Skip to main content
Springer Nature Link
Log in

ICSOMPA: A novel improved hybrid algorithm for global optimisation

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

The Marine Predators Algorithm (MPA) is among the recently proposed metaheuristic algorithms (MAs), and it got its inspiration from the ocean predators’ foraging behaviour based on Brownian and Levy motions. Good exploration, convergence accuracy, ease of implementation, easy parameter settings, fewer parameters, etc., are some of its strengths. Nevertheless, it experiences premature convergence and local optima trapping sometimes. The Competitive Swarm Optimiser (CSO) is a Particle Swarm Optimiser (PSO) variant. It got its inspiration from the social groups’ collective decision-making and social behaviour. Good exploitation, a balance between exploitation and exploration, low premature convergence, algorithmic simplicity, etc., are some of its strengths. However, it has a loss of diversity and premature convergence. Aiming at solving the MPA’s weaknesses and utilising the complementary strengths of MPA and CSO, an improved hybrid MPA has been proposed and it’s named ICSOMPA. The MPA was first improved by utilising a chaotic mapping strategy for the MPA initialisation, utilising an adaptive convergence factor (CF) for step size control aiming at striking a balance between local exploitation and global exploration, utilising the Weibull distribution in place of Brownian motion aiming at preventing algorithm local trapping, and utilising chaotic sequences in the MPA’s early stages as opposed to using random numbers to avoid overlap and uneven agent distribution. The improved MPA was then hybridised with the CSO aiming at leveraging the MPA’s and CSO’s strengths to provide higher convergence accuracy, convergence speed, and avoiding local optima trapping. The proposed algorithm’s performance was tested and validated using the Congress on Evolutionary Computation (CEC) suites and engineering design problems. The CEC2014, CEC2017, CEC2020, CEC2022, and the 3 most employed engineering design problems have been utilised. Six different sets of experiments have been conducted utilising different dimensions of the CEC suites by carrying out convergence accuracy analysis, convergence rate analysis, the Wilcoxon test, the Friedman test, and the Bonferroni-Holm test. Some of the state-of-the-art and variant MAs have been utilised for comparison purposes. From the experimental results, the ICSOMPA had a superior performance compared to the MAs used for comparison. The experiments on the CEC suites showed that it can strike a good balance between exploration and exploitation. In general, it also has higher convergence accuracy and rate. The statistical analyses conducted showed a significant difference between the results obtained by the ICSOMPA and the other algorithms.

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
Algorithm 1
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Algorithm 2
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Explore related subjects

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

Data availability

No datasets were generated or analysed during the current study.

References

  1. Hashim FA, Houssein EH, Hussain K et al (2022) Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Math Comput Simul 192:84–110. https://doi.org/10.1016/j.matcom.2021.08.013

    Article  MathSciNet  Google Scholar 

  2. Chan-Ley M, Olague G (2020) Categorization of digitized artworks by media with brain programming. Appl Opt 59:4437–4447. https://doi.org/10.1364/AO.385552

    Article  Google Scholar 

  3. Abualigah L, Almotairi KH, Elaziz MA (2023) Multilevel thresholding image segmentation using meta-heuristic optimization algorithms: comparative analysis, open challenges and new trends. Appl Intell 53:11654–11704. https://doi.org/10.1007/s10489-022-04064-4

    Article  Google Scholar 

  4. Aslan S, Erkin T (2023) An immune plasma algorithm based approach for UCAV path planning. J King Saud Univ - Comput Inform Sci 35:56–69. https://doi.org/10.1016/j.jksuci.2022.06.004

    Article  Google Scholar 

  5. Gupta S, Abderazek H, Yıldız BS et al (2021) Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems. Expert Syst Appl 183:115351. https://doi.org/10.1016/j.eswa.2021.115351

    Article  Google Scholar 

  6. Ensermu G, Vijayashanthi M, Suresh M et al (2023) An FRLQG Controller-Based Small-Signal Stability Enhancement of Hybrid Microgrid Using the BCSSO Algorithm. J Elect Comput Eng 2023:1–15. https://doi.org/10.1155/2023/8404457

    Article  Google Scholar 

  7. Ali Shah Tirmzi SA, Umar AI, Shirazi SH et al (2022) Modified genetic algorithm for optimal classification of abnormal MRI tissues using hybrid model with discriminative learning approach. Comput Methods Biomech Biomed Eng: Imaging Vis 10:14–21. https://doi.org/10.1080/21681163.2021.1956371

    Article  Google Scholar 

  8. Peng Z, Wang L, Tong L et al (2023) Multi-threshold image segmentation of 2D OTSU inland ships based on improved genetic algorithm. PLoS ONE 18:e0290750. https://doi.org/10.1371/journal.pone.0290750

