DataVXJournal of Bionic EngineeringPaper
journal article·2023-05

A Boosted Communicational Salp Swarm Algorithm: Performance Optimization and Comprehensive Analysis

Chao Lin, Pengjun Wang, Ali Asghar Heidari, Xuehua Zhao, Huiling Chen

Journal of Bionic Engineering· Volume 20· Issue 3
Publisher page
Bibliographic record

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Metaheuristic Optimization Algorithms Research · Neural Networks and Reservoir Computing · Evolutionary Algorithms and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (100+)

Show source-provided references
1. Li, Y., Wang, G., Chen, H. L., Shi, L., & Qin, L. (2013). An ant colony optimization based dimension reduction method for high-dimensional datasets. Journal of Bionic Engineering, 10(2), 231–241. https://doi.org/10.1016/S1672-6529(13)60219-X10.1016/S1672-6529(13)60219-X ↗
2. Ridha, H. M., Gomes, C., Hizam, H., Ahmadipour, M., Heidari, A. A., & Chen, H. L. (2021). Multi-objective optimization and multi-criteria decision-making methods for optimal design of standalone photovoltaic system: A comprehensive review. Renewable & Sustainable Energy Reviews, 135, 110202. https://doi.org/10.1016/j.rser.2020.11020210.1016/j.rser.2020.110202 ↗
3. Liu, Y., Chong, G. S., Heidari, A. A., Chen, H. L., Liang, G. X., Ye, X. J., Cai, Z. N., & Wangg, M. J. (2020). Horizontal and vertical crossover of harris hawk optimizer with nelder-mead simplex for parameter estimation of photovoltaic models. Energy Conversion and Management. https://doi.org/10.1016/j.enconman.2020.11321110.1016/j.enconman.2020.113211 ↗
4. Yu, H. L., Zhao, N. N., Wang, P. J., Chen, H. L., & Li, C. Y. (2020). Chaos-enhanced synchronized bat optimizer. Applied Mathematical Modelling, 77, 1201–1215. https://doi.org/10.1016/j.apm.2019.09.02910.1016/j.apm.2019.09.029 ↗
5. Gupta, S., Deep, K., Heidari, A. A., Moayedi, H., & Chen, H. L. (2021). Harmonized salp chain-built optimization. Engineering with Computers, 37(2), 1049–1079. https://doi.org/10.1007/s00366-019-00871-510.1007/s00366-019-00871-5 ↗
6. Lin, A. J., Wu, Q. Q., Heidari, A. A., Xu, Y. T., Chen, H. L., Geng, W. J., Li, Y. P., & Li, C. Y. (2019). Predicting intentions of students for master programs using a chaos-induced sine cosine-based fuzzy k-nearest neighbor classifier. IEEE Access, 7, 67235–67248. https://doi.org/10.1109/Access.2019.291802610.1109/Access.2019.2918026 ↗
7. Chen, H. L., Li, S. M., Heidari, A. A., Wang, P. J., Li, J. W., Yang, Y. T., Wang, M. J., & Huang, C. C. (2020). Efficient multi-population outpost fruit fly-driven optimizers: Framework and advances in support vector machines. Expert Systems with Applications, 142, 112999. https://doi.org/10.1016/j.eswa.2019.11299910.1016/j.eswa.2019.112999 ↗
8. Zhang, Q., Chen, H. L., Heidari, A. A., Zhao, X. H., Xu, Y. Y., Wang, P. J., Li, Y. P., & Li, C. Y. (2019). Chaos-induced and mutation-driven schemes boosting salp chains-inspired optimizers. IEEE Access, 7, 31243–31261. https://doi.org/10.1109/Access.2019.290230610.1109/Access.2019.2902306 ↗
9. Chen, H., Heidari, A. A., Zhao, X. H., Zhang, L. J., & Chen, H. L. (2020). Advanced orthogonal learning-driven multi-swarm sine cosine optimization: Framework and case studies. Expert Systems with Applications, 144, 113113. https://doi.org/10.1016/j.eswa.2019.11311310.1016/j.eswa.2019.113113 ↗
10. Xu, Z. Z., Hu, Z. Y., Heidari, A. A., Wang, M. J., Zhao, X. H., Chen, H. L., & Cai, X. D. (2020). Orthogonally-designed adapted grasshopper optimization: A comprehensive analysis. Expert Systems with Applications, 150, 113282. https://doi.org/10.1016/j.eswa.2020.11328210.1016/j.eswa.2020.113282 ↗
11. Cao, B., Zhang, W. Z., Wang, X. S., Zhao, J. W., Gu, Y., & Zhang, Y. (2021). A memetic algorithm based on two_arch2 for multi-depot heterogeneous-vehicle capacitated arc routing problem. Swarm and Evolutionary Computation, 63, 100864. https://doi.org/10.1016/j.swevo.2021.10086410.1016/j.swevo.2021.100864 ↗
12. Zhang, J. Z., Zhu, C. Y., Zheng, L. T., & Xu, K. (2021). Rosefusion: Random optimization for online dense reconstruction under fast camera motion. ACM Transactions on Graphics (TOG), 40(4), 1–17.
