Skip to main content
SpringerLink
Log in

Elite dominance scheme ingrained adaptive salp swarm algorithm: a comprehensive study

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

Abstract

This paper focuses on the performance of an improved algorithm based on the salp swarm algorithm (SSA), called AGSSA. We planned several new ideas to improve the defects of the original optimizer, such as ease to fall into local optimum and low convergence accuracy. To solve these problems, the SSA algorithm is improved in two parts. Salp swarm algorithm (SSA) is a recently proposed optimization algorithm with advantages and disadvantages, simulating a perception of the salp's foraging and navigation behavior in the deep ocean. The first improvement includes the adaptive control parameter introduced into the follower position update stage, which boosts the local exploitative ability of the population. The second improvement includes the elite gray wolf domination strategy introduced in the last stage of the population position update, which helps the population find the globally optimal solution faster. The performance of AGSSA is verified by a series of problems, including the IEEE CEC2014 benchmark functions, engineering design problems, and feature selection tasks. The experimental results of AGSSA are compared with some well-known metaheuristic algorithms. Simulations reveal that the performance of AGSSA is significantly better than lots of competitive metaheuristic algorithms. Moreover, in solving real-world problems, AGSSA also shows high accuracy in comparison with other metaheuristic algorithms. These points prove that the introduction of the two strategies has a positive effect on the original SSA. Promisingly, the proposed AGSSA can be used as a potential optimization tool in many optimization tasks.

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
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

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

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

  3. https://aliasgharheidari.com/RUN.html.

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

References

  1. Faris H et al (2019) An intelligent system for spam detection and identification of the most relevant features based on evolutionary random weight networks. Inf Fusion 48:67–83

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Tubishat M et al (2019) Improved salp swarm algorithm based on opposition based learning and novel local search algorithm for feature selection. Expert Syst Appl 145:113122

    Article  Google Scholar 

  4. Aljarah I, Mafarja M, Heidari AA, Faris H, Mirjalili S (2020) Multi-verse optimizer: theory, literature review, and application in data clustering. In: Mirjalili S, Song Dong J, Lewis A (eds) Nature-inspired optimizers. Studies in Computational Intelligence, vol 811. Springer, Cham. https://doi.org/10.1007/978-3-030-12127-3_8

  5. Song S, Wang P, Heidari AA, Wang M, Zhao X, Chen H, ... , Xu S (2021) Dimension decided Harris hawks optimization with Gaussian mutation: Balance analysis and diversity patterns. Knowledge-Based Syst 215:106425.

    Article  Google Scholar 

  6. Rezaee Jordehi A, Jasni J, Abdul Wahab NI, Abd Kadir MZA (2013) Particle swarm optimisation applications in FACTS optimisation problem. In: 2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO), pp 193–198. https://doi.org/10.1109/PEOCO.2013.6564541

  7. Heidari AA et al (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872

    Article  Google Scholar 

  8. Yang Y et al (2021) Hunger Games Search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst Appl 177:114864

    Article  Google Scholar 

  9. Ahmadianfar I et al (2021) RUN beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method. Expert Syst Appl 181:115079

    Article  Google Scholar 

  10. Li S et al (2020) Slime mould algorithm: a new method for stochastic optimization. Future Gener Comput Syst 111:300–323

    Article  Google Scholar 

  11. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  12. Zhao D et al (2020) Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2020.106510

    Article  Google Scholar 

  13. Zhao D et al (2020) Ant colony optimization with horizontal and vertical crossover search: fundamental visions for multi-threshold image segmentation. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.114122

    Article  Google Scholar 

  14. Zhang Y et al (2020) Boosted binary Harris hawks optimizer and feature selection. Eng Comput 1–30

    Article  Google Scholar 

  15. Hu J et al (2021) Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection. Knowl Based Syst 213:106684

    Article  Google Scholar 

  16. Zhang X et al (2020) Gaussian mutational chaotic fruit fly-built optimization and feature selection. Expert Syst Appl 141:112976

