Skip to main content

Advertisement

SpringerLink
Log in

MJS: a modified artificial jellyfish search algorithm for continuous optimization problems

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Artificial jellyfish search algorithm (JS) is a recently proposed optimization algorithm inspired by the search behavior of jellyfish in the ocean. There are two different search behaviors in JS: the motion of the jellyfish due to ocean currents (global search) and the motion of the jellyfish within the swarm (local search). In this study, two modifications, one in the local and the other in the global search formula, were made to strengthen the search capability of the standard algorithm. By means of the modification made in the global search, the search direction was directed toward the best and elite set individuals and higher quality solutions were found. A more detailed search around the individuals and the longer preservation of diversity in the population were ensured by another modification to the local search. In addition, it was studied to find the most ideal value for the time control mechanism that provides the transition between local and global search. The new modified algorithm (MJS), obtained as a result of all these modifications, was tested on a total of eighty minimization problems, including standard benchmark functions, Congress of Evolutionary Computation 2013 (CEC2013) test function, and Congress of Evolutionary Computation 2017 (CEC2017) test functions. The results of these tests for different dimensions were compared to the standard JS algorithm and the algorithms selected from the literature. Also, the results were interpreted by means of statistical tests. These comparisons and statistical tests showed that the proposed MJS algorithm produced acceptable, successful, and competitive results.

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

Similar content being viewed by others

Data availability

Data will be made available on reasonable request.

References

  1. Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver Press, Bristol, UK

    Google Scholar 

  2. Chou JS, Truong DN (2021) A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Appl Math Comput 389:125535

    MATH  Google Scholar 

  3. Chou JS, Truong DN (2020) Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems. Chaos Solitons Fractals 135:109738

    Article  Google Scholar 

  4. Gouda EA, Kotb MF, El-Fergany AA (2021) Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: steady-state performance and analysis. Energy 221:119836

    Article  Google Scholar 

  5. Shaheen AM, Elsayed AM, Ginidi AR, Elattar EE, El-Sehiemy RA (2021) Effective automation of distribution systems with joint integration of DGs/SVCs considering reconfiguration capability by jellyfish search algorithm. IEEE Access 9:92053–92069

    Article  Google Scholar 

  6. Almodfer R, Zayed ME, Abd Elaziz M, Aboelmaaref MM, Mohammed M, Elsheikh AH (2022) Modeling of a solar-powered thermoelectric air-conditioning system using a random vector functional link network integrated with jellyfish search algorithm. Case Stud Therm Eng 31:101797

    Article  Google Scholar 

  7. Ginidi A, Elsayed A, Shaheen A, Elattar E, El-Sehiemy R (2021) An innovative hybrid heap-based and jellyfish search algorithm for combined heat and power economic dispatch in electrical grids. Mathematics 9(17):2053

    Article  Google Scholar 

  8. Yıldızdan G, Baykan ÖK (2021) A novel artificial jellyfish search algorithm improved with detailed local search strategy. In: 2021 6th international conference on computer science and engineering (UBMK): IEEE, Published, pp. 180–185

  9. Kaveh A, Biabani Hamedani K, Kamalinejad M, Joudaki A (2021) Quantum-based jellyfish search optimizer for structural optimization. Int J Optim Civ Eng 11(2):329–356

    Google Scholar 

  10. Jiang SJ, Nguyen TT, Dao TK, Vu VD, Ngo TG (2021) A power system economic load dispatch using jellyfish search algorithm. In: Tiwari A, Ahuja K, Yadav A, Bansal JC, Deep K, Nagar AK (eds) Soft computing for problem solving. Springer, Singapore, pp 321–331. https://doi.org/10.1007/978-981-16-2712-5_271

    Chapter  Google Scholar 

  11. Tiwari V, Dubey HM, Pandit M (2021) Optimal allocation of DG and capacitor units using jellyfish search algorithm. In: 2021 13th IEEE PES Asia Pacific power & energy engineering conference (APPEEC): IEEE, Published, pp. 1–6

  12. Siddiqui NI et al (2021) Artificial jellyfish search algorithm-based selective harmonic elimination in a cascaded H-bridge multilevel inverter. Electronics 10(19):2402

    Article  Google Scholar 

  13. Manita G, Zermani A (2021) A modified jellyfish search optimizer with orthogonal learning strategy. Proced Comput Sci 192:697–708

    Article  Google Scholar 

  14. Elkabbash ET, Mostafa RR, Barakat SI (2021) Android malware classification based on random vector functional link and artificial jellyfish search optimizer. PLoS One 16(11):e0260232