    Article  Google Scholar 

  9. Xi E, Zhang J (2021) Research on Image Deblurring Processing Technology Based on Genetic Algorithm. J Phys: Conf Ser 1852:022042. https://doi.org/10.1088/1742-6596/1852/2/022042

    Article  Google Scholar 

  10. Wang J, Liu Y, Rao S et al (2023) A novel self-adaptive multi-strategy artificial bee colony algorithm for coverage optimization in wireless sensor networks. Ad Hoc Netw 150:103284. https://doi.org/10.1016/j.adhoc.2023.103284

    Article  Google Scholar 

  11. Bai Y, Zhang C, Bai W (2023) A two-level parallel decomposition-based artificial bee colony method for dynamic multi-objective optimization problems. Applied Soft Computing 110741. https://doi.org/10.1016/j.asoc.2023.110741

  12. Zare M, Ghasemi M, Zahedi A et al (2023) A Global Best-guided Firefly Algorithm for Engineering Problems. J Bionic Eng 20:2359–2388. https://doi.org/10.1007/s42235-023-00386-2

    Article  Google Scholar 

  13. Abdel-Basset M, Mohamed R, Azeem SAA et al (2023) Kepler optimization algorithm: A new metaheuristic algorithm inspired by Kepler’s laws of planetary motion. Knowl-Based Syst 268:110454. https://doi.org/10.1016/j.knosys.2023.110454

    Article  Google Scholar 

  14. Hashim FA, Houssein EH, Mabrouk MS et al (2019) Henry gas solubility optimization: A novel physics-based algorithm. Futur Gener Comput Syst 101:646–667. https://doi.org/10.1016/j.future.2019.07.015

    Article  Google Scholar 

  15. Mirjalili S (2016) SCA: A Sine Cosine Algorithm for solving optimization problems. Knowl-Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022

    Article  Google Scholar 

  16. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-Verse Optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513. https://doi.org/10.1007/s00521-015-1870-7

    Article  Google Scholar 

  17. 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. https://doi.org/10.1016/j.compstruc.2012.07.010

    Article  Google Scholar 

  18. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: A Gravitational Search Algorithm. Inf Sci 179:2232–2248. https://doi.org/10.1016/j.ins.2009.03.004

    Article  Google Scholar 

  19. Geem ZW, Kim JH, Loganathan GV (2001) A New Heuristic Optimization Algorithm: Harmony Search. SIMULATION 76:60–68. https://doi.org/10.1177/003754970107600201

    Article  Google Scholar 

  20. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by Simulated Annealing. Science 220:671–680. https://doi.org/10.1126/science.220.4598.671

    Article  MathSciNet  Google Scholar 

  21. Černý V (1985) Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. J Optim Theory Appl 45:41–51. https://doi.org/10.1007/BF00940812

    Article  MathSciNet  Google Scholar 

  22. 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 13:2592–2612. https://doi.org/10.1016/j.asoc.2012.11.026

    Article  Google Scholar 

  23. Hashim FA, Hussain K, Houssein EH et al (2021) Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Appl Intell 51:1531–1551. https://doi.org/10.1007/s10489-020-01893-z

    Article  Google Scholar 

  24. Su H, Zhao D, Heidari AA et al (2023) RIME: A physics-based optimization. Neurocomputing 532:183–214. https://doi.org/10.1016/j.neucom.2023.02.010

    Article  Google Scholar 

  25. Ghasemi M, Zare M, Zahedi A et al (2023) Geyser Inspired Algorithm: A New Geological-inspired Meta-heuristic for Real-parameter and Constrained Engineering Optimization. J Bionic Eng. https://doi.org/10.1007/s42235-023-00437-8

    Article  Google Scholar 

  26. Anita YA (2019) AEFA: Artificial electric field algorithm for global optimization. Swarm Evol Comput 48:93–108. https://doi.org/10.1016/j.swevo.2019.03.013

    Article  Google Scholar 

  27. David B. Fogel (1998) Artificial Intelligence through Simulated Evolution. In: Evolutionary Computation: The Fossil Record. IEEE, 227–296

  28. Holland JH (1975) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, 1st edn. MIT Press, Cambridge, Massachusetts, USA

    Google Scholar 

  29. Beyer H-G, Schwefel H-P (2002) Evolution strategies – A comprehensive introduction. Nat Comput 1:3–52. https://doi.org/10.1023/A:1015059928466

    Article  MathSciNet  Google Scholar 

  30. Koza JR (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection, 1st edn. The MIT Press, Cambridge, MA, USA

    Google Scholar 

  31. Ferreira C (2001) Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems 13:87–129

    MathSciNet  Google Scholar 

  32. Storn R, Price K (1997) Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. J Global Optim 11:341–359. https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  Google Scholar 