    Article  Google Scholar 

  17. Li Q et al (2017) An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis. Comput Math Methods Med. https://doi.org/10.1155/2017/9512741

  18. Liu T et al (2015) A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection. Int J Syst Sci 46(5):919–931

    Article  MATH  Google Scholar 

  19. Pang J et al (2018) A scatter simulated annealing algorithm for the bi-objective scheduling problem for the wet station of semiconductor manufacturing. Comput Ind Eng 123:54–66

    Article  Google Scholar 

  20. Zhou H et al (2018) A modified particle swarm optimization algorithm for a batch-processing machine scheduling problem with arbitrary release times and non-identical job sizes. Comput Ind Eng 123:67–81

    Article  Google Scholar 

  21. Zeng G-Q, Lu Y-Z, Mao W-J (2011) Modified extremal optimization for the hard maximum satisfiability problem. J Zhejiang Univ Sci C 12(7):589–596

    Article  Google Scholar 

  22. Zeng G et al (2012) Backbone guided extremal optimization for the hard maximum satisfiability problem. Int J Innov Comput Inf Control 8(12):8355–8366

    Google Scholar 

  23. Hu L et al (2017) A new machine-learning method to prognosticate paraquat poisoned patients by combining coagulation, liver, and kidney indices. PLoS ONE 12(10):e0186427

    Article  Google Scholar 

  24. Li C et al (2018) Developing a new intelligent system for the diagnosis of tuberculous pleural effusion. Comput Methods Programs Biomed 153:211–225

    Article  Google Scholar 

  25. Zhao X et al (2019) Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients. Comput Biol Chem 78:481–490

    Article  Google Scholar 

  26. Huang H et al (2019) A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features. BMC Bioinform 20(8):1–14

    Google Scholar 

  27. Zhang Y et al (2020) Towards augmented kernel extreme learning models for bankruptcy prediction: algorithmic behavior and comprehensive analysis. Neurocomputing. https://doi.org/10.1016/j.neucom.2020.10.038

    Article  Google Scholar 

  28. Yu C et al (2021) SGOA: annealing-behaved grasshopper optimizer for global tasks. Eng Comput. https://doi.org/10.1007/s00366-020-01234-1

    Article  Google Scholar 

  29. Cai Z et al (2019) Evolving an optimal kernel extreme learning machine by using an enhanced grey wolf optimization strategy. Expert Syst Appl 138:112814

    Article  Google Scholar 

  30. Xu Y et al (2019) An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks. Expert Syst Appl 129:135–155

    Article  Google Scholar 

  31. Luo J 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 

  32. Wang M et al (2017) Grey wolf optimization evolving kernel extreme learning machine: Application to bankruptcy prediction. Eng Appl Artif Intell 63:54–68

    Article  Google Scholar 

  33. Zeng G-Q et al (2014) Binary-coded extremal optimization for the design of PID controllers. Neurocomputing 138:180–188

    Article  Google Scholar 

  34. Zeng G-Q et al (2015) Design of fractional order PID controller for automatic regulator voltage system based on multi-objective extremal optimization. Neurocomputing 160:173–184

    Article  Google Scholar 

  35. Zeng G-Q et al (2019) Adaptive population extremal optimization-based PID neural network for multivariable nonlinear control systems. Swarm Evol Comput 44:320–334

    Article  Google Scholar 

  36. Wei Y et al (2020) Predicting entrepreneurial intention of students: an extreme learning machine with Gaussian barebone Harris hawks optimizer. IEEE Access 8:76841–76855

    Article  Google Scholar 

  37. Zhu W et al (2020) Evaluation of sino foreign cooperative education project using orthogonal sine cosine optimized kernel extreme learning machine. IEEE Access 8:61107–61123

    Article  Google Scholar 

  38. Lin A et al (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

    Article  Google Scholar 

  39. Tu J et al (2019) Predict the entrepreneurial intention of fresh graduate students based on an adaptive support vector machine framework. Math Probl Eng 2019:1–16