13. Mou, J. H., Duan, P. Y., Gao, L., Liu, X. H., & Li, J. Q. (2022). An effective hybrid collaborative algorithm for energy-efficient distributed permutation flow-shop inverse scheduling. Future Generation Computer Systems, 128, 521–537.10.1016/j.future.2021.10.003 ↗
14. Zhang, M. W., Chen, Y., & Lin, J. Q. (2021). A privacy-preserving optimization of neighborhood-based recommendation for medical-aided diagnosis and treatment. Ieee Internet of Things Journal, 8(13), 10830–10842. https://doi.org/10.1109/Jiot.2021.305106010.1109/Jiot.2021.3051060 ↗
15. Zhang, H., Luo, G. Y., Li, J. L., & Wang, F. Y. (2022). C2fda: Coarse-to-fine domain adaptation for traffic object detection. Ieee Transactions on Intelligent Transportation Systems, 23(8), 12633–12647. https://doi.org/10.1109/tits.2021.311582310.1109/tits.2021.3115823 ↗
16. Zhan, Z. H., Shi, L., Tan, K. C., & Zhang, J. (2022). A survey on evolutionary computation for complex continuous optimization. Artificial Intelligence Review, 55(1), 59–110. https://doi.org/10.1007/s10462-021-10042-y10.1007/s10462-021-10042-y ↗
17. Xia, J., Wang, Z., Yang, D., Li, R., Liang, G., Chen, H., Heidari, A. A., Turabieh, H., Mafarja, M., & Pan, Z. (2022). Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis. Computers in Biology and Medicine, 143, 105206. https://doi.org/10.1016/j.compbiomed.2021.10520610.1016/j.compbiomed.2021.105206 ↗
18. Xia, J., Yang, D., Zhou, H., Chen, Y., Zhang, H., Liu, T., Heidari, A. A., Chen, H., & Pan, Z. (2022). Evolving kernel extreme learning machine for medical diagnosis via a disperse foraging sine cosine algorithm. Computers in Biology and Medicine, 141, 105137. https://doi.org/10.1016/j.compbiomed.2021.10513710.1016/j.compbiomed.2021.105137 ↗
19. Hu, J., Han, Z., Heidari, A. A., Shou, Y., Ye, H., Wang, L., Huang, X., Chen, H., Chen, Y., & Wu, P. (2022). Detection of covid-19 severity using blood gas analysis parameters and harris hawks optimized extreme learning machine. Computers in Biology and Medicine, 142, 105166. https://doi.org/10.1016/j.compbiomed.2021.10516610.1016/j.compbiomed.2021.105166 ↗
20. Zhan, Z. H., Wang, Z. J., Jin, H., & Zhang, J. (2019). Adaptive distributed differential evolution. IEEE Trans Cybern, 50(11), 4633–4647. https://doi.org/10.1109/TCYB.2019.294487310.1109/TCYB.2019.2944873 ↗
21. Zhan, Z. H., Liu, X. F., Zhang, H. X., Yu, Z. T., Weng, J., Li, Y., Gu, T. L., & Zhang, J. (2016). Cloudde: A heterogeneous differential evolution algorithm and its distributed cloud version. IEEE Transactions on Parallel and Distributed Systems, 28(3), 704–716.10.1109/TPDS.2016.2597826 ↗
22. Cao, B., Gu, Y., Lv, Z. H., Yang, S., Zhao, J. W., & Li, Y. J. (2021). Rfid reader anticollision based on distributed parallel particle swarm optimization. Ieee Internet of Things Journal, 8(5), 3099–3107. https://doi.org/10.1109/Jiot.2020.303347310.1109/Jiot.2020.3033473 ↗
23. Sun, G. J., Li, C. L., & Deng, L. B. (2021). An adaptive regeneration framework based on search space adjustment for differential evolution. Neural Computing & Applications, 33(15), 9503–9519. https://doi.org/10.1007/s00521-021-05708-110.1007/s00521-021-05708-1 ↗
24. Mirjalili, S., Dong, J. S., & Lewis, A. (2019). Nature-inspired optimizers: Theories, literature reviews and applications (Vol. 811). Berlin: Springer.
25. Li, S. M., Chen, H. L., Wang, M. J., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems—The International Journal of Escience, 111, 300–323. https://doi.org/10.1016/j.future.2020.03.05510.1016/j.future.2020.03.055 ↗
26. Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X. F., & Chen, H. L. (2021). Run beyond the metaphor: An efficient optimization algorithm based on runge kutta method. Expert Systems with Applications, 181, 115079. https://doi.org/10.1016/j.eswa.2021.11507910.1016/j.eswa.2021.115079 ↗
27. Ahmadianfar, I., Heidari, A. A., Noshadian, S., Chen, H. L., & Gandomi, A. H. (2022). Info: An efficient optimization algorithm based on weighted mean of vectors. Expert Systems with Applications, 195, 116516. https://doi.org/10.1016/j.eswa.2022.11651610.1016/j.eswa.2022.116516 ↗
28. Wang, Y. T., Han, X. F., & Jin, S. F. (2022). Map based modeling method and performance study of a task offloading scheme with time-correlated traffic and vm repair in mec systems. Wireless Networks. https://doi.org/10.1007/s11276-022-03099-210.1007/s11276-022-03099-2 ↗
29. Yang, Y. T., Chen, H. L., Heidari, A. A., & Gandomi, A. H. (2021). Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, 114864. https://doi.org/10.1016/j.eswa.2021.11486410.1016/j.eswa.2021.114864 ↗
30. Zhu, B., Zhong, Q., Chen, Y., Liao, S., Li, Z., Shi, K., & Sotelo, M. A. (2022). A novel reconstruction method for temperature distribution measurement based on ultrasonic tomography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69(7), 2352–2370. https://doi.org/10.1109/TUFFC.2022.317746910.1109/TUFFC.2022.3177469 ↗
31. Salgotra, R., & Singh, U. (2019). The naked mole-rat algorithm. Neural Computing & Applications, 31(12), 8837–8857. https://doi.org/10.1007/s00521-019-04464-710.1007/s00521-019-04464-7 ↗
32. Zhao, W. G., Wang, L. Y., & Zhang, Z. X. (2019). A novel atom search optimization for dispersion coefficient estimation in groundwater. Future Generation Computer Systems—The International Journal of Escience, 91, 601–610. https://doi.org/10.1016/j.future.2018.05.03710.1016/j.future.2018.05.037 ↗
33. Tu, J., Chen, H. L., Wang, M. J., & Gandomi, A. H. (2021). The colony predation algorithm. Journal of Bionic Engineering, 18(3), 674–710. https://doi.org/10.1007/s42235-021-0050-y10.1007/s42235-021-0050-y ↗
34. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. L. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems-the International Journal of Escience, 97, 849–872. https://doi.org/10.1016/j.future.2019.02.02810.1016/j.future.2019.02.028 ↗
35. Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.00210.1016/j.advengsoft.2017.07.002 ↗
36. Hussien, A. G., Heidari, A. A., Ye, X. J., Liang, G. X., Chen, H. L., & Pan, Z. F. (2022). Boosting whale optimization with evolution strategy and gaussian random walks: An image segmentation method. Engineering with Computers. https://doi.org/10.1007/s00366-021-01542-010.1007/s00366-021-01542-0 ↗
37. Su, H., Zhao, D., Yu, F., Heidari, A. A., Zhang, Y., Chen, H., Li, C., Pan, J., & Quan, S. (2022). Horizontal and vertical search artificial bee colony for image segmentation of covid-19 x-ray images. Computers in Biology and Medicine, 142, 105181. https://doi.org/10.1016/j.compbiomed.2021.10518110.1016/j.compbiomed.2021.105181 ↗
38. Yu, H. L., Song, J. M., Chen, C. C., Heidari, A. A., Liu, J. W., Chen, H. L., Zaguia, A., & Mafarja, M. (2022). Image segmentation of leaf spot diseases on maize using multi-stage cauchy-enabled grey wolf algorithm. Engineering Applications of Artificial Intelligence, 109, 104653. https://doi.org/10.1016/j.engappai.2021.10465310.1016/j.engappai.2021.104653 ↗
39. Zhao, F. Q., Di, S. L., Cao, J., & Tang, J. X. (2021). A novel cooperative multi-stage hyper-heuristic for combination optimization problems. Complex System Modeling and Simulation, 1(2), 91–108.10.23919/CSMS.2021.0010 ↗
40. Liu, P. D., & Gao, H. (2021). A novel green supplier selection method based on the interval type-2 fuzzy prioritized choquet bonferroni means. Ieee-Caa Journal of Automatica Sinica, 8(9), 1549–1566. https://doi.org/10.1109/Jas.2020.100344410.1109/Jas.2020.1003444 ↗
41. Deng, W., Xu, J. J., Zhao, H. M., & Song, Y. J. (2022). A novel gate resource allocation method using improved pso-based qea. Ieee Transactions on Intelligent Transportation Systems, 23(3), 1737–1745. https://doi.org/10.1109/Tits.2020.302579610.1109/Tits.2020.3025796 ↗
42. Yu, H. L., Cheng, X. H., Chen, C. C., Heidari, A. A., Liu, J. W., Cai, Z. N., & Chen, H. L. (2022). Apple leaf disease recognition method with improved residual network. Multimedia Tools and Applications, 81(6), 7759–7782. https://doi.org/10.1007/s11042-022-11915-210.1007/s11042-022-11915-2 ↗
43. Han, X., Han, Y. Y., Chen, Q. D., Li, J. Q., Sang, H. Y., Liu, Y. P., Pan, Q. K., & Nojima, Y. (2021). Distributed flow shop scheduling with sequence-dependent setup times using an improved iterated greedy algorithm. Complex System Modeling and Simulation, 1(3), 198–217.10.23919/CSMS.2021.0018 ↗
44. Gao, D., Wang, G. G., & Pedrycz, W. (2020). Solving fuzzy job-shop scheduling problem using de algorithm improved by a selection mechanism. IEEE Transactions on Fuzzy Systems, 28(12), 3265–3275. https://doi.org/10.1109/Tfuzz.2020.300350610.1109/Tfuzz.2020.3003506 ↗
45. Hu, J., Chen, H. L., Heidari, A. A., Wang, M. J., Zhang, X. Q., Chen, Y., & Pan, Z. F. (2021). Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection. Knowledge-Based Systems, 213, 106684. https://doi.org/10.1016/j.knosys.2020.10668410.1016/j.knosys.2020.106684 ↗
46. Hu, J., Gui, W. Y., Heidari, A. A., Cai, Z. N., Liang, G. X., Chen, H. L., & Pan, Z. F. (2022). Dispersed foraging slime mould algorithm: Continuous and binary variants for global optimization and wrapper-based feature selection. Knowledge-Based Systems, 237, 107761. https://doi.org/10.1016/j.knosys.2021.10776110.1016/j.knosys.2021.107761 ↗
47. Too, J., Liang, G. X., & Chen, H. L. (2021). Memory-based harris hawk optimization with learning agents: A feature selection approach. Engineering with Computers. https://doi.org/10.1007/s00366-021-01479-410.1007/s00366-021-01479-4 ↗
48. Yi, J. H., Deb, S., Dong, J. Y., Alavi, A. H., & Wang, G. G. (2018). An improved nsga-iii algorithm with adaptive mutation operator for big data optimization problems. Future Generation Computer Systems-the International Journal of Escience, 88, 571–585. https://doi.org/10.1016/j.future.2018.06.00810.1016/j.future.2018.06.008 ↗
49. Deng, W., Liu, H. L., Xu, J. J., Zhao, H. M., & Song, Y. J. (2020). An improved quantum-inspired differential evolution algorithm for deep belief network. Ieee Transactions on Instrumentation and Measurement, 69(10), 7319–7327. https://doi.org/10.1109/Tim.2020.298323310.1109/Tim.2020.2983233 ↗
50. Zhao, H. M., Liu, H. D., Xu, J. J., & Deng, W. (2020). Performance prediction using high-order differential mathematical morphology gradient spectrum entropy and extreme learning machine. Ieee Transactions on Instrumentation and Measurement, 69(7), 4165–4172. https://doi.org/10.1109/Tim.2019.294841410.1109/Tim.2019.2948414 ↗
51. Dong, R. Y., Chen, H. L., Heidari, A. A., Turabieh, H., Mafarja, M., & Wang, S. S. (2021). Boosted kernel search: Framework, analysis and case studies on the economic emission dispatch problem. Knowledge-Based Systems, 233, 107529. https://doi.org/10.1016/j.knosys.2021.10752910.1016/j.knosys.2021.107529 ↗
52. Wu, S. H., Zhan, Z. H., & Zhang, J. (2021). Safe: Scale-adaptive fitness evaluation method for expensive optimization problems. IEEE Transactions on Evolutionary Computation, 25(3), 478–491. https://doi.org/10.1109/Tevc.2021.305160810.1109/Tevc.2021.3051608 ↗
53. Li, J. Y., Zhan, Z. H., Wang, C., Jin, H., & Zhang, J. (2020). Boosting data-driven evolutionary algorithm with localized data generation. IEEE Transactions on Evolutionary Computation, 24(5), 923–937. https://doi.org/10.1109/Tevc.2020.297974010.1109/Tevc.2020.2979740 ↗
54. Shi, B. B., Ye, H., Zheng, L., Lyu, J. C., Chen, C., Heidari, A. A., Hu, Z. Y., Chen, H. L., & Wu, P. L. (2021). Evolutionary warning system for covid-19 severity: Colony predation algorithm enhanced extreme learning machine. Computers in Biology and Medicine, 136, 104698. https://doi.org/10.1016/j.compbiomed.2021.10469810.1016/j.compbiomed.2021.104698 ↗
55. Wu, S. B., Mao, P., Li, R. Z., Cai, Z. N., Heidari, A. A., Xia, J. F., Chen, H. L., Mafarja, M., Turabieh, H., & Chen, X. W. (2021). Evolving fuzzy k-nearest neighbors using an enhanced sine cosine algorithm: Case study of lupus nephritis. Computers in Biology and Medicine, 135, 104582. https://doi.org/10.1016/j.compbiomed.2021.10458210.1016/j.compbiomed.2021.104582 ↗
56. Sun, Y. N., Xue, B., Zhang, M. J., & Yen, G. G. (2019). Evolving deep convolutional neural networks for image classification. IEEE Transactions on Evolutionary Computation, 24(2), 394–407.10.1109/TEVC.2019.2916183 ↗
57. Zhang, Y. N., Liu, R. J., Heidari, A. A., Wang, X., Chen, Y., Wang, M. J., & Chen, H. L. (2021). Towards augmented kernel extreme learning models for bankruptcy prediction: Algorithmic behavior and comprehensive analysis. Neurocomputing, 430, 185–212. https://doi.org/10.1016/j.neucom.2020.10.03810.1016/j.neucom.2020.10.038 ↗
58. Cai, Z. N., Gu, J. H., Luo, J., Zhang, Q., Chen, H. L., Pan, Z. F., Li, Y. P., & Li, C. Y. (2019). Evolving an optimal kernel extreme learning machine by using an enhanced grey wolf optimization strategy. Expert Systems with Applications, 138, 112814. https://doi.org/10.1016/j.eswa.2019.07.03110.1016/j.eswa.2019.07.031 ↗
59. Zeng, G. Q., Lu, K. D., Dai, Y. X., Zhang, Z. J., Chen, M. R., Zheng, C. W., Wu, D., & Peng, W. W. (2014). Binary-coded extremal optimization for the design of pid controllers. Neurocomputing, 138, 180–188. https://doi.org/10.1016/j.neucom.2014.01.04610.1016/j.neucom.2014.01.046 ↗
60. Zeng, G. Q., Chen, J., Dai, Y. X., Li, L. M., Zheng, C. W., & Chen, M. R. (2015). Design of fractional order pid controller for automatic regulator voltage system based on multi-objective extremal optimization. Neurocomputing, 160, 173–184. https://doi.org/10.1016/j.neucom.2015.02.05110.1016/j.neucom.2015.02.051 ↗
61. Zeng, G. Q., Xie, X. Q., Chen, M. R., & Weng, J. (2019). Adaptive population extremal optimization-based pid neural network for multivariable nonlinear control systems. Swarm and Evolutionary Computation, 44, 320–334. https://doi.org/10.1016/j.swevo.2018.04.00810.1016/j.swevo.2018.04.008 ↗
62. Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82.10.1109/4235.585893 ↗