  33. Qin AK, Suganthan PN (2005) Self-adaptive Differential Evolution Algorithm for Numerical Optimization. 2005 IEEE Congress on Evolutionary Computation. IEEE, Edinburgh, Scotland, UK, pp 1785–1791

    Chapter  Google Scholar 

  34. Zhang J, Sanderson AC (2009) JADE: Adaptive Differential Evolution With Optional External Archive. IEEE Trans Evol Computat 13:945–958. https://doi.org/10.1109/TEVC.2009.2014613

    Article  Google Scholar 

  35. Tanabe R, Fukunaga A (2013) Success-history based parameter adaptation for Differential Evolution. 2013 IEEE Congress on Evolutionary Computation. IEEE, Cancun, Mexico, pp 71–78

    Chapter  Google Scholar 

  36. Golalipour K, Faraji Davoudkhani I, Nasri S et al (2023) The corona virus search optimizer for solving global and engineering optimization problems. Alex Eng J 78:614–642. https://doi.org/10.1016/j.aej.2023.07.066

    Article  Google Scholar 

  37. Chen X, Liu Y, Li X et al (2019) A New Evolutionary Multiobjective Model for Traveling Salesman Problem. IEEE Access 7:66964–66979. https://doi.org/10.1109/ACCESS.2019.2917838

    Article  Google Scholar 

  38. Han M, Liu C, Xing J (2014) An evolutionary membrane algorithm for global numerical optimization problems. Inf Sci 276:219–241. https://doi.org/10.1016/j.ins.2014.02.057

    Article  MathSciNet  Google Scholar 

  39. Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13:533–549. https://doi.org/10.1016/0305-0548(86)90048-1

    Article  MathSciNet  Google Scholar 

  40. Yang Y, Chen H, Heidari AA, Gandomi AH (2021) Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst Appl 177:114864. https://doi.org/10.1016/j.eswa.2021.114864

    Article  Google Scholar 

  41. Samareh Moosavi SH, Bardsiri VK (2019) Poor and rich optimization algorithm: A new human-based and multi populations algorithm. Eng Appl Artif Intell 86:165–181. https://doi.org/10.1016/j.engappai.2019.08.025

    Article  Google Scholar 

  42. Askari Q, Younas I, Saeed M (2020) Political Optimizer: A novel socio-inspired meta-heuristic for global optimization. Knowl-Based Syst 195:105709. https://doi.org/10.1016/j.knosys.2020.105709

    Article  Google Scholar 

  43. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Comput Aided Des 43:303–315. https://doi.org/10.1016/j.cad.2010.12.015

    Article  Google Scholar 

  44. Kaveh A (2021) Imperialist Competitive Algorithm. In: Kaveh A (ed) Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer International Publishing, Cham, pp 369–390

    Chapter  Google Scholar 

  45. Zhang Y, Jin Z (2020) Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems. Expert Syst Appl 148:113246. https://doi.org/10.1016/j.eswa.2020.113246

    Article  Google Scholar 

  46. TummalaSLV A, Ramakrishna NSS, Elavarasan RM et al (2022) War Strategy Optimization Algorithm: A New Effective Metaheuristic Algorithm for Global Optimization. IEEE Access 10:25073–25105. https://doi.org/10.1109/ACCESS.2022.3153493

    Article  Google Scholar 

  47. Moazzeni AR, Khamehchi E (2020) Rain optimization algorithm (ROA): A new metaheuristic method for drilling optimization solutions. J Petrol Sci Eng 195:107512. https://doi.org/10.1016/j.petrol.2020.107512

    Article  Google Scholar 

  48. Agushaka JO, Ezugwu AE, Abualigah L (2022) Dwarf Mongoose Optimization Algorithm. Comput Methods Appl Mech Eng 391:114570. https://doi.org/10.1016/j.cma.2022.114570

    Article  MathSciNet  Google Scholar 

  49. Dhiman G, Kumar V (2017) Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70. https://doi.org/10.1016/j.advengsoft.2017.05.014

    Article  Google Scholar 

  50. Zhao W, Zhang Z, Wang L (2020) Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell 87:103300. https://doi.org/10.1016/j.engappai.2019.103300

    Article  Google Scholar 

  51. Yang X-S (2012) Flower Pollination Algorithm for Global Optimization. In: Durand-Lose J, Jonoska N (eds) Unconventional Computation and Natural Computation. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 240–249

    Chapter  Google Scholar 

  52. Gandomi AH, Alavi AH (2012) Krill herd: A new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17:4831–4845. https://doi.org/10.1016/j.cnsns.2012.05.010