    Google Scholar 

  40. Wei Y et al (2017) An improved grey wolf optimization strategy enhanced SVM and its application in predicting the second major. Math Probl Eng 2017:1–12

    Article  Google Scholar 

  41. Zhao X et al (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 

  42. Zhao X et al (2015) A two-stage feature selection method with its application. Comput Electr Eng 47:114–125

    Article  Google Scholar 

  43. Mirjalili S et al (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  44. Pavan Kumar Neeli VSR, Salma U (2020) Automatic generation control for autonomous hybrid power system using single and multi-objective salp swarm algorithm. In: Advances in intelligent systems and computing, pp 624–636

  45. Yang B et al (2019) Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition. J Clean Prod 215:1203–1222

    Article  Google Scholar 

  46. Sambaiah KS, Jayabarathi T (2019) Optimal allocation of renewable distributed generation and capacitor banks in distribution systems using salp swarm algorithm. Int J Renew Energy Res 9(1):96–107

    Google Scholar 

  47. 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 

  48. Hussien AG, Hassanien AE, Houssein EH (2017) Swarming behaviour of salps algorithm for predicting chemical compound activities. In: 2017 IEEE 8th international conference on intelligent computing and information systems, ICICIS 2017

    Article  Google Scholar 

  49. Tubishat M et al (2021) Dynamic salp swarm algorithm for feature selection. Expert Syst Appl 164:113873

    Article  Google Scholar 

  50. Zhang Q et al (2019) Chaos-induced and mutation-driven schemes boosting salp chains-inspired optimizers. IEEE Access 7:31243–31261

    Article  Google Scholar 

  51. Shekhawat SS et al (2021) bSSA: binary salp swarm algorithm with hybrid data transformation for feature selection. IEEE Access 9:14867–14882

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  53. Hussien AG (2021) An enhanced opposition-based salp swarm algorithm for global optimization and engineering problems. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-02892-9

  54. Thawkar S (2021) A hybrid model using teaching-learning-based optimization and Salp swarm algorithm for feature selection and classification in digital mammography. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02662-z

  55. Rachapudi V, Lavanya-Devi G (2019) Feature selection for histopathological image classification using levy flight salp swarm optimizer. Recent Pat Comput Sci 12(4):329–337

    Article  Google Scholar 

  56. Nautiyal B et al (2021) Improved salp swarm algorithm with mutation schemes for solving global optimization and engineering problems. Eng Comput. https://doi.org/10.1007/s00366-020-01252-z

  57. Yildiz AR, Erdas MU (2021) A new hybrid Taguchi-salp swarm optimization algorithm for the robust design of real-world engineering problems. Mater Test 63(2):157–162

    Article  Google Scholar 

  58. Aljarah I et al (2020) A dynamic locality multi-objective salp swarm algorithm for feature selection. Comput Ind Eng 147:106628

    Article  Google Scholar 

  59. Qais MH, Hasanien HM, Alghuwainem S (2019) Enhanced salp swarm algorithm: Application to variable speed wind generators. Eng Appl Artif Intell 80:82–96

    Article  Google Scholar 

  60. Faris H et al (2020) Time-varying hierarchical chains of salps with random weight networks for feature selection. Expert Syst Appl 140:112898

    Article  Google Scholar 

  61. Ibrahim RA et al (2019) Improved salp swarm algorithm based on particle swarm optimization for feature selection. J Ambient Intell Humaniz Comput 10(8):3155–3169

    Article  Google Scholar 

  62. Hegazy AE, Makhlouf MA, El-Tawel GS (2019) Feature selection using chaotic salp swarm algorithm for data classification. Arab J Sci Eng 44(4):3801–3816

    Article  Google Scholar 

  63. Liu Y et al (2021) Chaos-assisted multi-population salp swarm algorithms: Framework and case studies. Expert Syst Appl 168:114369