63. Kansal, V., & Dhillon, J. S. (2020). Emended salp swarm algorithm for multiobjective electric power dispatch problem. Applied Soft Computing, 90, 106172. https://doi.org/10.1016/j.asoc.2020.10617210.1016/j.asoc.2020.106172 ↗
64. Abbassi, A., Abbassi, R., Heidari, A. A., Oliva, D., Chen, H. L., Habib, A., Jemli, M., & Wang, M. J. (2020). Parameters identification of photovoltaic cell models using enhanced exploratory salp chains-based approach. Energy. https://doi.org/10.1016/j.energy.2020.11733310.1016/j.energy.2020.117333 ↗
65. Zhang, Y. N., Liu, R. J., Wang, X., Chen, H. L., & Li, C. Y. (2021). Boosted binary harris hawks optimizer and feature selection. Engineering with Computers, 37(4), 3741–3770. https://doi.org/10.1007/s00366-020-01028-510.1007/s00366-020-01028-5 ↗
66. Yu, H. L., Yuan, K., Li, W. S., Zhao, N. N., Chen, W. B., Huang, C. C., Chen, H. L., & Wang, M. J. (2021). Improved butterfly optimizer-configured extreme learning machine for fault diagnosis. Complexity. https://doi.org/10.1155/2021/631501010.1155/2021/6315010 ↗
67. Aljarah, I., Habib, M., Faris, H., Al-Madi, N., Heidari, A. A., Mafarja, M., Abd Elaziz, M., & Mirjalili, S. (2020). A dynamic locality multi-objective salp swarm algorithm for feature selection. Computers & Industrial Engineering, 147, 106628. https://doi.org/10.1016/j.cie.2020.10662810.1016/j.cie.2020.106628 ↗
68. Neggaz, N., Ewees, A. A., Abd Elaziz, M., & Mafarja, M. (2020). Boosting salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selection. Expert Systems with Applications, 145, 113103. https://doi.org/10.1016/j.eswa.2019.11310310.1016/j.eswa.2019.113103 ↗
69. Ren, H., Li, J., Chen, H. L., & Li, C. Y. (2021). Stability of salp swarm algorithm with random replacement and double adaptive weighting. Applied Mathematical Modelling, 95, 503–523. https://doi.org/10.1016/j.apm.2021.02.00210.1016/j.apm.2021.02.002 ↗
70. Ren, H., Li, J., Chen, H. L., & Li, C. Y. (2021). Adaptive levy-assisted salp swarm algorithm: Analysis and optimization case studies. Mathematics and Computers in Simulation, 181, 380–409. https://doi.org/10.1016/j.matcom.2020.09.02710.1016/j.matcom.2020.09.027 ↗
71. Saafan, M. M., & El-Gendy, E. M. (2021). Iwossa: An improved whale optimization salp swarm algorithm for solving optimization problems. Expert Systems with Applications, 176, 114901. https://doi.org/10.1016/j.eswa.2021.11490110.1016/j.eswa.2021.114901 ↗
72. Zhang, H. L., Wang, Z. Y., Chen, W. B., Heidari, A. A., Wang, M. J., Zhao, X. H., Liang, G. X., Chen, H. L., & Zhang, X. (2021). Ensemble mutation-driven salp swarm algorithm with restart mechanism: Framework and fundamental analysis. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2020.11389710.1016/j.eswa.2020.113897 ↗
73. Nautiyal, B., Prakash, R., Vimal, V., Liang, G. X., & Chen, H. L. (2021). Improved salp swarm algorithm with mutation schemes for solving global optimization and engineering problems. Engineering with Computers. https://doi.org/10.1007/s00366-020-01252-z10.1007/s00366-020-01252-z ↗
74. Liang, J.J., Qu, B.Y., & Suganthan, P.N. (2013). Problem definitions and evaluation criteria for the cec 2014 special session and competition on single objective real-parameter numerical optimization. Technical Report 201212. (Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore)
75. Zhong, T., Cheng, M., Lu, S. P., Dong, X. T., & Li, Y. (2021). Rcen: A deep-learning-based background noise suppression method for das-vsp records. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.