    Article  MathSciNet  Google Scholar 

  53. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Comput Struct 169:1–12. https://doi.org/10.1016/j.compstruc.2016.03.001

    Article  Google Scholar 

  54. Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine Predators Algorithm: A nature-inspired metaheuristic. Expert Syst Appl 152:113377. https://doi.org/10.1016/j.eswa.2020.113377

    Article  Google Scholar 

  55. Yang X-S, Deb S (2009) Cuckoo Search via Lévy flights. In: World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE, India, pp 210–214

  56. Yang X-S (2010) A New Metaheuristic Bat-Inspired Algorithm. In: González JR, Pelta DA, Cruz C et al (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 65–74

    Chapter  Google Scholar 

  57. Karaboga D (2005) An Idea Based on Honey Bee Swarm for Numerical Optimization. Erciyes University, Kayseri, Turkiye, Department of Computer Engineering, Engineering Faculty

    Google Scholar 

  58. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper Optimisation Algorithm: Theory and application. Adv Eng Softw 105:30–47. https://doi.org/10.1016/j.advengsoft.2017.01.004

    Article  Google Scholar 

  59. Mirjalili S, Gandomi AH, Mirjalili SZ et al (2017) Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002

    Article  Google Scholar 

  60. Mirjalili S (2015) Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006

    Article  Google Scholar 

  61. Mirjalili S, Lewis A (2016) The Whale Optimization Algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  62. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  63. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22:52–67. https://doi.org/10.1109/MCS.2002.1004010

    Article  Google Scholar 

  64. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst, Man, Cybern B 26:29–41. https://doi.org/10.1109/3477.484436

    Article  Google Scholar 

  65. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks. IEEE, Perth, WA, Australia, 1942–1948

  66. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Computat 10:281–295. https://doi.org/10.1109/TEVC.2005.857610

    Article  Google Scholar 

  67. Cheng R, Jin Y (2015) A Competitive Swarm Optimizer for Large Scale Optimization. IEEE Trans Cybern 45:191–204. https://doi.org/10.1109/TCYB.2014.2322602

    Article  Google Scholar 

  68. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput & Applic 27:1053–1073. https://doi.org/10.1007/s00521-015-1920-1

    Article  Google Scholar 

  69. Dutta T, Bhattacharyya S, Dey S, Platos J (2020) Border Collie Optimization. IEEE. Access 8:109177–109197. https://doi.org/10.1109/ACCESS.2020.2999540

    Article  Google Scholar 

  70. Heidari AA, Mirjalili S, Faris H et al (2019) Harris hawks optimization: Algorithm and applications. Futur Gener Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028

    Article  Google Scholar 

  71. Fathollahi-Fard AM, Hajiaghaei-Keshteli M, Tavakkoli-Moghaddam R (2020) Red deer algorithm (RDA): a new nature-inspired meta-heuristic. Soft Comput 24:14637–14665. https://doi.org/10.1007/s00500-020-04812-z

    Article  Google Scholar 

  72. Mirjalili S (2015) The Ant Lion Optimizer. Adv Eng Softw 83:80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010

    Article  Google Scholar 

  73. Liang S, Pan Y, Zhang H et al (2022) Marine Predators Algorithm Based on Adaptive Weight and Chaos Factor and Its Application. Sci Program 2022:1–12. https://doi.org/10.1155/2022/4623980

    Article  Google Scholar 

  74. Rai R, Dhal KG, Das A, Ray S (2023) An Inclusive Survey on Marine Predators Algorithm: Variants and Applications. Arch Computat Methods Eng 30:3133–3172. https://doi.org/10.1007/s11831-023-09897-x

    Article  Google Scholar 

  75. Al-Betar MA, Awadallah MA, Makhadmeh SN et al (2023) Marine Predators Algorithm: A Review. Arch Computat Methods Eng 30:3405–3435. https://doi.org/10.1007/s11831-023-09912-1

    Article  Google Scholar 

  76. Ali S, Bhargava A, Saxena A, Kumar P (2023) A Hybrid Marine Predator Sine Cosine Algorithm for Parameter Selection of Hybrid Active Power Filter. Mathematics 11:598. https://doi.org/10.3390/math11030598

    Article  Google Scholar 

  77. Ma Y, Chang C, Lin Z et al (2022) Modified Marine Predators Algorithm hybridized with teaching-learning mechanism for solving optimization problems. MBE 20:93–127. https://doi.org/10.3934/mbe.2023006