    Article  Google Scholar 

  64. Zhang H et al (2021) Ensemble mutation-driven salp swarm algorithm with restart mechanism: framework and fundamental analysis. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113897

    Article  Google Scholar 

  65. Al-Zoubi AM et al (2020) Salp chain-based optimization of support vector machines and feature weighting for medical diagnostic information systems. In: Mirjalili S, Faris H, Aljarah I (eds) Evolutionary machine learning techniques: algorithms and applications. Springer Singapore, Singapore, pp 11–34

    Chapter  Google Scholar 

  66. Abbassi R et al (2019) An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Convers Manage 179:362–372

    Article  Google Scholar 

  67. Faris H et al (2020) Salp swarm algorithm: theory, literature review, and application in extreme learning machines. Nature-inspired optimizers. Springer, Berlin, pp 185–199

    Google Scholar 

  68. Chen L et al (2014) An evolutionary algorithm based on covariance matrix leaning and searching preference for solving CEC 2014 benchmark problems. In: Proceedings of the 2014 IEEE congress on evolutionary computation, CEC 2014

    Article  Google Scholar 

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

    Article  Google Scholar 

  70. Liang Z et al (2017) An enhanced artificial bee colony algorithm with adaptive differential operators. Appl Soft Comput 58:480–494

    Article  Google Scholar 

  71. Lin Q et al (2016) Adaptive composite operator selection and parameter control for multiobjective evolutionary algorithm. Inf Sci 339:332–352

    Article  Google Scholar 

  72. Wulandhari LA, Wibowo A, Desa MI (2015) Condition diagnosis of multiple bearings using adaptive operator probabilities in genetic algorithms and back propagation neural networks. Neural Comput Appl 26(1):57–65

    Article  Google Scholar 

  73. Lv Z et al (2021) Fine-grained visual computing based on deep learning. ACM Trans Multimed Comput Commun Appl 17(1s):1–19

    Article  Google Scholar 

  74. Lv Z, Singh AK, Li J (2021) Deep learning for security problems in 5G heterogeneous networks. IEEE Netw 35(2):67–73

    Article  Google Scholar 

  75. Lv Z et al (2020) Deep learning enabled security issues in the internet of things. IEEE Internet Things J 8(12):9531–9538

    Article  Google Scholar 

  76. Hua L et al (2021) Novel finite-time reliable control design for memristor-based inertial neural networks with mixed time-varying delays. IEEE Trans Circ Syst I Regul Pap 68(4):1599–1609

    Article  MathSciNet  Google Scholar 

  77. Lv Z et al (2020) Industrial security solution for virtual reality. IEEE Internet Things J 8(8):6273–6281

    Article  Google Scholar 

  78. Alcalá-Fdez J et al (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318

    Article  Google Scholar 

  79. Heidari AA, Abbaspour RA, Chen H (2019) Efficient boosted grey wolf optimizers for global search and kernel extreme learning machine training. Appl Soft Comput 81:105521

    Article  Google Scholar 

  80. Elhosseini MA et al (2019) Biped robot stability based on an A-C parametric whale optimization algorithm. J Comput Sci 31:17–32

    Article  MathSciNet  Google Scholar 

  81. Heidari AA et al (2019) An enhanced associative learning-based exploratory whale optimizer for global optimization. Neural Comput Appl 32(9):5185–5211

  82. Nenavath H, Jatoth RK (2018) Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking. Appl Soft Comput J 62:1019–1043

    Article  Google Scholar 

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

    Article  Google Scholar 

  84. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimization. Int J Bio-Inspired Comput 2(2):78–84

    Article  Google Scholar 

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

    Article  Google Scholar 

  86. Mirjalili S et al (2020) Whale optimization algorithm: theory, literature review, and application in designing photonic crystal filters. In: Studies in computational intelligence, pp 219–238

    Google Scholar 

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

    Article  Google Scholar 

  88. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: Proceedings—international conference on computational intelligence for modelling, control and automation, CIMCA 2005 and international conference on intelligent agents, web technologies and internet

