Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations | Paper Citation Record

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This paper cites Abualigah and et al.Hybrid Harris hawks optimization with differential evolu- tion for data clustering, Metaheuristics in machine learning: theory and applications.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Abualigah and et al.Hybrid Harris hawks optimization with differential evolu- tion for data clustering, Metaheuristics in machine learning: theory and applications

Reference 1

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 2

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This paper cites Aranha and et al.Metaphor-based metaheuristics, a call for action: the ele- phant in the room, Swarm Intelligence, 16(1), 1-6, (2022),https://doi.org/10.1007/ s11721-021-00202-9.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Aranha and et al.Metaphor-based metaheuristics, a call for action: the ele- phant in the room, Swarm Intelligence, 16(1), 1-6, (2022),https://doi.org/10.1007/ s11721-021-00202-9

Reference 3

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 4

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Observation ea06ffff-3287-40e1-a0ae-4dafe8805f42 · outbound

This paper cites Auger and N.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Auger and N

Reference 5

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 6

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This paper cites Brest and M.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Brest and M

Reference 7

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Observation 5eaf0a13-2305-42c5-8c35-9314b2f6d1c6 · outbound

This paper cites Clerc,Standard Particle Swarm Optimisation, (2012),https://hal.science/ hal-00764996v1.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Clerc,Standard Particle Swarm Optimisation, (2012),https://hal.science/ hal-00764996v1

Reference 8

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This paper cites Recent Trends in the Use of Statistical Tests for Comparing Swarm and Evolutionary Computing Algorithms: Practical Guidelines and a Critical Review.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Recent Trends in the Use of Statistical Tests for Comparing Swarm and Evolutionary Computing Algorithms: Practical Guidelines and a Critical Review

Reference 9

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This paper cites Chen and et al.Improved slime mould algorithm hybridizing chaotic maps and differential evolution strategy for global optimization, IEEE Access 10, pp.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Chen and et al.Improved slime mould algorithm hybridizing chaotic maps and differential evolution strategy for global optimization, IEEE Access 10, pp

Reference 10

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 11

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 12

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 13

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This paper cites Cymerys and M.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Cymerys and M

Reference 14

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 15

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Observation 2ce98e78-e1b7-4905-a61e-eba3cd49b529 · outbound

This paper cites Derrac, S.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Derrac, S

Reference 16

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This paper cites Dhawale, V.K.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Dhawale, V.K

Reference 17

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This paper cites Biology-inspired joint distribution neurons based on Hierarchical Correlation Reconstruction allowing for multidirectional propagation of values and densities.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Biology-inspired joint distribution neurons based on Hierarchical Correlation Reconstruction allowing for multidirectional propagation of values and densities

Reference 18

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Observation 39797d89-2b60-40dd-bf1a-bb3c8517bea7 · outbound

This paper cites Faramarzi, M.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Faramarzi, M

Reference 19

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Observation ee8f7b18-8627-431d-a926-627a3d0abc85 · outbound

This paper cites Hassan and N.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Hassan and N

Reference 20

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This paper cites Hansen and A.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Hansen and A

Reference 21

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This paper cites Hansen, S.D.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Hansen, S.D

Reference 22

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This paper cites 282–291, Berlin: Springer, (2004),http://www.bionik.tu-berlin.de/ user/niko/ppsn2004hansenkern.pdf.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations 282–291, Berlin: Springer, (2004),http://www.bionik.tu-berlin.de/ user/niko/ppsn2004hansenkern.pdf

Reference 23

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 25

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This paper cites Hong and et al.A variant of the united multi-operator evolutionary algorithms using sequential quadratic programming and improved SHADE-cnEpSin, Information Sciences, 622, pp.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Hong and et al.A variant of the united multi-operator evolutionary algorithms using sequential quadratic programming and improved SHADE-cnEpSin, Information Sciences, 622, pp

Reference 26

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 27

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Observation e603746e-a377-4fc1-b8d5-765124097c24 · outbound

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 28

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Observation d0b3621f-5184-4a60-af02-86b8fd84ca5e · outbound