    Article  Google Scholar 

  78. Mohd Tumari MZ, Ahmad MA, Suid MH, Hao MR (2023) An Improved Marine Predators Algorithm-Tuned Fractional-Order PID Controller for Automatic Voltage Regulator System. Fractal Fract 7:561. https://doi.org/10.3390/fractalfract7070561

    Article  Google Scholar 

  79. Salgotra R, Singh S, Singh U et al (2023) Marine predator inspired naked mole-rat algorithm for global optimization. Expert Syst Appl 212:118822. https://doi.org/10.1016/j.eswa.2022.118822

    Article  Google Scholar 

  80. Eid A, Kamel S, Abualigah L (2021) Marine predators algorithm for optimal allocation of active and reactive power resources in distribution networks. Neural Comput & Applic 33:14327–14355. https://doi.org/10.1007/s00521-021-06078-4

    Article  Google Scholar 

  81. Wang N, Wang JS, Zhu LF et al (2021) A Novel Dynamic Clustering Method by Integrating Marine Predators Algorithm and Particle Swarm Optimization Algorithm. IEEE Access 9:3557–3569. https://doi.org/10.1109/ACCESS.2020.3047819

    Article  Google Scholar 

  82. Shaheen MAM, Yousri D, Fathy A et al (2020) A Novel Application of Improved Marine Predators Algorithm and Particle Swarm Optimization for Solving the ORPD Problem. Energies 13:5679. https://doi.org/10.3390/en13215679

    Article  Google Scholar 

  83. Houssein EH, Mahdy MA, Fathy A, Rezk H (2021) A modified Marine Predator Algorithm based on opposition based learning for tracking the global MPP of shaded PV system. Expert Syst Appl 183:115253. https://doi.org/10.1016/j.eswa.2021.115253

    Article  Google Scholar 

  84. Naraharisetti JNL, Devarapalli R, Bathina V (2020) Parameter extraction of solar photovoltaic module by using a novel hybrid marine predators – success history based adaptive differential evolution algorithm. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 1–23. https://doi.org/10.1080/15567036.2020.1806956

  85. Hai T, Zhou J, Masdari M, Marhoon HA (2023) A Hybrid Marine Predator Algorithm for Thermal-aware Routing Scheme in Wireless Body Area Networks. J Bionic Eng 20:81–104. https://doi.org/10.1007/s42235-022-00263-4

    Article  Google Scholar 

  86. Dehkordi AA, Etaati B, Neshat M, Mirjalili S (2023) Adaptive Chaotic Marine Predators Hill Climbing Algorithm for Large-Scale Design Optimizations. IEEE Access 11:39269–39294. https://doi.org/10.1109/ACCESS.2023.3266991

    Article  Google Scholar 

  87. 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. https://doi.org/10.1007/s11042-022-12001-3

    Article  Google Scholar 

  88. Kumar R (2023) Hybrid Marine Predators and Border Collie Optimization algorithm for multipath routing in IoT. Int J Communication 36:e5567. https://doi.org/10.1002/dac.5567

    Article  Google Scholar 

  89. Panagant N, Yıldız M, Pholdee N et al (2021) A novel hybrid marine predators-Nelder-Mead optimization algorithm for the optimal design of engineering problems. Materials Testing 63:453–457. https://doi.org/10.1515/mt-2020-0077

    Article  Google Scholar 

  90. Kusuma PD, Adiputra D (2023) Hybrid marine predator algorithm and hide object game optimization. Eng Lett 31:262–270

  91. Qin C, Han B (2022) A Novel Hybrid Quantum Particle Swarm Optimization With Marine Predators for Engineering Design Problems. IEEE Access 10:129322–129343. https://doi.org/10.1109/ACCESS.2022.3226813

    Article  MathSciNet  Google Scholar 

  92. Han B, Li B, Qin C (2023) A novel hybrid particle swarm optimization with marine predators. Swarm Evol Comput 83:101375. https://doi.org/10.1016/j.swevo.2023.101375

    Article  Google Scholar 

  93. Yousri D, Fathy A, Rezk H et al (2021) A reliable approach for modeling the photovoltaic system under partial shading conditions using three diode model and hybrid marine predators-slime mould algorithm. Energy Convers Manage 243:114269. https://doi.org/10.1016/j.enconman.2021.114269

    Article  Google Scholar 

  94. Gao Z, Zhuang Y, Chen C, Wang Q (2023) Hybrid modified marine predators algorithm with teaching-learning-based optimization for global optimization and abrupt motion tracking. Multimed Tools Appl 82:19793–19828. https://doi.org/10.1007/s11042-022-13819-7

    Article  Google Scholar 

  95. Balamurugan A, Janakiraman S, Priya MD, Malar ACJ (2022) Hybrid Marine predators optimization and improved particle swarm optimization-based optimal cluster routing in wireless sensor networks (WSNs). China Commun 19:219–247. https://doi.org/10.23919/JCC.2022.06.017

