Skip to main content
SpringerLink
Log in

Improved Salp Swarm Algorithm with mutation schemes for solving global optimization and engineering problems

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Salp Swarm Algorithm (SSA) is a recent metaheuristic algorithm developed from the inspiration of salps’ swarming behavior and characterized by a simple search mechanism with few handling parameters. However, in solving complex optimization problems, the SSA may suffer from the slow convergence rate and a trend of falling into sub-optimal solutions. To overcome these shortcomings, in this study, versions of the SSA by employing Gaussian, Cauchy, and levy-flight mutation schemes are proposed. The Gaussian mutation is used to enhance neighborhood-informed ability. The Cauchy mutation is used to generate large steps of mutation to increase the global search ability. The levy-flight mutation is used to increase the randomness of salps during the search. These versions are tested on 23 standard benchmark problems using statistical and convergence curves investigations, and the best-performed optimizer is compared with some other state-of-the-art algorithms. The experiments demonstrate the impact of mutation schemes, especially Gaussian mutation, in boosting the exploitation and exploration abilities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. https://aliasgharheidari.com/HHO.html.

  2. https://aliasgharheidari.com/SMA.html.

References

  1. Abbassi A, Abbassi R, Heidari AA, Oliva D, Chen H, Habib A, Jemli M, Wang M (2020) Parameters identification of photovoltaic cell models using enhanced exploratory salp chains-based approach. Energy 198:117,333. https://doi.org/10.1016/j.energy.2020.117333

    Article  Google Scholar 

  2. Abedini M, Zhang C (2020) Performance assessment of concrete and steel material models in ls-dyna for enhanced numerical simulation, a state of the art review. Arch Comput Methods Eng:1–22

  3. Abualigah L, Shehab M, Alshinwan M, Alabool H (2019) Salp swarm algorithm: a comprehensive survey. Neural Comput Appl:1–21

  4. Abusnaina AA, Ahmad S, Jarrar R, Mafarja M (2018) Training neural networks using salp swarm algorithm for pattern classification. In: Proceedings of the 2nd international conference on future networks and distributed systems, pp 1–6

  5. Akhtar S, Tai K, Ray T (2002) A socio-behavioural simulation model for engineering design optimization. Eng Optim 34(4):341–354

    Article  Google Scholar 

  6. Ala’M AZ, Heidari AA, Habib M, Faris H, Aljarah I, Hassonah MA (2020) Salp chain-based optimization of support vector machines and feature weighting for medical diagnostic information systems. Springer, Berlin, pp 11–34

    Google Scholar 

  7. Aljarah I, Mafarja M, Heidari AA, Faris H, Zhang Y, Mirjalili S (2018) Asynchronous accelerating multi-leader salp chains for feature selection. Appl Soft Comput 71:964–979

    Article  Google Scholar 

  8. Aljarah I, Mafarja M, Heidari AA, Faris H, Mirjalili S (2019) Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach. Knowl Inf Syst. https://doi.org/10.1007/s10115-019-01358-x

    Article  Google Scholar 

  9. Aljarah I, Habib M, Faris H, Al-Madi N, Heidari AA, Mafarja M, Abd Elaziz M, Mirjalili S (2020) A dynamic locality multi-objective salp swarm algorithm for feature selection. Comput Ind Eng 147(106):628

    Google Scholar 

  10. Arora JS (2004) Introduction to optimum design. Elsevier, Oxford

    Book  Google Scholar 

  11. Belegundu AD, Arora JS (1985) A study of mathematical programming methods for structural optimization part i: Theory. Int J Num Methods Eng 21(9):1583–1599

    Article  MATH  Google Scholar 

  12. Cai C, Gao X, Teng Q, Kiran R, Liu J, Wei Q, Shi Y (2020a) Hot isostatic pressing of a near a-ti alloy: Temperature optimization, microstructural evolution and mechanical performance evaluation. Mater Sci Eng A:140426

  13. Cai C, Wu X, Liu W, Zhu W, Chen H, Qiu JCD, Sun CN, Liu J, Wei Q, Shi Y (2020b) Selective laser melting of near-a titanium alloy ti-6al-2zr-1mo-1v: Parameter optimization, heat treatment and mechanical performance. J Mater Sci Technol

  14. Cao B, Zhao J, Gu Y, Fan S, Yang P (2019a) Security-aware industrial wireless sensor network deployment optimization. IEEE Trans Ind Inform 16(8):5309–5316

    Article  Google Scholar 

  15. Cao B, Zhao J, Yang P, Gu Y, Muhammad K, Rodrigues JJ, de Albuquerque VHC (2019b) Multiobjective 3-d topology optimization of next-generation wireless data center network. IEEE Trans Ind Inform 16(5):3597–3605

    Article  Google Scholar 

  16. Cao B, Dong W, Lv Z, Gu Y, Singh S, Kumar P (2020a) Hybrid microgrid many-objective sizing optimization with fuzzy decision. IEEE Trans Fuzzy Syst

  17. Cao B, Fan S, Zhao J, Yang P, Muhammad K, Tanveer M (2020b) Quantum-enhanced multiobjective large-scale optimization via parallelism. Swarm Evol Comput 57(100):697. https://doi.org/10.1016/j.swevo.2020.100697

    Article  Google Scholar 

  18. Cao B, Wang X, Zhang W, Song H, Lv Z (2020c) A many-objective optimization model of industrial internet of things based on private blockchain. IEEE Netw 34(5):78–83

