Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 867

The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.

A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization

Zhang, Xingyi, Tian, Ye, Jin, Yaochu·2015·IEEE Transactions on Evolutionary Computation
DOI 10.1109/tevc.2014.2378512
Open this paper in CoChat — free account

Abstract

Evolutionary algorithms (EAs) have shown to be promising in solving many-objective optimization problems (MaOPs), where the performance of these algorithms heavily depends on whether solutions that can accelerate convergence toward the Pareto front and maintaining a high degree of diversity will be selected from a set of nondominated solutions. In this paper, we propose a knee point-driven EA to solve MaOPs. Our basic idea is that knee points are naturally most preferred among nondominated solutions if no explicit user preferences are given. A bias toward the knee points in the nondominated solutions in the current population is shown to be an approximation of a bias toward a large hypervolume, thereby enhancing the convergence performance in many-objective optimization. In addition, as at most one solution will be identified as a knee point inside the neighborhood of each solution in the nondominated front, no additional diversity maintenance mechanisms need to be introduced in the proposed algorithm, considerably reducing the computational complexity compared to many existing multiobjective EAs for many-objective optimization. Experimental results on 16 test problems demonstrate the competitiveness of the proposed algorithm in terms of both solution quality and computational efficiency.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tevc.2014.2378512

Cites

44 of 44 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 44 references

Cited by

See all 867

Metadata

Authors
Zhang, X., Tian, Y., & Jin, Y.
Venue
IEEE Transactions on Evolutionary Computation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2015-12
Volume
19
Issue
6
Pages
761-776
ISSN
  • 1089-778X (ISSN-L)
  • 1941-0026
DOI
10.1109/tevc.2014.2378512
OpenAlex
W2072661909
Cited by
867
References
44
FWCI
40.53
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization. https://app.cochat.ai/papers/10.1109/tevc.2014.2378512
The paper
Zhang, X., Tian, Y., & Jin, Y. (2015). A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/tevc.2014.2378512

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.