    Article  Google Scholar 

  96. Yousri D, Abd Elaziz M, Oliva D et al (2022) Fractional-order comprehensive learning marine predators algorithm for global optimization and feature selection. Knowl-Based Syst 235:107603. https://doi.org/10.1016/j.knosys.2021.107603

    Article  Google Scholar 

  97. Alrasheedi AF, Alnowibet KA, Saxena A et al (2022) Chaos Embed Marine Predator (CMPA) Algorithm for Feature Selection. Mathematics 10:1411. https://doi.org/10.3390/math10091411

    Article  Google Scholar 

  98. Yu G, Meng Z, Ma H, Liu L (2021) An adaptive Marine Predators Algorithm for optimizing a hybrid PV/DG/Battery System for a remote area in China. Energy Rep 7:398–412. https://doi.org/10.1016/j.egyr.2021.01.005

    Article  Google Scholar 

  99. Fan Q, Huang H, Chen Q et al (2022) A modified self-adaptive marine predators algorithm: framework and engineering applications. Eng Comput 38:3269–3294. https://doi.org/10.1007/s00366-021-01319-5

    Article  Google Scholar 

  100. Chen T, Chen Y, He Z et al (2023) A novel marine predators algorithm with adaptive update strategy. J Supercomput 79:6612–6645. https://doi.org/10.1007/s11227-022-04903-8

    Article  Google Scholar 

  101. Owoola EO, Xia K, Ogunjo S et al (2022) Advanced Marine Predator Algorithm for Circular Antenna Array Pattern Synthesis. Sensors 22:5779. https://doi.org/10.3390/s22155779

    Article  Google Scholar 

  102. Xing Z, He Y (2021) Many-objective multilevel thresholding image segmentation for infrared images of power equipment with boost marine predators algorithm. Appl Soft Comput 113:107905. https://doi.org/10.1016/j.asoc.2021.107905

    Article  Google Scholar 

  103. Yousri D, Fathy A, Rezk H (2021) A new comprehensive learning marine predator algorithm for extracting the optimal parameters of supercapacitor model. J Energy Storage 42:103035. https://doi.org/10.1016/j.est.2021.103035

    Article  Google Scholar 

  104. Zhong K, Zhou G, Deng W et al (2021) MOMPA: Multi-objective marine predator algorithm. Comput Methods Appl Mech Eng 385:114029. https://doi.org/10.1016/j.cma.2021.114029

    Article  MathSciNet  Google Scholar 

  105. Abd Elaziz M, Thanikanti SB, Ibrahim IA et al (2021) Enhanced Marine Predators Algorithm for identifying static and dynamic Photovoltaic models parameters. Energy Convers Manage 236:113971. https://doi.org/10.1016/j.enconman.2021.113971

    Article  Google Scholar 

  106. Hassan MH, Daqaq F, Selim A et al (2023) MOIMPA: multi-objective improved marine predators algorithm for solving multi-objective optimization problems. Soft Comput 27:15719–15740. https://doi.org/10.1007/s00500-023-08812-7

    Article  Google Scholar 

  107. Kumar S, Yildiz BS, Mehta P et al (2023) Chaotic marine predators algorithm for global optimization of real-world engineering problems. Knowl-Based Syst 261:110192. https://doi.org/10.1016/j.knosys.2022.110192

    Article  Google Scholar 

  108. Zhang C, He Z, Li Q et al (2023) An adaptive marine predator algorithm based optimization method for hood lightweight design. J Comput Des Eng 10:1219–1249. https://doi.org/10.1093/jcde/qwad047

    Article  Google Scholar 

  109. Mohd Tumari MZ, Ahmad MA, Suid MH et al (2023) An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system. J Low Frequen Noise, Vib Active Control 42:1666–1698. https://doi.org/10.1177/14613484231183938

    Article  Google Scholar 

  110. Mehmood K, Chaudhary NI, Cheema KM et al (2023) Design of Nonlinear Marine Predator Heuristics for Hammerstein Autoregressive Exogenous System Identification with Key-Term Separation. Mathematics 11:2512. https://doi.org/10.3390/math11112512

    Article  Google Scholar 

  111. Zhang J, Xu Y (2023) Training Feedforward Neural Networks Using an Enhanced Marine Predators Algorithm. Processes 11:924. https://doi.org/10.3390/pr11030924

    Article  Google Scholar 

  112. Chen D, Zhang Y (2023) Diversity-Aware Marine Predators Algorithm for Task Scheduling in Cloud Computing. Entropy 25:285. https://doi.org/10.3390/e25020285