    Article  Google Scholar 

  19. Cao B, Zhao J, Gu Y, Ling Y, Ma X (2020d) Applying graph-based differential grouping for multiobjective large-scale optimization. Swarm Evol Comput 53(100):626

    Google Scholar 

  20. Cao Y, Li Y, Zhang G, Jermsittiparsert K, Nasseri M (2020e) An efficient terminal voltage control for pemfc based on an improved version of whale optimization algorithm. Energy Rep 6:530–542

    Article  Google Scholar 

  21. Cao Y, Wang Q, Cheng W, Nojavan S, Jermsittiparsert K (2020f) Risk-constrained optimal operation of fuel cell/photovoltaic/battery/grid hybrid energy system using downside risk constraints method. Int J Hydrogen Energy 45(27):14,108–14,118. https://doi.org/10.1016/j.ijhydene.2020.03.090

    Article  Google Scholar 

  22. Chantar H, Mafarja M, Alsawalqah H, Heidari AA, Aljarah I, Faris H (2020) Feature selection using binary grey wolf optimizer with elite-based crossover for Arabic text classification. Neural Comput Appl 32(16):12,201–12,220

    Article  Google Scholar 

  23. Chao L, Zhang K, Li Z, Zhu Y, Wang J, Yu Z (2018) Geographically weighted regression based methods for merging satellite and gauge precipitation. J Hydrol 558:275–289

    Article  Google Scholar 

  24. Chen HL, Wang G, Ma C, Cai ZN, Liu WB, Wang SJ (2016) An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson’s disease. Neurocomputing 184:131–144

    Article  Google Scholar 

  25. Chen Y, He L, Guan Y, Lu H, Li J (2017) Life cycle assessment of greenhouse gas emissions and water-energy optimization for shale gas supply chain planning based on multi-level approach: case study in Barnett, Marcellus, Fayetteville, and Haynesville shales. Energy Convers Manag 134:382–398

    Article  Google Scholar 

  26. Chen H, Qiao H, Xu L, Feng Q, Cai K (2019a) A fuzzy optimization strategy for the implementation of rbf lssvr model in vis-nir analysis of pomelo maturity. IEEE Trans Ind Inform 15(11):5971–5979

    Article  Google Scholar 

  27. Chen H, Yang C, Heidari AA, Zhao X (2019b) An efficient double adaptive random spare reinforced whale optimization algorithm. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2019.113018

    Article  Google Scholar 

  28. Chen H, Chen A, Xu L, Xie H, Qiao H, Lin Q, Cai K (2020a) A deep learning cnn architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources. Agric Water Manag 240(106):303

    Google Scholar 

  29. Chen H, Fan DL, Fang L, Huang W, Huang J, Cao C, Yang L, He Y, Zeng L (2020b) Particle swarm optimization algorithm with mutation operator for particle filter noise reduction in mechanical fault diagnosis. Int J Pattern Recogn Artif Intell:2058012

  30. Chen H, Heidari AA, Chen H, Wang M, Pan Z, Gandomi AH (2020) Multi-population differential evolution-assisted Harris Hawks optimization: framework and case studies. Future Gen Comput Syst 111:175–198. https://doi.org/10.1016/j.future.2020.04.008

    Article  Google Scholar 

  31. Chen H, Li S, Heidari AA, Wang P, Li J, Yang Y, Wang M, Huang C (2020d) Efficient multi-population outpost fruit fly-driven optimizers: framework and advances in support vector machines. Expert Syst Appl 142(112):999

    Google Scholar 

  32. Chen H, Zhang G, Fan D, Fang L, Huang L (2020e) Nonlinear lamb wave analysis for microdefect identification in mechanical structural health assessment. Measurement:108026

  33. Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127

    Article  Google Scholar 

  34. Coello CAC (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(11–12):1245–1287

    Article  MathSciNet  MATH  Google Scholar 

  35. Elaziz MA, Heidari AA, Fujita H, Moayedi H (2020) A competitive chain-based Harris Hawks optimizer for global optimization and multi-level image thresholding problems. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2020.106347

    Article  Google Scholar 

  36. Fan Y, Wang P, Heidari AA, Wang M, Zhao X, Chen H, Li C (2020a) Boosted hunting-based fruit fly optimization and advances in real-world problems. Expert Syst Appl:113502

  37. Fan Y, Wang P, Heidari AA, Wang M, Zhao X, Chen H, Li C (2020b) Rationalized fruit fly optimization with sine cosine algorithm: a comprehensive analysis. Expert Syst Appl:113486

  38. Faris H, Heidari AA, Ala’M AZ, Mafarja M, Aljarah I, Eshtay M, Mirjalili S (2020a) Time-varying hierarchical chains of salps with random weight networks for feature selection. Expert Syst Appl 140(112):898

    Google Scholar 

  39. Faris H, Mirjalili S, Aljarah I, Mafarja M, Heidari AA (2020b) Salp swarm algorithm: theory, literature review, and application in extreme learning machines. Springer, Berlin, pp 185–199

    Google Scholar 

  40. Fu X, Yang Y (2020) Modeling and analysis of cascading node-link failures in multi-sink wireless sensor networks. Reliab Eng Syst Saf 197(106):815

    Google Scholar 

  41. Fu X, Fortino G, Li W, Pace P, Yang Y (2019) Wsns-assisted opportunistic network for low-latency message forwarding in sparse settings. Future Gen Comput Syst 91:223–237

    Article  Google Scholar 

  42. Fu X, Pace P, Aloi G, Yang L, Fortino G (2020) Topology optimization against cascading failures on wireless sensor networks using a memetic algorithm. Comput Netw:107327