This paper cites Kumagai and Y.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Kumagai and Y

Reference 29

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 30

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 31

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This paper cites Li and et al.Slime mould algorithm: A new method for stochastic optimization, Future generationcomputersystems, 111, 300-323, (2020),https://doi.org/10.1016/j.future.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Li and et al.Slime mould algorithm: A new method for stochastic optimization, Future generationcomputersystems, 111, 300-323, (2020),https://doi.org/10.1016/j.future

Reference 32

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Observation 9de693fe-999e-480e-8eec-4cf1b57abfca · outbound

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 33

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Observation b61caf23-361e-4a0d-ba5b-786c491a0121 · outbound

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 34

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This paper cites Mazarei, et al.Online boxplot derived outlier detection, International journal of data sci- ence and analytics, 19(1), 83-97, (2025),https://doi.org/10.1007/s41060-024-00559-0.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Mazarei, et al.Online boxplot derived outlier detection, International journal of data sci- ence and analytics, 19(1), 83-97, (2025),https://doi.org/10.1007/s41060-024-00559-0

Reference 35

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Observation 7dff4c56-d845-4501-af61-1703028f640f · outbound

This paper cites Meng and Li.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Meng and Li

Reference 36

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Observation 252bad10-7e81-4608-8fa5-1fc744e34c5f · outbound

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Onay and S.B

Reference 37

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Observation f37e273b-116c-494c-baf4-4de2dbe74c12 · outbound

This paper cites Oszust, G.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Oszust, G

Reference 38

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This paper cites Pacheco-Del-Moral and C.A.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Pacheco-Del-Moral and C.A

Reference 39

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Observation 7bae7a50-7630-4bd2-a94f-74b96567b63a · outbound

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 40

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Observation ebb64383-87ea-4bcb-93a1-dfea7d0ddd57 · outbound

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 41

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Observation 311705c7-3672-40b8-8cfc-8e22c4581380 · outbound

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 42

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Observation bfb0a702-4361-4e5e-89d1-569cc935f6ea · outbound

This paper cites Sallam and et al.IMODEII: An Improved IMODE algorithm based on the Reinforce- ment Learning, 2022 IEEE Congress on Evolutionary Computation (CEC).

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Sallam and et al.IMODEII: An Improved IMODE algorithm based on the Reinforce- ment Learning, 2022 IEEE Congress on Evolutionary Computation (CEC)

Reference 43

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Observation 79e80fed-3b66-4090-b976-35637c4ac1fb · outbound

This paper cites Satapathy, D.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Satapathy, D

Reference 44

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Observation 9fe84edb-1b3d-40d1-a45c-81eec0aecd1c · outbound

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Sroka and M

Reference 45

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Observation b503facd-287a-4ecf-a014-2094941825cc · outbound

This paper cites org/10.1109/CEC.2013.6557555.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations org/10.1109/CEC.2013.6557555

Reference 46

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Observation 17a12de0-caee-4f7f-a478-631945ceb47b · outbound

This paper cites Thangaraj, M.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Thangaraj, M

Reference 47

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 48

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Observation 16428091-0847-4510-9607-4245f27e248b · outbound

This paper cites IEEE, pp.27-33, (2019),https://doi.org/10.1109/ CEC.2019.8789912.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations IEEE, pp.27-33, (2019),https://doi.org/10.1109/ CEC.2019.8789912

Reference 49

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Observation d2106566-5756-43dc-be94-9f1009b83115 · outbound

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Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 50

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Observation 38540257-34aa-4c8d-afac-0abfcef1b15a · outbound

This paper cites Yıldız, B.S.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Yıldız, B.S

Reference 51

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Observation d7b7e82e-0d9a-482d-a62e-46bc9e749dae · outbound

This paper cites an unresolved cited work.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Unresolved cited work

Reference 52

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Observation f60e4009-c6ed-40bf-8fda-8d8d17f02b42 · outbound

This paper cites Zhang, Q.

Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations Zhang, Q

Reference 53

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