76. Wu, X. X., Zheng, W., Chen, X., Zhao, Y., Yu, T. T., & Mu, D. J. (2021). Improving high-impact bug report prediction with combination of interactive machine learning and active learning. Information and Software Technology, 133, 106530. https://doi.org/10.1016/j.infsof.2021.10653010.1016/j.infsof.2021.106530 ↗
77. Li, J., Xu, K., Chaudhuri, S., Yumer, E., Zhang, H., & Guibas, L. (2017). Grass: Generative recursive autoencoders for shape structures. ACM Transactions on Graphics (TOG), 36(4), 1–14.
78. Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm & Evolutionary Computation, 1(1), 3–18.10.1016/j.swevo.2011.02.002 ↗
79. García, S., Fernández, A., Luengo, J., & Herrera, F. (2010). Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences, 180(10), 2044–2064.10.1016/j.ins.2009.12.010 ↗
80. Lu, S. Y., Guo, J. L., Liu, S., Yang, B., Liu, M. Z., Yin, L. R., & Zheng, W. F. (2022). An improved algorithm of drift compensation for olfactory sensors. Applied Sciences-Basel, 12(19), 9529. https://doi.org/10.3390/app1219952910.3390/app12199529 ↗
81. Zhang, Z. Y., Tian, J. W., Huang, W. Z., Yin, L. R., Zheng, W. F., & Liu, S. (2021). A haze prediction method based on one-dimensional convolutional neural network. Atmosphere, 12(10), 1327. https://doi.org/10.3390/atmos1210132710.3390/atmos12101327 ↗
82. Shang, K. L., Chen, Z. Y., Liu, Z. X., Song, L. H., Zheng, W. F., Yang, B., Liu, S., & Yin, L. R. (2021). Haze prediction model using deep recurrent neural network. Atmosphere, 12(12), 1625. https://doi.org/10.3390/atmos1212162510.3390/atmos12121625 ↗
83. Zhang, H. L., Cai, Z. N., Ye, X. J., Wang, M. J., Kuang, F. J., Chen, H. L., Li, C. Y., & Li, Y. P. (2022). A multi-strategy enhanced salp swarm algorithm for global optimization. Engineering with Computers, 38(2), 1177–1203. https://doi.org/10.1007/s00366-020-01099-410.1007/s00366-020-01099-4 ↗
84. Qais, M. H., Hasanien, H. M., & Alghuwainem, S. (2019). Enhanced salp swarm algorithm: Application to variable speed wind generators. Engineering Applications of Artificial Intelligence, 80, 82–96. https://doi.org/10.1016/j.engappai.2019.01.01110.1016/j.engappai.2019.01.011 ↗
85. Sayed, G. I., Khoriba, G., & Haggag, M. H. (2018). A novel chaotic salp swarm algorithm for global optimization and feature selection. Applied Intelligence, 48(10), 3462–3481. https://doi.org/10.1007/s10489-018-1158-610.1007/s10489-018-1158-6 ↗
86. Heidari, A. A., Aljarah, I., Faris, H., Chen, H. L., Luo, J., & Mirjalili, S. (2020). An enhanced associative learning-based exploratory whale optimizer for global optimization. Neural Computing & Applications, 32(9), 5185–5211. https://doi.org/10.1007/s00521-019-04015-010.1007/s00521-019-04015-0 ↗
87. Tubishat, M., Abushariah, M., Idris, N., & Aljarah, I. (2019). Improved whale optimization algorithm for feature selection in arabic sentiment analysis. Applied Intelligence, 49(5), 1688–1707. https://doi.org/10.1007/s10489-018-1334-810.1007/s10489-018-1334-8 ↗