  43. Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

    Article  Google Scholar 

  44. Gao N, Luo D, Cheng B, Hou H (2020) Teaching-learning-based optimization of a composite metastructure in the 0–10 kHz broadband sound absorption range. J Acoust Soc Am 148(2):EL125-EL129

    Article  Google Scholar 

  45. 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. Inf Sci 180(10):2044–2064

    Article  Google Scholar 

  46. Gholipour G, Zhang C, Mousavi AA (2020a) Nonlinear numerical analysis and progressive damage assessment of a cable-stayed bridge pier subjected to ship collision. Mar Struct 69(102):662

    Google Scholar 

  47. Gholipour G, Zhang C, Mousavi AA (2020b) Numerical analysis of axially loaded rc columns subjected to the combination of impact and blast loads. Eng Struct 219(110):924

    Google Scholar 

  48. Guo J, Zhang X, Gu F, Zhang H, Fan Y (2020a) Does air pollution stimulate electric vehicle sales? empirical evidence from twenty major cities in china. J Clean Prod 249(119):372

    Google Scholar 

  49. Guo L, Sriyakul T, Nojavan S, Jermsittiparsert K (2020b) Risk-based traded demand response between consumers’ aggregator and retailer using downside risk constraints technique. IEEE Access 8:90,957–90,968

    Article  Google Scholar 

  50. Gupta S, Deep K (2019a) A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Syst Appl 119:210–230

    Article  Google Scholar 

  51. Gupta S, Deep K (2019b) A novel random walk grey wolf optimizer. Swarm Evol Comput 44:101–112

    Article  Google Scholar 

  52. Gupta S, Deep K, Heidari AA, Moayedi H, Chen H (2019a) Harmonized salp chain-built optimization. Eng Comput:1–31

  53. Gupta S, Deep K, Heidari AA, Moayedi H, Chen H (2019b) Harmonized salp chain-built optimization. Eng Comput. https://doi.org/10.1007/s00366-019-00871-5

    Article  Google Scholar 

  54. Gupta S, Deep K, Heidari AA, Moayedi H, Wang M (2020a) Opposition-based learning harris hawks optimization with advanced transition rules: principles and analysis. Expert Syst Appl:113510

  55. Gupta S, Deep K, Mirjalili S (2020b) An efficient equilibrium optimizer with mutation strategy for numerical optimization. Appl Soft Comput 96(106):542

    Google Scholar 

  56. Hegazy AE, Makhlouf M, El-Tawel GS (2020) Improved salp swarm algorithm for feature selection. J King Saud Univ Comput Inf Sci 32(3):335–344

    Google Scholar 

  57. Heidari AA, Pahlavani P (2017) An efficient modified grey wolf optimizer with lévy flight for optimization tasks. Appl Soft Comput 60:115–134

    Article  Google Scholar 

  58. Heidari AA, Abbaspour RA, Chen H (2019a) Efficient boosted grey wolf optimizers for global search and kernel extreme learning machine training. Appl Soft Comput 81(105):521

    Google Scholar 

  59. Heidari AA, Aljarah I, Faris H, Chen H, Luo J, Mirjalili S (2019b) An enhanced associative learning-based exploratory whale optimizer for global optimization. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04015-0

    Article  Google Scholar 

  60. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019c) Harris hawks optimization: algorithm and applications. Future Gen Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.0280

    Article  Google Scholar 

  61. Heidari AA, Yin Y, Mafarja M, Jalali SMJ, Dong JS, Mirjalili S (2020) Efficient moth-flame-based neuroevolution models. Springer, Berlin, pp 51–66

    Google Scholar 

  62. Higashi N, Iba H (2003) Particle swarm optimization with gaussian mutation. In: Proceedings of the 2003 IEEE swarm intelligence symposium. SIS’03 (Cat. No. 03EX706), IEEE, pp 72–79

  63. Hsu YL, Liu TC (2007) Developing a fuzzy proportional-derivative controller optimization engine for engineering design optimization problems. Eng Optim 39(6):679–700

    Article  MathSciNet  Google Scholar 

  64. Hu L, Hong G, Ma J, Wang X, Chen H (2015) An efficient machine learning approach for diagnosis of paraquat-poisoned patients. Comput Biol Med 59:116–124

    Article  Google Scholar 

  65. Hu X, Ma P, Gao B, Zhang M (2019) An integrated step-up inverter without transformer and leakage current for grid-connected photovoltaic system. IEEE Trans Power Electron 34(10):9814–9827

    Article  Google Scholar 

  66. Ibrahim HT, Mazher WJ, Ucan ON, Bayat O (2017) Feature selection using salp swarm algorithm for real biomedical datasets. IJCSNS 17(12):13–20

    Google Scholar 

  67. Ibrahim RA, Ewees AA, Oliva D, Abd Elaziz M, Lu S (2019) Improved salp swarm algorithm based on particle swarm optimization for feature selection. J Ambient Intell Hum Comput 10(8):3155–3169

    Article  Google Scholar 

  68. Jiang Q, Wang G, Jin S, Li Y, Wang Y (2013) Predicting human microRNA-disease associations based on support vector machine. Int J Data Min Bioinform 8(3):282

    Article  Google Scholar 

  69. Ku KJ, Rao SS, Chen L (1998) Taguchi-aided search method for design optimization of engineering systems. Eng Optim 30(1):1–23

