Skip to main content

Advertisement

Springer Nature Link
Log in

Boosting salp swarm algorithm by opposition-based learning concept and sine cosine algorithm for engineering design problems

  • Optimization
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

A unique hybrid meta-heuristic combining the salp swarm algorithm and the sine cosine algorithm (SSCA) is established in this study to improve convergence speed while outperforming existing conventional algorithms. The sine cosine position equations are utilized to update the position of the salp leader in search space while a weighting factor updates the position of the salp follower so that the best and possible optimal solutions are obtained using the sine or cosine and weighting function. Particle swarm optimization PSO inspires this weighting factor. Each salp uses the information-sharing approach of sine and cosine functions during this process to strengthen their exploration and exploitation abilities. The goal of incorporating modifications to the salp swarm optimizer algorithm is to help the standard approach avoid premature convergence that leads the search to the most likely search space. The proposed algorithm is tested on classical optimization benchmark functions and eight real engineering applications. The goal is to investigate and validate the SSCA's proper behaviour while finding the optimum solutions. The results of the comparison demonstrated that the SSCA method achieves the best accuracies.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

Data is available on request from the authors.

References

  • Abdollahzadeh B, Gharehchopogh FS, Mirjalili S (2021) African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems. Comput Ind Eng 158:107408

    Google Scholar 

  • Abualigah L (2021) Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications. Neural Comput Appl 33:2949–2972

    Google Scholar 

  • Abualigah L, Diabat A (2021) Advances in sine cosine algorithm: a comprehensive survey. Artif Intell Rev 54:2567–2608

    Google Scholar 

  • Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609

    MathSciNet  MATH  Google Scholar 

  • Abualigah L, Al-Okbi NK, Elaziz MA, Houssein EH (2022) Boosting marine predators algorithm by salp swarm algorithm for multilevel thresholding image segmentation. Multimed Tools Appl 81:16707–16742

    Google Scholar 

  • Agushaka JO, Ezugwu AE, Abualigah L (2022) Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer. Neural Comput Appl 6:2

    Google Scholar 

  • Arora JS (2004) Introduction to optimum design. Elsevier, Amsterdam

    Google Scholar 

  • Asghar A, Mirjalili S, Faris H, Aljarah I (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872

    Google Scholar 

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

    MATH  Google Scholar 

  • Chamchuen S, Siritaratiwat A, Fuangfoo P, Suthisopapan P, Khunkitti P (2021) Adaptive salp swarm algorithm as optimal feature selection for power quality disturbance classification. Appl Sci 11:2

    Google Scholar 

  • Chauhan S, Vashishtha G (2023) A synergy of an evolutionary algorithm with slime mould algorithm through series and parallel construction for improving global optimization and conventional design problem. Eng Appl Artif Intell 118:105650

    Google Scholar 

  • Chauhan S, Singh M, Aggarwal AK (2020) Diversity driven multi-parent evolutionary algorithm with adaptive non-uniform mutation. J Exp Theor Artif Intell 2:1–32. https://doi.org/10.1080/0952813X.2020.1785020

    Article  Google Scholar 

  • Chauhan S, Singh M, Aggarwal AK (2021a) Cluster head selection in heterogeneous wireless sensor network using a new evolutionary algorithm. Wirel Pers Commun 119:585–616

    Google Scholar 

  • Chauhan S, Singh M, Aggarwal AK (2021b) Bearing defect identification via evolutionary algorithm with adaptive wavelet mutation strategy. Measurement 179:109445

    Google Scholar 

  • Chauhan S, Vashishtha G, Kumar A, Abualigah L (2022a) Conglomeration of reptile search algorithm and differential evolution algorithm for optimal designing of FIR filter. Circ Syst Signal Process 2:2. https://doi.org/10.1007/s00034-022-02255-5

    Article  Google Scholar 

  • Chauhan S, Vashishtha G, Kumar A (2022b) Approximating parameters of photovoltaic models using an amended reptile search algorithm. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-022-04412-9

    Article  Google Scholar 

  • Chauhan S, Vashishtha G (2021) Mutation-based arithmetic optimization algorithm for global optimization. In: 2021 International Conference on Intelligent Technologies (CONIT) Karnataka, India, 1–6 (IEEE, 2021)

  • Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  • Coelho LD (2010) Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst Appl 37:1676–1683

    Google Scholar 

  • Coello Coello CA, Montes EM (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inf 16:193–203

    Google Scholar 

  • Deb K (1990) Optimal design of a class of welded structures via genetic algorithms. Collect Tech Pap AIAA/ASME/ASCE/AHS/ASC Struct Struct Dyn Mater Conf 444–453. doi:https://doi.org/10.2514/6.1990-1179

  • Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. Proc Sixth Int Symp Micro Mach Hum Sci IEEE. 39–43. doi: https://doi.org/10.1109/mhs.1995.494215

  • Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110–111:151–166

    Google Scholar 

  • Faris H, Habib M, Almomani I, Eshtay M, Aljarah I (2020) Optimizing extreme learning machines using chains of salps for efficient android ransomware detection. Appl Sci 10:2

    Google Scholar 

  • Gandomi AH, Yang XS, Alavi AH, Talatahari S (2013a) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22:1239–1255

    Google Scholar 

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

    Google Scholar 

  • Gong W, Cai Z, Liang D (2014) Engineering optimization by means of an improved constrained differential evolution. Comput Methods Appl Mech Eng 268:884–904

    MathSciNet  MATH  Google Scholar 

  • Gupta S, Tiwari R, Nair SB (2007) Multi-objective design optimisation of rolling bearings using genetic algorithms. Mech Mach Theory 42:1418–1443

    MATH  Google Scholar 

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

    Google Scholar 

  • He Q, Wang L (2007b) A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Appl Math Comput 186:1407–1422

    MathSciNet  MATH  Google Scholar 

  • Huang F, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186:340–356

    MathSciNet  MATH  Google Scholar 

  • Kassaymeh S et al (2022) Self-adaptive salp swarm algorithm for optimization problems. Soft Comput 26:9349–9368

    Google Scholar 

  • Kaveh A, Bakhshpoori T (2016) Water evaporation optimization: a novel physically inspired optimization algorithm. Comput Struct 167:69–85

    Google Scholar 

  • Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84

    Google Scholar 

  • Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system. Acta Mech 289:267–289

    MATH  Google Scholar 

  • Kropat E, Meyer-Nieberg S, Weber GW (2019) Computational networks and systems—homogenization of variational problems on micro-architectured networks and devices. Optim Methods Softw 34:586–611

    MathSciNet  MATH  Google Scholar 

  • Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst 111:300–323

    Google Scholar 

  • Lin C, Wang P, Heidari AA, Zhao X, Chen H (2023) A boosted communicational salp swarm algorithm: performance optimization and comprehensive analysis. J Bionic Eng 20:1296–1332

    Google Scholar 

  • Ling SH, Lu HHC, Yeung CW (2008) Hybrid particle swarm optimization with wavelet mutation and its industrial applications. IEEE Trans Syst Man Cybern Part B 38:743–763

    Google Scholar 

  • Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. J Central South Univ 10:629–640

    Google Scholar 

  • Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312

    Google Scholar 

  • Meraihi Y, Gabis AB, Ramdane-Cherif A, Acheli D (2021) A comprehensive survey of crow search algorithm and its applications. Artif Intell Rev 54:2669–2716

    Google Scholar 

  • Mezura-Montes E, Coello Coello CA (2005) A simple evolution strategy to solve constrained optimization problems. IEEE Trans Evol Comput 9:1–17

    MATH  Google Scholar 

  • Mezura-Montes E, Coello CAC, Velázquez-Reyes J, Muñoz-Dávila L (2007) Multiple trial vectors in differential evolution for engineering design. Eng Optim 39:567–589

    MathSciNet  Google Scholar 

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

    Google Scholar 

  • Mirjalili S (2015b) The ant lion optimizer. Adv Eng Softw 83:80–98

    Google Scholar 

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

    Google Scholar 

  • Mirjalili S (2016b) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27:1053–1073

    Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

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

    Google Scholar 

  • Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-Verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl. https://doi.org/10.1007/s00521-015-1870-7

    Article  Google Scholar 

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

    Google Scholar 

  • Montemurro M, Vincenti A, Vannucci P (2013) The automatic dynamic penalisation method (ADP) for handling constraints with genetic algorithms. Comput Methods Appl Mech Eng 256:70–87

    MathSciNet  MATH  Google Scholar 

  • Neggaz N, Ewees AA, Elaziz MA, Mafarja M (2020) Boosting salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selection. Expert Syst Appl 145:113103

    Google Scholar 

  • Özmen A, Kropat E, Weber GW (2017) Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty. Optimization 66:2135–2155

    MathSciNet  MATH  Google Scholar 

  • Pedamallu C, Ozdamar L, Ganesh L, Weber G-W, Kropat E (2010) A system dynamics model for improving primary education enrollment in a developing country. Organizacija 43:90–101

    Google Scholar 

  • Ragsdell KM, Phillips DT (1976) Optimal design of a class of welded structures using geometric programming. J Manuf Sci Eng Trans ASME 98:1021–1025

    Google Scholar 

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

    Google Scholar 

  • Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (ny) 179:2232–2248

    MATH  Google Scholar 

  • Rosenbrock HH (1960) An automatic method for finding the greatest or least value of a function. Comput J 3:175–184

    MathSciNet  Google Scholar 

  • Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm : a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput J 13:2592–2612

    Google Scholar 

  • Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl-Based Syst 75:1–18

    Google Scholar 

  • Samareh Moosavi SH, Khatibi Bardsiri V (2017) Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Eng Appl Artif Intell 60:1–15