    Article  Google Scholar 

  89. Yin F et al (2021) Multifidelity genetic transfer: an efficient framework for production optimization. SPE J 1–22

    Article  Google Scholar 

  90. Jiang Q et al (2017) Optimizing multistage discriminative dictionaries for blind image quality assessment. IEEE Trans Multimed 20(8):2035–2048

    Article  Google Scholar 

  91. Lv Z et al (2021) Analysis of using blockchain to protect the privacy of drone big data. IEEE Netw 35(1):44–49

    Article  Google Scholar 

  92. Shen H et al (2021) A cloud-aided privacy-preserving multi-dimensional data comparison protocol. Inf Sci 545:739–752

    Article  MathSciNet  Google Scholar 

  93. Zhang X et al (2018) Adaptive estimated inverse output-feedback quantized control for piezoelectric positioning stage. IEEE Trans Cybern 49(6):2106–2118

    Article  Google Scholar 

  94. Cai X et al (2021) Dissipative sampled-data control for high-speed train systems with quantized measurements. IEEE Trans Intell Transport Syst. https://doi.org/10.1109/TITS.2021.3052940

  95. Cai X et al (2021) Dissipative analysis for high speed train systems via looped-functional and relaxed condition methods. Appl Math Model 96:570–583

    Article  MathSciNet  MATH  Google Scholar 

  96. Cai X et al (2020) Robust H∞ control for uncertain delayed T-S fuzzy systems with stochastic packet dropouts. Appl Math Comput 385:125432

    MathSciNet  MATH  Google Scholar 

  97. Cai X et al (2021) Fuzzy quantized sampled-data control for extended dissipative analysis of T-S fuzzy system and its application to WPGSs. J Franklin Inst 358(2):1350–1375

    Article  MathSciNet  MATH  Google Scholar 

  98. Qu S et al (2021) Design and implementation of a fast sliding-mode speed controller with disturbance compensation for SPMSM syste. IEEE Trans Transport Electrif. https://doi.org/10.1109/TTE.2021.3060102

  99. Hu J et al (2020) Formation control and collision avoidance for multi-UAV systems based on Voronoi partition. Sci China Technol Sci 63(1):65–72

    Article  Google Scholar 

  100. Hu J et al (2020) Convergent multiagent formation control with collision avoidance. IEEE Trans Rob 36(6):1805–1818

    Article  Google Scholar 

  101. Lv Z, Qiao L, You I (2020) 6G-enabled network in box for internet of connected vehicles. IEEE Trans Intell Transport Syst. https://doi.org/10.1109/TITS.2020.3034817

  102. Lv Z, Qiao L, Song H (2020) Analysis of the security of internet of multimedia things. ACM Trans Multimed Comput Commun Appl (TOMM) 16(3s):1–16

    Article  Google Scholar 

  103. Sheng H et al (2021) Near-online tracking with co-occurrence constraints in blockchain-based edge computing. IEEE Internet Things J 8(4):2193–2207

    Article  Google Scholar 

  104. Zhao J et al (2020) Efficient deployment with geometric analysis for mmwave UAV communications. IEEE Wirel Commun Lett 9(7):1115–1119

    Google Scholar 

  105. Hu J et al (2020) Object traversing by monocular UAV in outdoor environment. Asian J Control. https://doi.org/10.1002/asjc.2415

  106. Liu Y et al (2020) Development of 340-GHz transceiver front end based on GaAs monolithic integration technology for THz active imaging array. Appl Sci 10(21):7924

    Article  Google Scholar 

  107. Li B-H et al (2020) A survey on blocking technology of entity resolution. J Comput Sci Technol 35(4):769–793

    Article  Google Scholar 

  108. Zhang B et al (2020) Four-hundred gigahertz broadband multi-branch waveguide coupler. IET Microwaves Antennas Propag 14:1175–1179