    Article  Google Scholar 

  70. Li C, Hou L, Sharma BY, Li H, Chen C, Li Y, Zhao X, Huang H, Cai Z, Chen H (2018) Developing a new intelligent system for the diagnosis of tuberculous pleural effusion. Comput Methods Programs Biomed 153:211–225

    Article  Google Scholar 

  71. Li T, Xu M, Zhu C, Yang R, Wang Z, Guan Z (2019a) A deep learning approach for multi-frame in-loop filter of hevc. IEEE Trans Image Process 28(11):5663–5678

    Article  MathSciNet  MATH  Google Scholar 

  72. Li X, Zhu Y, Wang J (2019b) Highly efficient privacy preserving location-based services with enhanced one-round blind filter. IEEE Trans Emerg Top Comput. https://doi.org/10.1109/TETC.2019.29263851

    Article  Google Scholar 

  73. Li C, Sun L, Xu Z, Wu X, Liang T, Shi W (2020) Experimental Investigation and Error Analysis of High Precision FBG Displacement Sensor for Structural Health Monitoring. Int J Struct Stab Dyn 20(06):2040011

    Article  Google Scholar 

  74. Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Future Gen Comput Syst 111:300–323. https://doi.org/10.1016/j.future.2020.03.0552

    Article  Google Scholar 

  75. Liu J, Wu C, Wu G, Wang X (2015) A novel differential search algorithm and applications for structure design. Appl Math Comput 268:246–269

    MATH  Google Scholar 

  76. Liu D, Wang S, Huang D, Deng G, Zeng F, Chen H (2016a) Medical image classification using spatial adjacent histogram based on adaptive local binary patterns. Comput Biol Med 72:185–200

    Article  Google Scholar 

  77. Liu S, Chan FT, Ran W (2016b) Decision making for the selection of cloud vendor: an improved approach under group decision-making with integrated weights and objective/subjective attributes. Expert Syst Appl 55:37–47

    Article  Google Scholar 

  78. Liu E, Li W, Cai H, Peng S (2019) Formation mechanism of trailing oil in product oil pipeline. Processes 7(1):7

    Article  Google Scholar 

  79. Liu S, Yu W, Chan FTS, Niu B (2020a) A variable weight-based hybrid approach for multi-attribute group decision making under interval-valued intuitionistic fuzzy sets. Int J Intell Syst. https://doi.org/10.1002/int.223293

    Article  Google Scholar 

  80. Liu Y, Shi Y, Chen H, Asghar Heidari A, Gui W, Wang M, Chen H, Li C (2020b) Chaos-assisted multi-population salp swarm algorithms: framework and case studies. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.114369

    Article  Google Scholar 

  81. Long W, Jiao J, Liang X, Tang M (2018) An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization. Eng Appl Artif Intell 68:63–80

    Article  Google Scholar 

  82. Luo J, Chen H, Xu Y, Huang H, Zhao X et al (2018) An improved grasshopper optimization algorithm with application to financial stress prediction. Appl Math Model 64:654–668

    Article  MathSciNet  MATH  Google Scholar 

  83. Luo J, Chen H, Heidari AA, Xu Y, Zhang Q, Li C (2019) Multi-strategy boosted mutative whale-inspired optimization approaches. Appl Math Model 73:109–123. https://doi.org/10.1016/j.apm.2019.03.0465

    Article  MathSciNet  MATH  Google Scholar 

  84. Lv Z, Kumar N (2020) Software defined solutions for sensors in 6g/ioe. Comput Commun 153:42–47

    Article  Google Scholar 

  85. Lv Z, Qiao L (2020) Deep belief network and linear perceptron based cognitive computing for collaborative robots. Appl Soft Comput:106300

  86. Mafarja M, Heidari AA, Habib M, Faris H, Thaher T, Aljarah I (2020) Augmented whale feature selection for iot attacks: structure, analysis and applications. Future Gen Comput Syst 112:18–40. https://doi.org/10.1016/j.future.2020.05.0206

    Article  Google Scholar 

  87. Mezura-Montes E, Coello CAC (2008) An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int J Gen Syst 37(4):443–473

    Article  MathSciNet  MATH  Google Scholar 

  88. Mezura-Montes E, Coello CC, Landa-Becerra R (2003) Engineering optimization using simple evolutionary algorithm. In: Proceedings. 15th IEEE international conference on tools with artificial intelligence, IEEE, pp 149–156

  89. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249

    Article  Google Scholar 

  90. Mirjalili S (2016) Sca: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133

    Article  Google Scholar 

  91. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  92. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  93. Mousavi AA, Zhang C, Masri SF, Gholipour G (2020) Structural damage localization and quantification based on a Ceemdan Hilbert transform neural network approach: A model steel truss bridge case study. Sensors 20(5):1271

    Article  Google Scholar 

  94. Ni T, Chang H, Song T, Xu Q, Huang Z, Liang H, Yan A, Wen X (2020) Non-intrusive online distributed pulse shrinking-based interconnect testing in 2.5d ic. IEEE Transact Circuits Syst II Express Briefs 67(11):2657–2661. https://doi.org/10.1109/TCSII.2019.29628247

    Article  Google Scholar 

  95. Nowacki H (1973) Optimization in pre-contract ship design. In: International conference on computer applications in the automation of shipyard operation and ship design, held by IFIP/IFAC/JSNA, Tokyo, Japan, Aug 28–30, 1973

  96. Pang R, Xu B, Kong X, Zou D (2018) Seismic fragility for high cfrds based on deformation and damage index through incremental dynamic analysis. Soil Dyn Earthq Eng 104:432–436