88. Gupta, S., & Deep, K. (2020). A novel hybrid sine cosine algorithm for global optimization and its application to train multilayer perceptrons. Applied Intelligence, 50(4), 993–1026. https://doi.org/10.1007/s10489-019-01570-w10.1007/s10489-019-01570-w ↗
89. Zhu, W., Ma, C., Zhao, X. H., Wang, M. J., Heidari, A. A., Chen, H. L., & Li, C. Y. (2020). Evaluation of sino foreign cooperative education project using orthogonal sine cosine optimized kernel extreme learning machine. Ieee Access, 8, 61107–61123. https://doi.org/10.1109/Access.2020.298196810.1109/Access.2020.2981968 ↗
90. Gupta, S., & Deep, K. (2019). A novel random walk grey wolf optimizer. Swarm and Evolutionary Computation, 44, 101–112. https://doi.org/10.1016/j.swevo.2018.01.00110.1016/j.swevo.2018.01.001 ↗
91. Liang, H. J., Liu, Y. G., Shen, Y. J., Li, F. Z., & Man, Y. C. (2018). A hybrid bat algorithm for economic dispatch with random wind power. Ieee Transactions on Power Systems, 33(5), 5052–5061. https://doi.org/10.1109/Tpwrs.2018.281271110.1109/Tpwrs.2018.2812711 ↗
92. Lyu, S. L., Li, Z., Huang, Y. L., Wang, J. H., & Hu, J. (2019). Improved self-adaptive bat algorithm with step-control and mutation mechanisms. Journal of Computational Science, 30, 65–78. https://doi.org/10.1016/j.jocs.2018.11.00210.1016/j.jocs.2018.11.002 ↗
93. Tessema, B., & Yen, G. G. (2009). An adaptive penalty formulation for constrained evolutionary optimization. Ieee Transactions on Systems Man and Cybernetics Part a-Systems and Humans, 39(3), 565–578. https://doi.org/10.1109/Tsmca.2009.201333310.1109/Tsmca.2009.2013333 ↗
94. Takahama, T., & Sakai, S. (2006). Constrained optimization by the ε constrained differential evolution with gradient-based mutation and feasible elites. 2006 IEEE International Conference on Evolutionary Computation, Barcelona, Spain.10.1109/CEC.2006.1688283 ↗
95. Wang, Y., & Cai, Z. X. (2012). Combining multiobjective optimization with differential evolution to solve constrained optimization problems. IEEE Transactions on Evolutionary Computation, 16(1), 117–134. https://doi.org/10.1109/Tevc.2010.209358210.1109/Tevc.2010.2093582 ↗
96. Wang, Y., Cai, Z., Zhou, Y., & Zeng, W. (2008). An adaptive tradeoff model for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, 12(1), 80–92. https://doi.org/10.1109/Tevc.2007.90285110.1109/Tevc.2007.902851 ↗
97. Fan, Y., Wang, P. J., Heidari, A. A., Wang, M. J., Zhao, X. H., Chen, H. L., & Li, C. Y. (2020). Boosted hunting-based fruit fly optimization and advances in real-world problems. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2020.11350210.1016/j.eswa.2020.113502 ↗
98. Shayanfar, H., & Gharehchopogh, F. S. (2018). Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems. Applied Soft Computing, 71, 728–746. https://doi.org/10.1016/j.asoc.2018.07.03310.1016/j.asoc.2018.07.033 ↗
99. Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems, 36(10), 5887–5958. https://doi.org/10.1002/int.2253510.1002/int.22535 ↗
100. Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering. https://doi.org/10.1016/j.cie.2021.10740810.1016/j.cie.2021.107408 ↗