    Article  Google Scholar 

  109. Niu Z et al (2020) The research on 220GHz multicarrier high-speed communication system. China Commun 17(3):131–139

    Article  Google Scholar 

  110. Zhang B et al (2019) A novel 220-GHz GaN diode on-chip tripler with high driven power. IEEE Electron Device Lett 40(5):780–783

    Article  Google Scholar 

  111. Coello Coello CA (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 

  112. Yildiz AR (2019) A novel hybrid whale-Nelder-Mead algorithm for optimization of design and manufacturing problems. Int J Adv Manuf Technol 105(12):5091–5104

    Article  Google Scholar 

  113. Cheng M-Y, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112

    Article  Google Scholar 

  114. Chlckermane H, Gea HC (1996) Structural optimization using a new local approximation method. Int J Numer Methods Eng 39(5):829–846

    Article  MathSciNet  MATH  Google Scholar 

  115. Xu Y et al (2019) Enhanced Moth-flame optimizer with mutation strategy for global optimization. Inf Sci 492:181–203

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  117. Deng W, Xu J, Zhao H (2019) An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2897580

    Article  Google Scholar 

  118. Arora JS (2004) Introduction to optimum design. 1–728

    Google Scholar 

  119. Arora J (2012) Introduction to optimum design

    Chapter  Google Scholar 

  120. Belegundu AD, Arora JS (1985) A study of mathematical programming methods for structural optimization. Part I: Theory. Int J Numer Methods Eng 21(9):1583–1599

    Article  MATH  Google Scholar 

  121. He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  123. Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112–113:283–294

    Article  Google Scholar 

  124. Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579

    MathSciNet  MATH  Google Scholar 

  125. Eskandar H et al (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110–111:151–166

    Article  Google Scholar 

  126. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Studies in computational intelligence, pp 65–74

    Chapter  Google Scholar 

  127. Liu C et al (2020) Crossing thyristor branches based hybrid modular multilevel converters for DC line faults. IEEE Trans Ind Electron 68(10):9719–9730

  128. Zhao D et al (2020) Ant colony optimization with horizontal and vertical crossover search: fundamental visions for multi-threshold image segmentation. Expert Syst Appl 114–122

    Article  Google Scholar 

  129. Mafarja M et al (2020) Augmented whale feature selection for IoT attacks: structure, analysis and applications. Future Gener Comput Syst 112:18–40

    Article  Google Scholar 

  130. Chantar H et al (2020) Feature selection using binary grey wolf optimizer with elite-based crossover for Arabic text classification. Neural Comput Appl 32(16):12201–12220

    Article  Google Scholar 

  131. Mafarja M et al (2018) Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowl Based Syst 161:185–204

    Article  Google Scholar 

  132. Mafarja M et al (2020) Efficient hybrid nature-inspired binary optimizers for feature selection. Cogn Comput 12(1):150–175

    Article  Google Scholar 

  133. Thaher T et al (2020) Binary Harris Hawks optimizer for high-dimensional, low sample size feature selection. Evolutionary machine learning techniques. Springer, Berlin, pp 251–272

    Chapter  Google Scholar 

  134. Ala’M A-Z et al (2021) Evolutionary competitive swarm exploring optimal support vector machines and feature weighting. Soft Comput 25(4):3335–3352

    Article  Google Scholar 

  135. Namous F et al (2020) Evolutionary and swarm-based feature selection for imbalanced data classification. Evolutionary machine learning techniques. Springer, Singapore, pp 231–250

    Chapter  Google Scholar 

  136. Taradeh M et al (2019) An evolutionary gravitational search-based feature selection. Inf Sci 497:219–239

    Article  Google Scholar 

  137. Mafarja M et al (2020) Dragonfly algorithm: theory, literature review, and application in feature selection. Nature-inspired optimizers. Springer, Cham, pp 47–67

    Google Scholar 

  138. Aljarah I et al (2018) Asynchronous accelerating multi-leader salp chains for feature selection. Appl Soft Comput 71:964–979