    Article  Google Scholar 

  97. Park Y, Chang M, Lee TY (2007) A new deterministic global optimization method for general twice-differentiable constrained nonlinear programming problems. Eng Optim 39(4):397–411

    Article  MathSciNet  Google Scholar 

  98. Qian J, Feng S, Li Y, Tao T, Han J, Chen Q, Zuo C (2020) Single-shot absolute 3D shape measurement with deep-learning-based color fringe projection profilometry. Opt Lett 45(7):1842

    Article  Google Scholar 

  99. Qian J, Feng S, Tao T, Hu Y, Li Y, Chen Q, Zuo C (2020) Deep-learning-enabled geometric constraints and phase unwrapping for single-shot absolute 3D shape measurement. APL Photonics 5(4):046105

    Article  Google Scholar 

  100. Qiu T, Shi X, Wang J, Li Y, Qu S, Cheng Q, Cui T, Sui S (2019) Deep learning: a rapid and efficient route to automatic metasurface design. Adv Sci 6(12):1900,128

    Article  Google Scholar 

  101. Qu K, Wei L, Zou Q (2019) A review of DNA-binding proteins prediction methods. Current Bioinform 14(3):246–254

    Article  Google Scholar 

  102. Qu S, Han Y, Wu Z, Raza H (2020) Consensus modeling with asymmetric cost based on data-driven robust optimization. Group Decis Negot:1–38

  103. Rao RV, Savsani VJ, Vakharia D (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aid Des 43(3):303–315

    Article  Google Scholar 

  104. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) Gsa: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  105. Ray T, Saini P (2001) Engineering design optimization using a swarm with an intelligent information sharing among individuals. Eng Optim 33(6):735–748

    Article  Google Scholar 

  106. Ridha HM, Gomes C, Hizam H, Ahmadipour M, Heidari AA, Chen H (2020a) Multi-objective optimization and multi-criteria decision-making methods for optimal design of standalone photovoltaic system: A comprehensive review. Renew Sustain Energy Rev 135(110):202

    Google Scholar 

  107. Ridha HM, Heidari AA, Wang M, Chen H (2020) Boosted mutation-based Harris Hawks optimizer for parameters identification of single-diode solar cell models. Energy Convers Manag 209(112):660. https://doi.org/10.1016/j.enconman.2020.112660

    Article  Google Scholar 

  108. Rodríguez-Esparza E, Zanella-Calzada LA, Oliva D, Heidari AA, Zaldivar D, Pérez-Cisneros M, Foong LK (2020) An efficient Harris Hawks-inspired image segmentation method. Expert Syst Appl 155(113):428. https://doi.org/10.1016/j.eswa.2020.113428

    Article  Google Scholar 

  109. Sayed GI, Khoriba G, Haggag MH (2018) A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl Intell 48(10):3462–3481

    Article  Google Scholar 

  110. Shen L, Chen H, Yu Z, Kang W, Zhang B, Li H, Yang B, Liu D (2016) Evolving support vector machines using fruit fly optimization for medical data classification. Knowl Based Syst 96:61–75

    Article  Google Scholar 

  111. Shi K, Tang Y, Liu X, Zhong S (2017) Non-fragile sampled-data robust synchronization of uncertain delayed chaotic lurie systems with randomly occurring controller gain fluctuation. ISA Trans 66:185–199

    Article  Google Scholar 

  112. Shi K, Tang Y, Zhong S, Yin C, Huang X, Wang W (2018) Nonfragile asynchronous control for uncertain chaotic Lurie network systems with Bernoulli stochastic process. Int J Robust Nonlinear Control 28(5):1693–1714

    Article  MathSciNet  MATH  Google Scholar 

  113. Shi K, Wang J, Tang Y, Zhong S (2020a) Reliable asynchronous sampled-data filtering of t-s fuzzy uncertain delayed neural networks with stochastic switched topologies. Fuzzy Sets Syst 381:1–25

    Article  MathSciNet  MATH  Google Scholar 

  114. Shi K, Wang J, Zhong S, Tang Y, Cheng J (2020b) Non-fragile memory filtering of ts fuzzy delayed neural networks based on switched fuzzy sampled-data control. Fuzzy Sets Syst 394:40–64

    Article  MathSciNet  MATH  Google Scholar 

  115. Singh N, Chiclana F, Magnot JP et al (2020) A new fusion of salp swarm with sine cosine for optimization of non-linear functions. Eng Comput 36(1):185–212

    Article  Google Scholar 

  116. Song S, Wang P, Heidari AA, Wang M, Zhao X, Chen H, He W, Xu S (2020) Dimension decided harris hawks optimization with gaussian mutation: Balance analysis and diversity patterns. Knowl Based Syst:106425

  117. Sun ZX, Hu R, Qian B, Liu B, Che GL (2018) Salp swarm algorithm based on blocks on critical path for reentrant job shop scheduling problems. In: International conference on intelligent computing, Springer, pp 638–648

  118. Sun G, Yang B, Yang Z, Xu G (2019) An adaptive differential evolution with combined strategy for global numerical optimization. Soft Comput:1–20

  119. Tang H, Xu Y, Lin A, Heidari AA, Wang M, Chen H, Luo Y, Li C (2020) Predicting green consumption behaviors of students using efficient firefly grey wolf-assisted k-nearest neighbor classifiers. IEEE Access 8:35,546–35,562. https://doi.org/10.1109/ACCESS.2020.29737630

    Article  Google Scholar 

  120. Thaher T, Heidari AA, Mafarja M, Dong JS, Mirjalili S (2020) Binary Harris Hawks optimizer for high-dimensional, low sample size feature selection. Springer, Berlin, pp 251–272

