The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Multiobjective firefly algorithm for continuous optimization
Open accessAbstract
Design problems in industrial engineering often involve a large number of design variables with multiple objectives, under complex nonlinear constraints. The algorithms for multiobjective problems can be significantly different from the methods for single objective optimization. To find the Pareto front and non-dominated set for a nonlinear multiobjective optimization problem may require significant computing effort, even for seemingly simple problems. Metaheuristic algorithms start to show their advantages in dealing with multiobjective optimization. In this paper, we extend the recently developed firefly algorithm to solve multiobjective optimization problems. We validate the proposed approach using a selected subset of test functions and then apply it to solve design optimization benchmarks. We will discuss our results and provide topics for further research.
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.
Integrity signals
Registry
Cites
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.
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
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Multi-Objective Optimization Using Evolutionary Algorithms
Deb, K., & Kalyanmoy, D. (2001) · Andalas University Repository (Andalas University) · Cited by 14,918
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Zhang, Q., & Li, H. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.892759 · Cited by 10,069
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Zitzler, E., & Thiele, L. (1999) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.797969 · Cited by 8,798
(2019) · Handbook of Machine Learning · doi:10.1142/9789811205675_0004 · Cited by 6,270
A New Heuristic Optimization Algorithm: Harmony Search
Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001) · SIMULATION · doi:10.1177/003754970107600201 · Cited by 6,247
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Zitzler, E., Deb, K., & Thiele, L. (2000) · Evolutionary Computation · doi:10.1162/106365600568202 · Cited by 5,697
Survey of multi-objective optimization methods for engineering
Marler, R. T., & Arora, J. S. (2004) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-003-0368-6 · Cited by 4,710
Nature-Inspired Metaheuristic Algorithms
Yang, X. (2008) · Medical Entomology and Zoology · Cited by 4,556
Self-citation
Cited by
A novel efficient substitution-box design based on firefly algorithm and discrete chaotic map
Ahmed, H. A., Zolkipli, M. F., & Ahmad, M. (2019) · Neural Computing and Applications · doi:10.1007/s00521-018-3557-3 · Cited by 142
Nature-inspired framework for hyperspectral band selection
Nakamura, R. Y. M., Fonseca, L. M. G., Santos, J. A. D., Torres, R. D. S., Yang, X.-S., & Papa, J. P. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2258351 · Cited by 70
Self-citation
Metadata
- Authors
- Yang, X.-S.
- Venue
- Engineering with Computers
- Published
- 2012-01-24
- Volume
- 29
- Issue
- 2
- Pages
- 175-184
- ISSN
- 0177-0667 (ISSN-L)
- 1435-5663
- OpenAlex
- W2156023754
- Cited by
- 513
- References
- 33
- FWCI
- 33.60
- Type
- journal article
- Language
- en
- Open access
- green
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: Multiobjective firefly algorithm for continuous optimization. https://app.cochat.ai/papers/10.1007/s00366-012-0254-1
- The paper
- Yang, X.-S. (2013). Multiobjective firefly algorithm for continuous optimization. Engineering with Computers. https://doi.org/10.1007/s00366-012-0254-1
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.