    Article  Google Scholar 

  139. Mafarja M et al (2018) Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowl Based Syst 145:25–45

    Article  Google Scholar 

  140. Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381

    Article  Google Scholar 

  141. Emary E, Zawba HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381

    Article  Google Scholar 

  142. Mirjalili S, Lewis A (2013) S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evol Comput 9:1–14

    Article  Google Scholar 

  143. Hussien AG, Houssein EH, Hassanien AE (2017) A binary whale optimization algorithm with hyperbolic tangent fitness function for feature selection. In: 2017 IEEE 8th international conference on intelligent computing and information systems, ICICIS 2017. https://doi.org/10.1109/INTELCIS.2017.8260031

  144. Yang R et al (2018) Enhancing quality for HEVC compressed videos. IEEE Trans Circ Syst Video Technol 29(7):2039–2054

    Article  Google Scholar 

  145. Xu M et al (2018) Assessing visual quality of omnidirectional videos. IEEE Trans Circ Syst Video Technol 29(12):3516–3530

    Article  Google Scholar 

  146. Dong S et al (2021) New study on fixed-time synchronization control of delayed inertial memristive neural networks. Appl Math Comput 399:126035

    MathSciNet  MATH  Google Scholar 

  147. Lv Z et al (2021) Big data analytics for 6G-enabled massive internet of things. IEEE Internet Things J 8(7):5350–5359

    Article  Google Scholar 

  148. Xiao N et al (2021) A diversity-based selfish node detection algorithm for socially aware networking. J Signal Process Syst 93(7):811–825

    Article  Google Scholar 

  149. Hu Z et al (2021) Uncertainty modeling for multi center autism spectrum disorder classification using Takagi-Sugeno-Kang fuzzy systems. IEEE Trans Cognit Dev Syst. https://doi.org/10.1109/TCDS.2021.3073368

  150. Chen C et al (2020) Diagnosis of Alzheimer's disease based on deeply-fused nets. Comb Chem High Throughput Screen

  151. Fei X et al (2020) Projective parameter transfer based sparse multiple empirical kernel learning Machine for diagnosis of brain disease. Neurocomputing 413:271–283

    Article  Google Scholar 

  152. Saber A et al (2021) A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access 9:71194–71209

    Article  Google Scholar 

  153. Zhang L et al (2021) Resource allocation and trust computing for blockchain-enabled edge computing system. Comput Secur 102249

    Article  Google Scholar 

  154. Zhang L et al (2020) A covert communication method using special bitcoin addresses generated by Vanitygen. Comput Mater Continua 65(1):597–616

    Article  Google Scholar 

  155. Zhang L et al (2021) Research on a covert communication model realized by using smart contracts in blockchain environment. IEEE Syst J. https://doi.org/10.1109/JSYST.2021.3057333

    Article  Google Scholar 

  156. Xue X et al (2019) Social learning evolution (SLE): computational experiment-based modeling framework of social manufacturing. IEEE Trans Ind Inform 15(6):3343–3355

    Article  Google Scholar 

  157. Xue X et al (2020) Value entropy: a systematic evaluation model of service ecosystem evolution. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2020.3016660

    Article  Google Scholar 

  158. Li J et al (2017) Towards context-aware social recommendation via individual trust. Knowl Based Syst 127:58–66

    Article  Google Scholar 

  159. Li J, Lin J (2020) A probability distribution detection based hybrid ensemble QoS prediction approach. Inf Sci 519:289–305

    Article  MathSciNet  Google Scholar 

  160. Li J et al (2014) An efficient and reliable approach for quality-of-service-aware service composition. Inf Sci 269:238–254

    Article  Google Scholar 

  161. Wu X et al (2020) Supervised feature selection with orthogonal regression and feature weighting. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.2991336

    Article  Google Scholar 

  162. Wang S-J et al (2021) MESNet: a convolutional neural network for spotting multi-scale micro-expression intervals in long videos. IEEE Trans Image Process 30:3956–3969