    Google Scholar 

  121. Tsai JF (2005) Global optimization of nonlinear fractional programming problems in engineering design. Eng Optim 37(4):399–409

    Article  MathSciNet  Google Scholar 

  122. Tsai Y-H, Wang J, Chien W-T, Wei C-Y, Wang X, Hsieh S-H (2019) A BIM-based approach for predicting corrosion under insulation. Autom Constr 107:102923

    Article  Google Scholar 

  123. Tu J, Chen H, Liu J, Heidari AA, Zhang X, Wang M, Ruby R, Pham QV (2020) Evolutionary biogeography-based whale optimization methods with communication structure: towards measuring the balance. Knowl Based Syst:106642

  124. Tubishat M, Idris N, Shuib L, Abushariah MA, Mirjalili S (2020) Improved salp swarm algorithm based on opposition based learning and novel local search algorithm for feature selection. Expert Syst Appl 145(113):122

    Google Scholar 

  125. Wang M, Chen H (2020) Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2019.1059461

    Article  Google Scholar 

  126. Wang H, Li H, Liu Y, Li C, Zeng S (2007) Opposition-based particle swarm algorithm with cauchy mutation. In: 2007 IEEE congress on evolutionary computation, IEEE, pp 4750–4756

  127. Wang SJ, Chen HL, Yan WJ, Chen YH, Fu X (2014) Face recognition and micro-expression recognition based on discriminant tensor subspace analysis plus extreme learning machine. Neural Process Lett 39(1):25–43

    Article  Google Scholar 

  128. Wang M, Chen H, Yang B, Zhao X, Hu L, Cai Z, Huang H, Tong C (2017) Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing 267:69–84

    Article  Google Scholar 

  129. Wang G, Yao Y, Chen Z, Hu P (2019) Thermodynamic and optical analyses of a hybrid solar cpv/t system with high solar concentrating uniformity based on spectral beam splitting technology. Energy 166:256–266

    Article  Google Scholar 

  130. Wang B, Zhang B, Liu X (2020a) An image encryption approach on the basis of a time delay chaotic system. Optik 225(165):737

    Google Scholar 

  131. Wang B, Zhang B, Liu X, Zou F (2020b) Novel infrared image enhancement optimization algorithm combined with dfocs. Optik 224(165):476

    Google Scholar 

  132. Wang M, Zhao X, Heidari AA, Chen H (2020c) Evaluation of constraint in photovoltaic models by exploiting an enhanced ant lion optimizer. Solar Energy 211:503–521

    Article  Google Scholar 

  133. Wang S, Zhang K, van Beek LP, Tian X, Bogaard TA (2020d) Physically-based landslide prediction over a large region: scaling low-resolution hydrological model results for high-resolution slope stability assessment. Environ Model Softw 124(104):607

    Google Scholar 

  134. Wang X, Chen H, Heidari AA, Zhang X, Xu J, Xu Y, Huang H (2020) Multi-population following behavior-driven fruit fly optimization: a Markov chain convergence proof and comprehensive analysis. Knowl Based Syst 210(106):437

    Google Scholar 

  135. Wei Y, Lv H, Chen M, Wang M, Heidari AA, Chen H, Li C (2020) Predicting entrepreneurial intention of students: an extreme learning machine with gaussian barebone Harris Hawks optimizer. IEEE Access 8:76,841–76,855. https://doi.org/10.1109/ACCESS.2020.2982796

    Article  Google Scholar 

  136. Wolpert DH, Macready WG, et al. (1995) No free lunch theorems for search. Technical Report SFI-TR-95-02-010, Santa Fe Institute, Tech rep

  137. Wu J, Nan R, Chen L (2019a) Improved salp swarm algorithm based on weight factor and adaptive mutation. J Exp Theor Artif Intell 31(3):493–515

    Article  Google Scholar 

  138. Wu T, Cao J, Xiong L, Zhang H (2019b) New stabilization results for semi-markov chaotic systems with fuzzy sampled-data control. Complexity

  139. Wu C, Wu P, Wang J, Jiang R, Chen M, Wang X (2020a) Critical review of data-driven decision-making in bridge operation and maintenance. Struct Infrastruct Eng:1–24

  140. Wu T, Xiong L, Cheng J, Xie X (2020) New results on stabilization analysis for fuzzy semi-Markov jump chaotic systems with state quantized sampled-data controller. Inf Sci 521:231–250

    Article  MathSciNet  MATH  Google Scholar 

  141. Xia J, Chen H, Li Q, Zhou M, Chen L, Cai Z, Fang Y, Zhou H (2017) Ultrasound-based differentiation of malignant and benign thyroid nodules: an extreme learning machine approach. Comput Methods Progr Biomed 147:37–49

    Article  Google Scholar 

  142. Xing Z, Jia H (2019) Multilevel color image segmentation based on glcm and improved salp swarm algorithm. IEEE Access 7:37,672–37,690

    Article  Google Scholar 

  143. Xiong Z, Xiao N, Xu F, Zhang X, Xu Q, Zhang K, Ye C (2020) An equivalent exchange based data forwarding incentive scheme for socially aware networks. J Signal Process Syst:1–15

  144. Xu X, Chen HL (2014) Adaptive computational chemotaxis based on field in bacterial foraging optimization. Soft Comput 18(4):797–807

    Article  Google Scholar 

  145. Xu Y, Chen H, Heidari AA, Luo J, Zhang Q, Zhao X, Li C (2019a) An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks. Expert Syst Appl 129:135–155. https://doi.org/10.1016/j.eswa.2019.03.0433

