Results 151 to 160 of about 1,756,488 (218)

Discovering High Entropy Alloy Electrocatalysts in Vast Composition Spaces with Multiobjective Optimization. [PDF]

J Am Chem Soc
Xu W, Diesen E, He T, Reuter K, Margraf JT.   +4 more
europepmc   +1 more source

Design and validation of a low-cost data-driven poultry heat and moisture production monitoring system. [PDF]

Poult Sci
Deng S, Zhang Z, Wang Y, Li B, Zheng W.
europepmc   +1 more source

AI-driven multi-objective optimization of FCHEV sizing and energy management considering degradation and vehicle dynamics under realistic machine learning-based traffic conditions. [PDF]

Sci Rep
Montazeri-Gh M, Mostashiri A.
europepmc   +1 more source

An Efficient Multi-Objective White Shark Algorithm. [PDF]

Biomimetics (Basel)
Guo W, Qiang Y, Dai F, Wang J, Li S.
europepmc   +1 more source

Satellite Observations Reveal a Positive Relationship Between Trait-Based Diversity and Drought Response in Temperate Forests. [PDF]

Glob Chang Biol
Helfenstein IS, Sturm JT, Schmid B, Damm A, Schuman MC, Morsdorf F.   +5 more
europepmc   +1 more source

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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.
Ke Shang, H. Ishibuchi, Yang Nan, Weiyu Chen   +3 more
semanticscholar   +2 more sources

A Survey on the Hypervolume Indicator in Evolutionary Multiobjective Optimization

IEEE Transactions on Evolutionary Computation, 2021
Hypervolume is widely used as a performance indicator in the field of evolutionary multiobjective optimization (EMO). It is used not only for performance evaluation of EMO algorithms (EMOAs) but also in indicator-based EMOAs to guide the search.
Ke Shang, H. Ishibuchi, Linjun He, Lie Meng Pang   +3 more
semanticscholar   +2 more sources

A Novel Method for Calculating the Parametric Hypervolume Indicator

Volume 3B: 47th Design Automation Conference (DAC), 2021
This paper presents a new methodology for calculating the hypervolume indicator (HVI)for multi-objective and parametric data. Existing multi-objective HVI calculation techniques cannot be directly used for parametric data because designers do not have ...
Jonathan M. Weaver-Rosen, R. Malak
semanticscholar   +2 more sources

A Simple and Fast Hypervolume Indicator-Based Multiobjective Evolutionary Algorithm

IEEE Transactions on Cybernetics, 2015
To find diversified solutions converging to true Pareto fronts (PFs), hypervolume (HV) indicator-based algorithms have been established as effective approaches in multiobjective evolutionary algorithms (MOEAs).
Siwei Jiang, Jie Zhang, Y. Ong, A. N. Zhang, Puay Siew Tan   +4 more
semanticscholar   +3 more sources

A Two-stage Hypervolume Contribution Approximation Method Based on R2 Indicator

2021 IEEE Congress on Evolutionary Computation (CEC), 2021
Hypervolume-based multi-objective evolutionary algorithms (HV-MOEAs) are one of the popular algorithm classes in the evolutionary multi-objective optimization (EMO) com-munity. HV-MOEAs, which can directly optimize the HV of a solution set, are useful in
Yang Nan, Ke Shang, H. Ishibuchi, Linjun He   +3 more
semanticscholar   +2 more sources