    Article  Google Scholar 

  15. Bujok P (2021) Three steps to improve jellyfish search optimizer. In: MENDEL, vol. 27, no. 1, Published, pp. 29–40

  16. Abdel-Basset M, Mohamed R, Abouhawwash M, Chakrabortty RK, Ryan MJ, Nam Y (2021) An improved jellyfish algorithm for multilevel thresholding of magnetic resonance brain image segmentations. Comput Mater Continua 68(3):2961-2977. https://doi.org/10.32604/cmc.2021.016956

    Article  Google Scholar 

  17. Huang R, Lin Y (2021) A maximum power point tracking strategy for photovoltaic system based on improved artificial jellyfish search optimizer. In: 2021 3rd international academic exchange conference on science and technology innovation (IAECST), IEEE Published, pp. 1918–1922

  18. Alam A et al (2021) Jellyfish search optimization algorithm for MPP tracking of PV system. Sustainability 13(21):11736

    Article  Google Scholar 

  19. Youssef H, Hassan MH, Kamel S, Elsayed SK (2021) Parameter estimation of single phase transformer using jellyfish search optimizer algorithm. In: 2021 IEEE international conference on automation/XXIV congress of the chilean association of automatic control (ICA-ACCA), IEEE Published, pp. 1–4

  20. Rajpurohit J (2021) A modified jellyfish search optimizer with opposition based learning and biased passive swarm motion. Ingénierie des Systèmes d'Information 26(6):577–584

    Article  Google Scholar 

  21. Rajpurohit J, Sharma TK (2022) Chaotic active swarm motion in jellyfish search optimizer. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-021-01561-6

    Article  Google Scholar 

  22. Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18(1):89–98

    Article  MATH  Google Scholar 

  23. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    Article  MATH  Google Scholar 

  24. Liang JJ, Qu B, Suganthan PN, Hernández-Díaz AG (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Comput Intell Lab Zhengzhou Univ Zhengzhou China Nanyang Technol Univ Singap Tech Rep 201212(34):281–295

    Google Scholar 

  25. Awad N, Ali M, Liang J, Qu B, Suganthan P (2016) Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization. Zhengzhou University, Zhengzhou

    Google Scholar 

  26. Sheskin DJ (2003) Handbook of parametric and nonparametric statistical procedures. Chapman and Hall/CRC, Boca Raton, FL, USA

    Book  MATH  Google Scholar 

  27. García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J Heuristics 15(6):617–644

    Article  MATH  Google Scholar 

  28. Mousavirad SJ, Ebrahimpour-Komleh H (2017) Human mental search: a new population-based metaheuristic optimization algorithm. Appl Intell 47(3):850–887

    Article  Google Scholar 

  29. Sahargahi V, Majidnezhad V, Afshord ST, Jafari Y (2022) An intelligent chaotic clonal optimizer. Appl Soft Comput 115:108126

    Article  Google Scholar 

  30. Le Chau N, Dao TP, Dang VA (2020) An efficient hybrid approach of improved adaptive neural fuzzy inference system and teaching learning-based optimization for design optimization of a jet pump-based thermoacoustic-stirling heat engine. Neural Comput Appl 32(11):7259–7273

    Article  Google Scholar 

  31. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95-international conference on neural networks, vol. 4. IEEE Published, pp. 1942–1948

  32. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132

    MATH  Google Scholar 

  33. Price KV (2013) Differential evolution. In: Zelinka I, Snášel V, Abraham A (eds) Handbook of optimization. Springer, Berlin, Heidelberg, pp 187–214. https://doi.org/10.1007/978-3-642-30504-7_8

    Chapter  Google Scholar 

  34. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, pp 65–74. https://doi.org/10.1007/978-3-642-12538-6_6

    Chapter  Google Scholar 

  35. Koyuncu H, Ceylan R (2019) A PSO based approach: scout particle swarm algorithm for continuous global optimization problems. J Comput Des Eng 6(2):129–142

    Google Scholar 

  36. Zeng T et al (2022) Artificial bee colony based on adaptive search strategy and random grouping mechanism. Expert Syst Appl 192:116332

    Article  Google Scholar 

  37. Mohamed AW, Mohamed AK (2019) Adaptive guided differential evolution algorithm with novel mutation for numerical optimization. Int J Mach Learn Cybern 10(2):253–277

    Article  Google Scholar 

  38. Chawla M, Duhan M (2015) Bat algorithm: a survey of the state-of-the-art. Appl Artif Intell 29(6):617–634

    Article  Google Scholar 

  39. Shaheen AM, El-Sehiemy RA, Alharthi MM, Ghoneim SS, Ginidi AR (2021) Multi-objective jellyfish search optimizer for efficient power system operation based on multi-dimensional OPF framework. Energy 237:121478

    Article  Google Scholar 

  40. Abdel-Basset M, Mohamed R, Chakrabortty RK, Ryan MJ, El-Fergany A (2021) An improved artificial jellyfish search optimizer for parameter identification of photovoltaic models. Energies 14(7):1867

