. Lourenço   +3 more
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Extending quick hypervolume

Journal of Heuristics, 2016
We extend the functionality of the quick hypervolume (QHV) algorithm. Given a set of d-dimensional points this algorithm determines the hypervolume of the dominated space, a useful measure for multiobjective evolutionary algorithms (MOEAs). We extend QHV in two ways: adapt it to compute the exclusive hypervolume of each point, and speed it up with ...
Luís M. S. Russo, Alexandre P. Francisco   +1 more
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Hypervolume Subset Selection with Small Subsets

Evolutionary Computation, 2019
The hypervolume subset selection problem (HSSP) aims at approximating a set of [Formula: see text] multidimensional points in [Formula: see text] with an optimal subset of a given size. The size [Formula: see text] of the subset is a parameter of the problem, and an approximation is considered best when it maximizes the hypervolume indicator.
Benoît, Groz, Silviu, Maniu
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Multiplicative approximations and the hypervolume indicator

Proceedings of the 11th Annual conference on Genetic and evolutionary computation, 2009
Indicator-based algorithms have become a very popular approach to solve multi-objective optimization problems. In this paper, we contribute to the theoretical understanding of algorithms maximizing the hypervolume for a given problem by distributing μ points on the Pareto front.
Friedrich, T., Horoba, C., Neumann, F.
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Expected Hypervolume Improvement Is a Particular Hypervolume Improvement

Proceedings of the AAAI Conference on Artificial Intelligence
Multi-objective Bayesian optimization (MOBO) aims to optimize multiple competing objective functions in the expensive-to-evaluate scenario. The Expected Hypervolume Improvement (EHVI) is a commonly used acquisition function for MOBO and shows a good performance.
Jingda Deng, Jianyong Sun, Qingfu Zhang, Hui Li   +3 more
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