  163. Li J, Soladie C, Seguier R (2020) Local temporal pattern and data augmentation for micro-expression spotting. IEEE Trans Affect Comput. https://doi.org/10.1109/TAFFC.2020.3023821

    Article  Google Scholar 

  164. Zhao H et al (2018) Parallel and efficient approximate nearest patch matching for image editing applications. Neurocomputing 305:39–50

    Article  Google Scholar 

  165. Zhao Y et al (2014) Parallel style-aware image cloning for artworks. IEEE Trans Visual Comput Graph 21(2):229–240

    Article  Google Scholar 

  166. Yang Y et al (2017) Semantic portrait color transfer with internet images. Multimed Tools Appl 76(1):523–541

    Article  Google Scholar 

  167. Liang X et al (2020) Chaotic oppositional sine–cosine method for solving global optimization problems. Eng Comput. https://doi.org/10.1007/s00366-020-01083-y

    Article  Google Scholar 

  168. Ba AF et al (2020) Levy-based antlion-inspired optimizers with orthogonal learning scheme. Eng Comput. https://doi.org/10.1007/s00366-020-01042-7

    Article  Google Scholar 

  169. Jin L, Wen Z, Hu Z (2020) Topology-preserving nonlinear shape registration on the shape manifold. Multimed Tools Appl 1–13

  170. Liu X et al (2021) A scalable redefined stochastic blockmodel. ACM Trans Knowl Discov Data (TKDD) 15(3):1–28

    Google Scholar 

  171. Cao X et al (2021) Risk-averse storage planning for improving RES hosting capacity under uncertain siting choice. IEEE Trans Sustain Energy. https://doi.org/10.1109/TSTE.2021.3075615

    Article  Google Scholar 

  172. Yang C et al (2020) Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning. Nat Commun 11(1):6358

    Article  Google Scholar 

  173. Chen H et al (2018) Next generation technology for epidemic prevention and control: data-driven contact tracking. IEEE Access 7:2633–2642

    Article  Google Scholar 

  174. Chen H et al (2019) Mining spatiotemporal diffusion network: a new framework of active surveillance planning. IEEE Access 7:108458–108473

    Article  Google Scholar 

  175. Fan M et al (2021) adaptive data structure regularized multiclass discriminative feature selection. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/tnnls.2021.3071603

  176. Zhang X et al (2020) Top-k feature selection framework using robust 0–1 integer programming. IEEE Trans Neural Netw Learn Syst

    Article  MathSciNet  Google Scholar 

  177. Zhang X et al (2015) Robust hand tracking via novel multi-cue integration. Neurocomputing 157:296–305

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by the Zhejiang Provincial Natural Science Foundation of China (LJ19F020001), the Science and Technology Plan Project of Wenzhou, China (2018ZG012), and Guangdong Natural Science Foundation (2018A030313339), Scientific Research Team Project of Shenzhen Institute of Information Technology (SZIIT2019KJ022). Taif University Researchers Supporting Project Number (TURSP-2020/125), Taif University, Taif, Saudi Arabia. We acknowledge the ideas of Ali Asghar Heidari (https://aliasgharheidari.com) during the first submission of this work.

Author information

Authors and Affiliations

  1. College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, 325035, China

    Songwei Zhao & Pengjun Wang

  2. School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen, 518172, China

    Xuehua Zhao

  3. Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia

    Hamza Turabieh

  4. Department of Computer Science, Birzeit University, P.O. Box 14, Birzeit, West Bank, Palestine

    Majdi Mafarja

  5. College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, Zhejiang, China

    Huiling Chen

Authors
  1. Songwei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pengjun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuehua Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hamza Turabieh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Majdi Mafarja
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Huiling Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Pengjun Wang 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

Zhao, S., Wang, P., Zhao, X. et al. Elite dominance scheme ingrained adaptive salp swarm algorithm: a comprehensive study. Engineering with Computers 38 (Suppl 5), 4501–4528 (2022). https://doi.org/10.1007/s00366-021-01464-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-021-01464-x

Keywords

Search

Navigation