    Article  Google Scholar 

  41. Singh A (2019) Laplacian whale optimization algorithm. Int J Syst Assur Eng Manag 10(4):713–730

    Article  Google Scholar 

  42. Li Y, Zhao Y, Liu J (2021) Dimension by dimension dynamic sine cosine algorithm for global optimization problems. Appl Soft Comput 98:106933

    Article  Google Scholar 

  43. Zhao X, Fang Y, Liu L, Li J, Xu M (2020) An improved moth-flame optimization algorithm with orthogonal opposition-based learning and modified position updating mechanism of moths for global optimization problems. Appl Intell 50(12):4434–4458

    Article  Google Scholar 

  44. Ahmed AM, Rashid TA, Saeed SAM (2021) Dynamic cat swarm optimization algorithm for backboard wiring problem. Neural Comput Appl 33(20):13981–13997

    Article  Google Scholar 

  45. Zhu A, Xu C, Li Z, Wu J, Liu Z (2015) Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC. J Syst Eng Electron 26(2):317–328

    Article  Google Scholar 

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

    Article  Google Scholar 

  47. 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 

  48. Zhou X, Lu J, Huang J, Zhong M, Wang M (2021) Enhancing artificial bee colony algorithm with multi-elite guidance. Inf Sci 543:242–258

    Article  MATH  Google Scholar 

  49. Peng H, Zeng Z, Deng C, Wu Z (2021) Multi-strategy serial cuckoo search algorithm for global optimization. Knowl-Based Syst 214:106729

    Article  Google Scholar 

  50. Lin A, Sun W, Yu H, Wu G, Tang H (2019) Adaptive comprehensive learning particle swarm optimization with cooperative archive. Appl Soft Comput 77:533–546

    Article  Google Scholar 

  51. Xie Z, Zhang C, Ouyang H, Li S, Gao L (2021) Self-adaptively commensal learning-based Jaya algorithm with multi-populations and its application. Soft Comput 25(24):15163–15181

    Article  Google Scholar 

  52. Li C, He Y, Xiao D, Luo Z, Fan J, Liu PX (2022) A novel hybrid approach of ABC with SCA for the parameter optimization of SVR in blind image quality assessment. Neural Comput Appl 34:4165–4191. https://doi.org/10.1007/s00521-021-06435-3

    Article  Google Scholar 

  53. Xia X, Tang Y, Wei B, Zhang Y, Gui L, Li X (2020) Dynamic multi-swarm global particle swarm optimization. Computing 102(7):1587–1626

    Article  Google Scholar 

  54. Noel MM, Muthiah-Nakarajan V, Amali GB, Trivedi AS (2021) A new biologically inspired global optimization algorithm based on firebug reproductive swarming behaviour. Expert Syst Appl 183:115408

    Article  Google Scholar 

  55. Salgotra R, Singh U, Saha S, Gandomi AH (2020) Improving cuckoo search: incorporating changes for CEC 2017 and CEC 2020 benchmark problems. In: 2020 IEEE congress on evolutionary computation (CEC). IEEE Published, pp. 1–7

  56. Mohamed AW, Hadi AA, Mohamed AK (2020) Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. Int J Mach Learn Cybern 11(7):1501–1529

    Article  Google Scholar 

  57. Biedrzycki R (2017) A version of IPOP-CMA-ES algorithm with midpoint for CEC 2017 single objective bound constrained problems. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE Published, pp. 1489–1494.

  58. Kommadath R, Kotecha P (2017) Teaching learning based optimization with focused learning and its performance on CEC2017 functions. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE Published, pp. 2397–2403

  59. Diep QB (2019) Self-organizing migrating algorithm team to team adaptive–SOMA T3A. In: 2019 IEEE congress on evolutionary computation (CEC). IEEE Published, pp. 1182–1187

  60. Amponsah AA, Han F, Ling QH, Kudjo PK (2021) An enhanced class topper algorithm based on particle swarm optimizer for global optimization. Appl Intell 51(2):1022–1040

    Article  Google Scholar 

  61. Tang HK, Cai Q, Goh SK (2022) Meta-heuristic optimizer inspired by the philosophy of Yi Jing PREPRINT (Version 1) available at Research Square. https://doi.org/10.21203/rs.3.rs-1259241/v1

Download references

Author information

Authors and Affiliations

  1. Kulu Vocational School, Selcuk University, Konya, Turkey

    Gülnur Yildizdan

Authors
  1. Gülnur Yildizdan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gülnur Yildizdan.

Ethics declarations

Conflict of interest

The author declares that she has no conflict of interest.

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 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

Yildizdan, G. MJS: a modified artificial jellyfish search algorithm for continuous optimization problems. Neural Comput & Applic 35, 3483–3519 (2023). https://doi.org/10.1007/s00521-022-07842-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07842-w

Keywords

Search

Navigation