    Article  Google Scholar 

  146. Xu Y, Chen H, Luo J, Zhang Q, Jiao S, Zhang X (2019b) Enhanced moth-flame optimizer with mutation strategy for global optimization. Inf Sci 492:181–203. https://doi.org/10.1016/j.ins.2019.04.0224

    Article  MathSciNet  Google Scholar 

  147. Xu Y, Chen H, Luo J, Zhang Q, Jiao S, Zhang X (2019c) Enhanced moth-flame optimizer with mutation strategy for global optimization. Inf Sci 492:181–203

    Article  MathSciNet  Google Scholar 

  148. Xu B, Pang R, Zhou Y (2020) Verification of stochastic seismic analysis method and seismic performance evaluation based on multi-indices for high cfrds. Eng Geol 264(105):412

    Google Scholar 

  149. Yan J, Pu W, Zhou S, Liu H, Bao Z (2020a) Collaborative detection and power allocation framework for target tracking in multiple radar system. Inf Fus 55:173–183

    Article  Google Scholar 

  150. Yan J, Pu W, Zhou S, Liu H, Greco MS (2020b) Optimal resource allocation for asynchronous multiple targets tracking in heterogeneous radar networks. IEEE Trans Signal Process 68:4055–4068

    Article  MATH  Google Scholar 

  151. Yang XS (2010) Firefly algorithm, levy flights and global optimization. In: Research and development in intelligent systems XXVI, Springer, pp 209–218

  152. Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput

  153. Yang L, Chen H (2019) Fault diagnosis of gearbox based on rbf-pf and particle swarm optimization wavelet neural network. Neural Comput Appl 31(9):4463–4478

    Article  Google Scholar 

  154. Yang S, Deng B, Wang J, Li H, Lu M, Che Y, Wei X, Loparo KA (2019) Scalable digital neuromorphic architecture for large-scale biophysically meaningful neural network with multi-compartment neurons. IEEE Trans Neural Netw Learn Syst 31(1):148–162

    Article  Google Scholar 

  155. Yang Y, Chen H, Li S, Heidari AA, Wang M (2020) Orthogonal learning harmonizing mutation-based fruit fly-inspired optimizers. Appl Math Model 86:368–383. https://doi.org/10.1016/j.apm.2020.05.0195

    Article  MathSciNet  MATH  Google Scholar 

  156. Yu C, Heidari AA, Chen H (2020) A quantum-behaved simulated annealing algorithm-based moth-flame optimization method. Appl Math Model 87:1–19. https://doi.org/10.1016/j.apm.2020.04.0196

    Article  MathSciNet  MATH  Google Scholar 

  157. Yue H, Wang H, Chen H, Cai K, Jin Y (2020) Automatic detection of feather defects using lie group and fuzzy fisher criterion for shuttlecock production. Mech Syst Signal Process 141(106):690. https://doi.org/10.1016/j.ymssp.2020.1066907

    Article  Google Scholar 

  158. Zhang C, Ou J (2015) Modeling and dynamical performance of the electromagnetic mass driver system for structural vibration control. Eng Struct 82:93–103

    Article  Google Scholar 

  159. Zhang C, Ou J, Zhang J (2006) Parameter optimization and analysis of a vehicle suspension system controlled by magnetorheological fluid dampers. Struct Control Health Monit 13(5):885–896

    Article  Google Scholar 

  160. Zhang J, Wang Z, Luo X (2018) Parameter estimation for soil water retention curve using the salp swarm algorithm. Water 10(6):815

    Article  Google Scholar 

  161. Zhang X, Wang Y, Chen X, Su CY, Li Z, Wang C, Peng Y (2018) Decentralized adaptive neural approximated inverse control for a class of large-scale nonlinear hysteretic systems with time delays. IEEE Trans Syst Man Cybern Syst 49(12):2424–2437

    Article  Google Scholar 

  162. Zhang Q, Chen H, Heidari AA, Zhao X, Xu Y, Wang P, Li Y, Li C (2019) Chaos-induced and mutation-driven schemes boosting salp chains-inspired optimizers. Ieee Access 7:31243–31261

    Article  Google Scholar 

  163. Zhang H, Cai Z, Ye X, Wang M, Kuang F, Chen H, Li C, Li Y (2020a) A multi-strategy enhanced salp swarm algorithm for global optimization. Eng Comput. https://doi.org/10.1007/s00366-020-01099-48

    Article  Google Scholar 

  164. Zhang H, Heidari AA, Wang M, Zhang L, Chen H, Li C (2020b) Orthogonal nelder-mead moth flame method for parameters identification of photovoltaic modules. Energy Convers Manag. https://doi.org/10.1016/j.enconman.2020.1127649

    Article  Google Scholar 

  165. Zhang H, Li R, Cai Z, Gu Z, Heidari AA, Wang M, Chen H, Chen M (2020c) Advanced orthogonal moth flame optimization with broyden–fletcher–goldfarb–shanno algorithm: framework and real-world problems. Expert Syst Appl:113617

  166. Zhang H, Qiu Z, Cao J, Abdel-Aty M, Xiong L (2020d) Event-triggered synchronization for neutral-type semi-markovian neural networks with partial mode-dependent time-varying delays. IEEE Trans Neural Netw Learn Syst 31(11):4437–4450. https://doi.org/10.1109/TNNLS.2019.29552870

