Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 5,696

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

SCA: A Sine Cosine Algorithm for solving optimization problems

Mirjalili, Seyedali·2016·Knowledge-Based Systems
DOI 10.1016/j.knosys.2015.12.022
Open this paper in CoChat — free account

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 · 10.1016/j.knosys.2015.12.022

Cites

3 of 3 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 3 references

Research works printed but not linked: 59

  1. Simpson, 1994, Genetic algorithms compared to other techniques for pipe optimization, J. Water Resour. Plan. Manag., 120, 423, 10.1061/(ASCE)0733-9496(1994)120:4(423) · doi:10.1061/(asce)0733-9496(1994)120:4(423
  2. Boussaïd, 2013, A survey on optimization metaheuristics, Inf. Sci., 237, 82, 10.1016/j.ins.2013.02.041 · DOI · Paper page
  3. Parejo, 2012, Metaheuristic optimization frameworks: a survey and benchmarking, Soft Comput., 16, 527, 10.1007/s00500-011-0754-8 · DOI
  4. Zhou, 2011, Multiobjective evolutionary algorithms: a survey of the state of the art, Swarm Evol. Comput., 1, 32, 10.1016/j.swevo.2011.03.001 · DOI · Paper page
  5. Droste, 2006, Upper and lower bounds for randomized search heuristics in black-box optimization, Theory of Comput. Syst., 39, 525, 10.1007/s00224-004-1177-z · DOI
  6. Parpinelli, 2011, New inspirations in swarm intelligence: a survey, Int. J. Bio-Inspired Comput., 3, 1, 10.1504/IJBIC.2011.038700 · DOI · Paper page
  7. Fonseca, 1995, An overview of evolutionary algorithms in multiobjective optimization, Evol. Comput., 3, 1, 10.1162/evco.1995.3.1.1 · DOI · Paper page
  8. Biswas, 2013, Physics-inspired optimization algorithms: a survey, J. Optim., 2013
  9. Gogna, 2013, Metaheuristics: review and application, J. Exp. Theor. Artif. Intell., 25, 503, 10.1080/0952813X.2013.782347 · DOI · Paper page
  10. Yang, 2013, Swarm intelligence and bio-inspired computation: theory and applications, Newnes
  11. Saremi, 2014, Biogeography-based optimisation with chaos, Neural Comput. Appl., 25, 1077, 10.1007/s00521-014-1597-x · DOI · Paper page
  12. Wang, 2014, Chaotic krill herd algorithm, Inf. Sci., 274, 17, 10.1016/j.ins.2014.02.123 · DOI · Paper page
  13. Wang, 2013, A chaotic particle-swarm krill herd algorithm for global numerical optimization, Kybernetes, 42, 962, 10.1108/K-11-2012-0108 · doi:10.1108/k-11-2012-0108
  14. Wang, 1726, Chaotic cuckoo search, Soft Comput., 1
  15. Wang, 2014, Incorporating mutation scheme into krill herd algorithm for global numerical optimization, Neural Comput. Appl., 24, 853, 10.1007/s00521-012-1304-8 · DOI
  16. Wang, 2012, A bat algorithm with mutation for UCAV path planning, Sci. World J., 2012
  17. Zhang, 2012, Image matching using a bat algorithm with mutation, Appl. Mech. Mater., 203, 88, 10.4028/www.scientific.net/AMM.203.88 · doi:10.4028/www.scientific.net/amm.203.88
  18. Zhu, 2004, An ant colony optimization algorithm based on mutation and dynamic pheromone updating, J. Softw., 15, 185
  19. Premalatha, 2008, A new approach for data clustering based on PSO with local search, Comput. Inf. Sci., 1, 139
  20. Noman, 2008, Accelerating differential evolution using an adaptive local search, IEEE Trans. Evol. Comput., 12, 107, 10.1109/TEVC.2007.895272 · DOI · Paper page
  21. Levine, 2004, Ant colony optimization and local search for bin packing and cutting stock problems, J. Oper. Res. Soc., 55, 705, 10.1057/palgrave.jors.2601771 · DOI
  22. Wang, 2013, A novel hybrid bat algorithm with harmony search for global numerical optimization, J. Appl. Math., 2013
  23. Wang, 2014, Hybrid krill herd algorithm with differential evolution for global numerical optimization, Neural Comput. Appl., 25, 297, 10.1007/s00521-013-1485-9 · DOI
  24. Wang, 2012, A hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning, Sci. World J., 2012
  25. Wang, 2013, Hybridizing harmony search with biogeography based optimization for global numerical optimization, J. Comput. Theor. Nanosci., 10, 2312, 10.1166/jctn.2013.3207 · doi:10.1166/jctn.2013.3207
  26. Duan, 2012, Test-sheet composition using analytic hierarchy process and hybrid metaheuristic algorithm TS/BBO, Math. Probl. Eng., 2012, 10.1155/2012/712752 · doi:10.1155/2012/712752
  27. Wang, 2012, A hybrid meta-heuristic DE/CS algorithm for UCAV path planning, J. Inf. Comput. Sci., 5, 4811
  28. Shi, 2005, An improved GA and a novel PSO-GA-based hybrid algorithm, Inf. Process. Lett., 93, 255, 10.1016/j.ipl.2004.11.003 · DOI · Paper page
  29. Holden, 2008, A hybrid PSO/ACO algorithm for discovering classification rules in data mining, J. Artif. Evol. Appl., 2008, 2
  30. Nemati, 2009, A novel ACO–GA hybrid algorithm for feature selection in protein function prediction, Expert Syst. Appl., 36, 12086, 10.1016/j.eswa.2009.04.023 · DOI · Paper page
  31. Lin, 2010, A GA–DE hybrid evolutionary algorithm for path synthesis of four-bar linkage, Mech. Mach. Theory, 45, 1096, 10.1016/j.mechmachtheory.2010.03.011 · DOI
  32. Duan, 2010, Three-dimension path planning for UCAV using hybrid meta-heuristic ACO-DE algorithm, Simul. Model. Pract. Theory, 18, 1104, 10.1016/j.simpat.2009.10.006 · DOI
  33. Wang, 2013, A new hybrid method based on krill herd and cuckoo search for global optimization tasks, Int. J. Bio-Inspired Comput.
  34. Wang, 2014, An effective krill herd algorithm with migration operator in biogeography-based optimization, Appl. Math. Model., 38, 2454, 10.1016/j.apm.2013.10.052 · DOI · Paper page
  35. Holland, 1977, Cognitive systems based on adaptive algorithms, ACM SIGART Bull., 10.1145/1045343.1045373 · DOI · Paper page
  36. Wang, 2014, Differential evolution based on covariance matrix learning and bimodal distribution parameter setting, Appl. Soft Comput., 18, 232, 10.1016/j.asoc.2014.01.038 · DOI · Paper page
  37. Wang, 2011, Differential evolution with composite trial vector generation strategies and control parameters, IEEE Trans. Evol. Comput., 15, 55, 10.1109/TEVC.2010.2087271 · DOI · Paper page
  38. Wang, 2012, Enhancing the search ability of differential evolution through orthogonal crossover, Inf. Sci., 185, 153, 10.1016/j.ins.2011.09.001 · DOI · Paper page
  39. Simon, 2008, Biogeography-based optimization, IEEE Trans. Evol. Comput., 12, 702, 10.1109/TEVC.2008.919004 · DOI · Paper page
  40. Karaboga, 2007, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, J. Global Optim., 39, 459, 10.1007/s10898-007-9149-x · DOI · Paper page
  41. Rashedi, 2009, GSA: a gravitational search algorithm, Inf. Sci., 179, 2232, 10.1016/j.ins.2009.03.004 · DOI · Paper page
  42. Kaveh, 2014, Colliding Bodies Optimization method for optimum discrete design of truss structures, Comput. Struct., 139, 43, 10.1016/j.compstruc.2014.04.006 · DOI
  43. Hatamlou, 2013, Black hole: a new heuristic optimization approach for data clustering, Inf. Sci., 222, 175, 10.1016/j.ins.2012.08.023 · DOI · Paper page
  44. Kashan, 2014, League Championship Algorithm (LCA): an algorithm for global optimization inspired by sport championships, Appl. Soft Comput., 16, 171, 10.1016/j.asoc.2013.12.005 · doi:10.1016/j.asoc.2013.12.005
  45. Sadollah, 2013, Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems, Appl. Soft Comput., 13, 2592, 10.1016/j.asoc.2012.11.026 · doi:10.1016/j.asoc.2012.11.026
  46. Rao, 2011, Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems, Comput.-Aided Des., 43, 303, 10.1016/j.cad.2010.12.015 · DOI · Paper page
  47. Mirjalili, 2015, Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowl.-Based Syst., 89, 228, 10.1016/j.knosys.2015.07.006 · DOI · Paper page
  48. Črepinšek, 2013, Exploration and exploitation in evolutionary algorithms: a survey, ACM Comput. Surv., 45, 35, 10.1145/2480741.2480752 · DOI · Paper page
  49. Yao, 1999, Evolutionary programming made faster, IEEE Trans. Evol. Comput., 3, 82, 10.1109/4235.771163 · DOI · Paper page
  50. Digalakis, 2001, On benchmarking functions for genetic algorithms, Int. J. Comput. Math., 77, 481, 10.1080/00207160108805080 · DOI · Paper page
  51. Molga, 2005, Test functions for optimization needs, Test Funct. Optim. Needs
  52. X.-S. Yang, Test problems in optimization, 2010. Available from arXiv:1008.0549. · arXiv
  53. Yang, 2010, Firefly algorithm, stochastic test functions and design optimisation, Int. J. Bio-Inspired Comput., 2, 78, 10.1504/IJBIC.2010.032124 · DOI · Paper page
  54. Yang, 2014, Flower pollination algorithm: a novel approach for multiobjective optimization, Eng. Optim., 46, 1222, 10.1080/0305215X.2013.832237 · DOI · Paper page
  55. Wang, 2012, Combining multiobjective optimization with differential evolution to solve constrained optimization problems, IEEE Trans. Evol. Comput., 16, 117, 10.1109/TEVC.2010.2093582 · DOI
  56. Pasandideh, 2015, Optimization of a multiproduct economic production quantity problem with stochastic constraints using sequential quadratic programming, Knowl.-Based Syst., 84, 98, 10.1016/j.knosys.2015.04.001 · doi:10.1016/j.knosys.2015.04.001
  57. Jalali, 2015, Optimizing a bi-objective reliable facility location problem with adapted stochastic measures using tuned-parameter multi-objective algorithms, Knowl.-Based Syst.
  58. Salimi, 2015, Stochastic fractal search: a powerful metaheuristic algorithm, Knowl.-Based Syst., 75, 1, 10.1016/j.knosys.2014.07.025 · DOI · Paper page
  59. Coello Coello, 2002, Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art, Comput. Methods Appl. Mech. Eng., 191, 1245, 10.1016/S0045-7825(01)00323-1 · DOI · Paper page

Cited by

See all 5,696

Metadata

Authors
Mirjalili, S.
Venue
Knowledge-Based Systems
Publisher
Elsevier BV
Published
2016-03
Volume
96
Pages
120-133
ISSN
0950-7051 (ISSN-L)
DOI
10.1016/j.knosys.2015.12.022
OpenAlex
W2232317135
Cited by
5,696
References
3
FWCI
155.75
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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: SCA: A Sine Cosine Algorithm for solving optimization problems. https://app.cochat.ai/papers/10.1016/j.knosys.2015.12.022
The paper
Mirjalili, S. (2016). SCA: A Sine Cosine Algorithm for solving optimization problems. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2015.12.022

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.