    Article  Google Scholar 

  113. Chen L, Hao C, Ma Y (2022) A Multi-Disturbance Marine Predator Algorithm Based on Oppositional Learning and Compound Mutation. Electronics 11:4087. https://doi.org/10.3390/electronics11244087

    Article  Google Scholar 

  114. Aydemir SB (2023) Enhanced marine predator algorithm for global optimization and engineering design problems. Adv Eng Softw 184:103517. https://doi.org/10.1016/j.advengsoft.2023.103517

    Article  Google Scholar 

  115. Liu J, Li L, Liu Y (2024) Enhanced marine predators algorithm optimized support vector machine for IGBT switching power loss estimation. Meas Sci Technol 35:015035. https://doi.org/10.1088/1361-6501/ad042b

    Article  MathSciNet  Google Scholar 

  116. Alatas B, Akin E, Ozer AB (2009) Chaos embedded particle swarm optimization algorithms. Chaos, Solitons Fractals 40:1715–1734. https://doi.org/10.1016/j.chaos.2007.09.063

    Article  MathSciNet  Google Scholar 

  117. Li Y, Deng S, Xiao D (2011) A novel Hash algorithm construction based on chaotic neural network. Neural Comput Applic 20:133–141. https://doi.org/10.1007/s00521-010-0432-2

    Article  Google Scholar 

  118. Fister I, Perc M, Kamal SM, Fister I (2015) A review of chaos-based firefly algorithms: Perspectives and research challenges. Appl Math Comput 252:155–165. https://doi.org/10.1016/j.amc.2014.12.006

    Article  MathSciNet  Google Scholar 

  119. Tavazoei MS, Haeri M (2007) Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms. Appl Math Comput 187:1076–1085. https://doi.org/10.1016/j.amc.2006.09.087

    Article  MathSciNet  Google Scholar 

  120. Gandomi AH, Yang X-S, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18:89–98. https://doi.org/10.1016/j.cnsns.2012.06.009

    Article  MathSciNet  Google Scholar 

  121. Yuan X, Zhao J, Yang Y, Wang Y (2014) Hybrid parallel chaos optimization algorithm with harmony search algorithm. Appl Soft Comput 17:12–22. https://doi.org/10.1016/j.asoc.2013.12.016

    Article  Google Scholar 

  122. Coelho LDS, Mariani VC (2009) A novel chaotic particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch. Chaos, Solitons Fractals 39:510–518. https://doi.org/10.1016/j.chaos.2007.01.093

    Article  Google Scholar 

  123. Pei Y (2015) From Determinism and Probability to Chaos: Chaotic Evolution towards Philosophy and Methodology of Chaotic Optimization. Scie World J 2015:1–14. https://doi.org/10.1155/2015/704587

    Article  Google Scholar 

  124. Yan T, Liu F, Chen B (2017) New Particle Swarm Optimisation Algorithm with Hénon Chaotic Map Structure. Chin J Electron 26:747–753. https://doi.org/10.1049/cje.2017.06.006

    Article  Google Scholar 

  125. Chen Y, Xie S, Zhang J (2022) A Hybrid Domain Image Encryption Algorithm Based on Improved Henon Map. Entropy 24:287. https://doi.org/10.3390/e24020287

    Article  MathSciNet  Google Scholar 

  126. Lazzús JA, Rivera M, López-Caraballo CH (2016) Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm. Phys Lett A 380:1164–1171. https://doi.org/10.1016/j.physleta.2016.01.040

    Article  MathSciNet  Google Scholar 

  127. Gu D-K, Zhang D-W, Liu Y-D (2020) Robust Parametric Control of Lorenz System via State Feedback. Complexity 2020:1–10. https://doi.org/10.1155/2020/6548142

    Article  Google Scholar 

  128. González-Zapata AM, Tlelo-Cuautle E, Ovilla-Martinez B et al (2022) Optimizing Echo State Networks for Enhancing Large Prediction Horizons of Chaotic Time Series. Mathematics 10:3886. https://doi.org/10.3390/math10203886

    Article  Google Scholar 

  129. Kumar K (2023) Data-driven modeling and parameter estimation of nonlinear systems. Eur Phys J B 96:107. https://doi.org/10.1140/epjb/s10051-023-00574-3

    Article  Google Scholar 

  130. Weiel M, Götz M, Klein A et al (2021) Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions. Nat Mach Intell 3:727–734. https://doi.org/10.1038/s42256-021-00366-3

    Article  Google Scholar 

  131. Mwitia SM, Segera DR (2022) An Aggressive Cuckoo Search Algorithm for Optimum Power Allocation in a CDMA-Based Cellular Network. Scientific World Journal 2022:1–30. https://doi.org/10.1155/2022/5443160