    Article  MathSciNet  Google Scholar 

  167. Zhang H, Wang Z, Chen W, Heidari AA, Wang M, Zhao X, Liang G, Chen H, Zhang X (2020e) Ensemble mutation-driven salp swarm algorithm with restart mechanism: framework and fundamental analysis. Expert Syst Appl 165(113):897

    Google Scholar 

  168. Zhang K, Ruben GB, Li X, Li Z, Yu Z, Xia J, Dong Z (2020f) A comprehensive assessment framework for quantifying climatic and anthropogenic contributions to streamflow changes: a case study in a typical semi-arid north china basin. Environ Model Softw:104704

  169. Zhang X, Xu Y, Yu C, Heidari AA, Li S, Chen H, Li C (2020g) Gaussian mutational chaotic fruit fly-built optimization and feature selection. Expert Syst Appl 141(112):976

    Google Scholar 

  170. Zhang Y, Liu R, Heidari AA, Wang X, Chen Y, Wang M, Chen H (2020h) Towards augmented kernel extreme learning models for bankruptcy prediction: algorithmic behavior and comprehensive analysis. Neurocomputing. https://doi.org/10.1016/j.neucom.2020.10.0381

    Article  Google Scholar 

  171. Zhang Y, Liu R, Wang X, Chen H, Li C (2020i) Boosted binary harris hawks optimizer and feature selection. Eng Comput. https://doi.org/10.1007/s00366-020-01028-52

    Article  Google Scholar 

  172. Zhang H, Wang Z, Chen W, Heidari AA, Wang M, Zhao X, Liang G, Chen H, Zhang X (2021) Ensemble mutation-driven salp swarm algorithm with restart mechanism: framework and fundamental analysis. Expert Syst Appl 165:113897. https://doi.org/10.1016/j.eswa.2020.1138973

    Article  Google Scholar 

  173. Zhao X, Li D, Yang B, Ma C, Zhu Y, Chen H (2014) Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton. Appl Soft Comput 24:585–596

    Article  Google Scholar 

  174. Zhao X, Li D, Yang B, Chen H, Yang X, Yu C, Liu S (2015) A two-stage feature selection method with its application. Comput Electr Eng 47:114–125

    Article  Google Scholar 

  175. Zhao X, Zhang X, Cai Z, Tian X, Wang X, Huang Y, Chen H, Hu L (2019) Chaos enhanced grey wolf optimization wrapped elm for diagnosis of paraquat-poisoned patients. Comput Biol Chem 78:481–490. https://doi.org/10.1016/j.compbiolchem.2018.11.0174

    Article  Google Scholar 

  176. Zhao D, Liu L, Yu F, Heidari AA, Wang M, Liang G, Muhammad K, Chen H (2020a) Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2d kapur entropy. Knowl Based Syst:106510

  177. Zhao D, Liu L, Yu F, Heidari AA, Wang M, Oliva D, Muhammad K, Chen H (2020b) Ant colony optimization with horizontal and vertical crossover search: Fundamental visions for multi-threshold image segmentation. Expert Syst Appl:114122

  178. Zhu B, Su B, Li Y (2018) Input-output and structural decomposition analysis of India’s carbon emissions and intensity, 2007/08-2013/14. Appl Energy 230:1545–1556

    Article  Google Scholar 

  179. Zhu J, Wang X, Chen M, Wu P, Kim MJ (2019) Integration of BIM and GIS: IFC geometry transformation to shapefile using enhanced open-source approach. Autom Constr 106:102859

    Article  Google Scholar 

  180. Zhu J, Wang X, Wang P, Wu Z, Kim MJ (2019) Integration of BIM and GIS: Geometry from IFC to shapefile using open-source technology. Autom Constr 102:105–119

    Article  Google Scholar 

  181. Zhu L, Kong L, Zhang C (2020) Numerical study on hysteretic behaviour of horizontal-connection and energy-dissipation structures developed for prefabricated shear walls. Appl Sci 10(4):1240

    Article  Google Scholar 

  182. Zhu G, Wang S, Sun L, Ge W, Zhang X (2020) Output Feedback Adaptive Dynamic Surface Sliding-Mode Control for Quadrotor UAVs with Tracking Error Constraints. Complexity 2020:1–23

    MATH  Google Scholar 

  183. Zhu J, Wu P, Chen M, Kim MJ, Wang X, Fang T (2020) Automatically Processing IFC Clipping Representation for BIM and GIS Integration at the Process Level. Appl Sci 10(6):2009

    Article  Google Scholar 

Download references

Author information

Author notes

  1. Guoxi Liang and Huiling Chen contributed equally to this work.

Authors and Affiliations

  1. Electronics and Communications Engineering, Graphic Era University, Dehradun, 248002, India

    Bhaskar Nautiyal & Rishi Prakash

  2. Computer Science and Engineering, Graphic Era Hill University, Dehradun, 248002, India

    Vrince Vimal

  3. Department of Information Technology, Wenzhou Polytechnic, Wenzhou, 325035, China

    Guoxi Liang

  4. Department of Computer Science, Wenzhou University, Wenzhou, 325035, China

    Huiling Chen

Authors
  1. Bhaskar Nautiyal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rishi Prakash
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vrince Vimal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guoxi Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huiling Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Guoxi Liang or Huiling Chen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nautiyal, B., Prakash, R., Vimal, V. et al. Improved Salp Swarm Algorithm with mutation schemes for solving global optimization and engineering problems. Engineering with Computers 38 (Suppl 5), 3927–3949 (2022). https://doi.org/10.1007/s00366-020-01252-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-01252-z

Keywords

Search

Navigation