    Google Scholar 

  • Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Google Scholar 

  • Savsani P, Savsani V (2016) Passing vehicle search (PVS): a novel metaheuristic algorithm. Appl Math Model 40:3951–3978

    Google Scholar 

  • Singh N, Singh SB, Houssein EH (2022) Hybridizing salp swarm algorithm with particle swarm optimization algorithm for recent optimization functions. Evol Intell 15:23–56

    Google Scholar 

  • Vashishtha G, Kumar R (2021a) An effective health indicator for the Pelton wheel using a Levy flight mutated. Meas Sci Technol 32:2

    Google Scholar 

  • Vashishtha G, Kumar R (2021b) Autocorrelation energy and aquila optimizer for MED filtering of sound signal to detect bearing defect in Francis turbine. Meas Sci Technol 33:15006

    Google Scholar 

  • Vashishtha G, Kumar R (2021c) Centrifugal pump impeller defect identification by the improved adaptive variational mode decomposition through vibration signals. Eng Res Express 3:035041

    Google Scholar 

  • Vashishtha G, Kumar R (2021d) Centrifugal pump impeller defect identification by the improved adaptive variational mode decomposition through vibration signals. Eng Res Express. https://doi.org/10.1088/2631-8695/ac23b5

    Article  Google Scholar 

  • Vashishtha G, Kumar R (2022) An amended grey wolf optimization with mutation strategy to diagnose bucket defects in Pelton wheel. Meas J Int Meas Confed 187:110272

    Google Scholar 

  • Vashishtha G, Kumar R (2023) Feature selection based on gaussian ant lion optimizer for fault identification in centrifugal pump BT. In: Gupta VK, Amarnath C, Tandon P, Ansari MZ (eds) Recent advances in machines and mechanisms. Springer Nature, Singapore, pp 295–310

    Google Scholar 

  • Vashishtha G, Chauhan S, Singh M, Kumar R (2021) Bearing defect identification by swarm decomposition considering permutation entropy measure and opposition-based slime mould algorithm. Measurement 178:109389

    Google Scholar 

  • Wang L, Li LP (2010) An effective differential evolution with level comparison for constrained engineering design. Struct Multidiscip Optim 41:947–963

    Google Scholar 

  • Wang Y, Cai Z, Zhou Y, Fan Z (2009) Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique. Struct Multidiscip Optim 37:395–413

    Google Scholar 

  • Wang Z, Luo Q, Zhou Y (2020) Hybrid metaheuristic algorithm using butterfly and flower pollination base on mutualism mechanism for global optimization problems. Engineering with computers. Springer, London. https://doi.org/10.1007/s00366-020-01025-8

    Chapter  Google Scholar 

  • Weber GW, Defterli O, Alparslan Gök SZ, Kropat E (2011) Modeling, inference and optimization of regulatory networks based on time series data. Eur J Oper Res 211:1–14

    MathSciNet  MATH  Google Scholar 

  • Wolpert DH, Nna D, Road H, Jose S, Macready WG (1996) No free lunch theorems for optimization 1–32

  • Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci (ny) 178:3043–3074

    Google Scholar 

  • Zhang H et al (2022) Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems. Eng Comput. https://doi.org/10.1007/s00366-021-01545-x

    Article  Google Scholar 

  • Zhang H et al (2022) A multi-strategy enhanced salp swarm algorithm for global optimization. Eng Comput 38:1177–1203

    Google Scholar 

  • Zw G, Jh K, Gv L (2001) A new heuristic optimization algorithm: harmony search. SIMULATION 76:60–68

    Google Scholar 

Download references

Funding

The study is not supported by any source or any organizations.

Author information

Authors and Affiliations

  1. Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421, Wroclaw, Poland

    Sumika Chauhan & Govind Vashishtha

  2. Precision Metrology Laboratory, Department of Mechanical Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, 148 106, India

    Govind Vashishtha

  3. Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  4. School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia

    Laith Abualigah

  5. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon

    Laith Abualigah

  6. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  7. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  8. Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan

    Laith Abualigah

  9. School of Engineering and Technology, Sunway University Malaysia, 27500, Petaling Jaya, Malaysia

    Laith Abualigah

  10. College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325 035, China

    Anil Kumar

Authors
  1. Sumika Chauhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Govind Vashishtha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laith Abualigah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anil Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SC: data curation, software, writing-original draft, methodology; GV: software, writing draft, methodology; LA: methodology, supervision; AK: writing-review & editing, supervision.

Corresponding author

Correspondence to Govind Vashishtha.

Ethics declarations

Conflict of interest

There is no conflict of Interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chauhan, S., Vashishtha, G., Abualigah, L. et al. Boosting salp swarm algorithm by opposition-based learning concept and sine cosine algorithm for engineering design problems. Soft Comput 27, 18775–18802 (2023). https://doi.org/10.1007/s00500-023-09147-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-09147-z

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy