[1] |
AartsE, KorstJ. Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing. John Wiley & Sons Inc.; 1989. · Zbl 0674.90059 |
[2] |
BackT, FogelDB, MichalewiczZ, eds. Handbook of Evolutionary Computation, 1st ed.Bristol, UK, UK: IOP Publishing Ltd.; 1997. · Zbl 0883.68001 |
[3] |
ChristianB, RoliA. Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv. 2003;35(3):268‐308. |
[4] |
GendreauM, PotvinJ‐Y. Handbook of Metaheuristics, 2nd ed.Springer Publishing Company Incorporated; 2010. |
[5] |
NelderJA, RogerM. A simplex method for function minimization. Comput J. 1965;7(4):308‐313. · Zbl 0229.65053 |
[6] |
FogelDB. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. John Wiley & Sons; 2006. |
[7] |
HenryHJ. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT press; 1992. |
[8] |
JamesK, RussellE. Particle swarm optimization IEEE; 1995:1942‐1948. |
[9] |
MarcoD, ChristianB. Ant colony optimization: a survey. Theor Comput Sci. 2005;344(2‐3):243‐278. · Zbl 1154.90626 |
[10] |
CarrilloJA, ChoiY‐P, TotzeckC, TseO. An analytical framework for consensus‐based global optimization method. Math Models Methods Appl Sci. 2018;28(06):1037‐1066. · Zbl 1397.35311 |
[11] |
PinnauR, TotzeckC, TseO, MartinS. A consensus‐based model for global optimization and its mean‐field limit. Math Models Methods Appl Sci. 2017;27(01):183‐204. · Zbl 1388.90098 |
[12] |
MillerPD. Applied Asymptotic Analysis. American Mathematical Soc; 2006. · Zbl 1101.41031 |
[13] |
AmirD, OferZ. Large Deviations Techniques and Applications. Springer‐Verlag Berlin Heidelberg; 2010. · Zbl 1177.60035 |
[14] |
FornasierM, KlockT, RiedlK. Consensus‐based optimization methods converge globally in mean‐field law. arXiv preprint arXiv:2103.15130; 2021. |
[15] |
CarrilloJA, JinS, LiL, ZhuY. A consensus‐based global optimization method for high dimensional machine learning problems. ESAIM ‐ Control Optim Calc Var. 2021;27:S5. · Zbl 1480.60195 |
[16] |
FornasierM, HuangH, PareschiL, SünnenP. Consensus‐based optimization on the sphere: Convergence to global minimizers and machine learning. J Mach Learn Res. 2021;22(237):1‐55. · Zbl 07626752 |
[17] |
FornasierM, HuangH, PareschiL, SünnenP. Consensus‐based optimization on hypersurfaces: well‐posedness and mean‐field limit. Math Models Methods Appl Sci. 2020;30(14):2725‐2751. · Zbl 1467.90039 |
[18] |
KimJ, KangM, KimD, HaS‐Y, YangI. A stochastic consensus method for nonconvex optimization on the Stiefel manifold IEEE; 2020:1050‐1057. |
[19] |
FornasierM, HuangH, PareschiL, SünnenP. Anisotropic diffusion in consensus‐based optimization on the sphere. arXiv preprint arXiv:2104.00420; 2021. |
[20] |
ClaudiaT, Marie‐ThereseW. Consensus‐based global optimization with personal best. Math Biosci Eng: MBE. 2020;17(5):6026‐6044. · Zbl 1473.90132 |
[21] |
BenfenatiA, BorghiG, PareschiL. Binary interaction methods for high dimensional global optimization and machine learning. arXiv preprint arXiv:2105.02695; 2021. |
[22] |
SaraG, LorenzoP. From particle swarm optimization to consensus based optimization: stochastic modeling and mean‐field limit. Math Models Methods Appl Sci. 2021;31(08):1625‐1657. · Zbl 1473.35570 |
[23] |
CiprianiC, HuangH, QiuJ. Zero‐inertia limit: from particle swarm optimization to consensus‐based optimization. SIAM J Math Anal. 2022. · Zbl 1500.90088 |
[24] |
TotzeckC. Trends in consensus‐based optimization; 2021. arXiv preprint arXiv:2104.01383. |
[25] |
SznitmanA‐S. Topics in propagation of chaos Springer; 1991:165‐251. · Zbl 0732.60114 |
[26] |
FetecauRC, HuangH, SunW. Propagation of chaos for the Keller-Segel equation over bounded domains. J Differ Equ. 2019;266(4):2142‐2174. · Zbl 1421.35381 |
[27] |
HuangH, LiuJ‐G, PicklP. On the mean‐field limit for the Vlasov-Poisson-Fokker-Planck system. J Stat Phys. 2020;181(5):1915‐1965. · Zbl 1466.35344 |
[28] |
HuangH, QiuJ. The microscopic derivation and well‐posedness of the stochastic Keller-Segel equation. J Nonlinear Sci. 2021;31(1):1‐31. · Zbl 1464.35363 |
[29] |
LiuJ‐G, YangR. Propagation of chaos for large Brownian particle system with Coulomb interaction. Res Math Sci. 2016;3(1):1‐33. · Zbl 1355.82025 |
[30] |
LiL, LiuJ‐G, YuP. On the mean field limit for Brownian particles with Coulomb interaction in 3D. J Math Phys. 2019;60(11):111501. · Zbl 1431.82019 |
[31] |
FournierN, HaurayM, MischlerS. Propagation of chaos for the 2D viscous vortex model. J Eur Math Soc. 2014;16(7):1423‐1466. · Zbl 1299.76040 |
[32] |
AldousD. Stopping times and tightness. Ann Probab. 1978:335‐340. · Zbl 0391.60007 |
[33] |
BillingsleyP. Convergence of Probability Measures. John Wiley & Sons; 1999. · Zbl 0944.60003 |
[34] |
JeanJ, AlbertS. Limit Theorems for Stochastic Processes.Springer Science & Business Media; 2002. |
[35] |
KurtzTG. Equivalence of stochastic equations and martingale problems. 113-130 Springer; 2011. · Zbl 1236.60073 |
[36] |
MetivierM. Pathwise differentiability with respect to a parameter of solutions of stochastic differential equations; 1982:490‐502. · Zbl 0482.60060 |