    Article  Google Scholar 

  132. Yousri D, Abd Elaziz M, Abualigah L et al (2021) COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions. Appl Soft Comput 101:107052. https://doi.org/10.1016/j.asoc.2020.107052

    Article  Google Scholar 

  133. Abd Elaziz M, Yousri D (2021) Automatic selection of heavy-tailed distributions-based synergy Henry gas solubility and Harris hawk optimizer for feature selection: case study drug design and discovery. Artif Intell Rev 54:4685–4730. https://doi.org/10.1007/s10462-021-10009-z

    Article  Google Scholar 

  134. Luo Y, Yu J, Lai W, Liu L (2019) A novel chaotic image encryption algorithm based on improved baker map and logistic map. Multimed Tools Appl 78:22023–22043. https://doi.org/10.1007/s11042-019-7453-3

    Article  Google Scholar 

  135. Pan S, Wei J, Hu S (2020) A Novel Image Encryption Algorithm Based on Hybrid Chaotic Mapping and Intelligent Learning in Financial Security System. Multimed Tools Appl 79:9163–9176. https://doi.org/10.1007/s11042-018-7144-5

    Article  Google Scholar 

  136. Caponetto R, Fortuna L, Fazzino S, Xibilia MG (2003) Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Trans Evol Computat 7:289–304. https://doi.org/10.1109/TEVC.2003.810069

    Article  Google Scholar 

  137. Demir FB, Tuncer T, Kocamaz AF (2020) A chaotic optimization method based on logistic-sine map for numerical function optimization. Neural Comput & Applic 32:14227–14239. https://doi.org/10.1007/s00521-020-04815-9

    Article  Google Scholar 

  138. Özbay FA (2023) A modified seahorse optimization algorithm based on chaotic maps for solving global optimization and engineering problems. Eng Sci Technol, Int J 41:101408. https://doi.org/10.1016/j.jestch.2023.101408

    Article  Google Scholar 

  139. Lu H, Wang X, Fei Z, Qiu M (2014) The Effects of Using Chaotic Map on Improving the Performance of Multiobjective Evolutionary Algorithms. Math Probl Eng 2014:1–16. https://doi.org/10.1155/2014/924652

    Article  MathSciNet  Google Scholar 

  140. Pourmousa N, Ebrahimi SM, Malekzadeh M, Alizadeh M (2019) Parameter estimation of photovoltaic cells using improved Lozi map based chaotic optimization Algorithm. Sol Energy 180:180–191. https://doi.org/10.1016/j.solener.2019.01.026

    Article  Google Scholar 

  141. Yu H, Zhao N, Wang P et al (2020) Chaos-enhanced synchronized bat optimizer. Appl Math Model 77:1201–1215. https://doi.org/10.1016/j.apm.2019.09.029

    Article  Google Scholar 

  142. Elaziz MA, Mirjalili S (2019) A hyper-heuristic for improving the initial population of whale optimization algorithm. Knowl-Based Syst 172:42–63. https://doi.org/10.1016/j.knosys.2019.02.010

    Article  Google Scholar 

  143. Sun Y, Gao Y, Shi X (2019) Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity. Mathematics 7:146. https://doi.org/10.3390/math7020146

    Article  Google Scholar 

  144. Sayed GI, Hassanien AE, Azar AT (2019) Feature selection via a novel chaotic crow search algorithm. Neural Comput & Applic 31:171–188. https://doi.org/10.1007/s00521-017-2988-6

    Article  Google Scholar 

  145. Arora S, Anand P (2019) Chaotic grasshopper optimization algorithm for global optimization. Neural Comput & Applic 31:4385–4405. https://doi.org/10.1007/s00521-018-3343-2

    Article  Google Scholar 

  146. Varol Altay E, Alatas B (2020) Bird swarm algorithms with chaotic mapping. Artif Intell Rev 53:1373–1414. https://doi.org/10.1007/s10462-019-09704-9

    Article  Google Scholar 

  147. Naik A (2023) Marine predators social group optimization: a hybrid approach. Evol Intel. https://doi.org/10.1007/s12065-023-00891-7

    Article  Google Scholar 

  148. Rao H, Jia H, Wu D et al (2022) A Modified Group Teaching Optimization Algorithm for Solving Constrained Engineering Optimization Problems. Mathematics 10:3765. https://doi.org/10.3390/math10203765

    Article  Google Scholar 

  149. Omran MGH, Iacca G (2022) An improved Jaya optimization algorithm with ring topology and population size reduction. J Intell Syst 31:1178–1210. https://doi.org/10.1515/jisys-2022-0200

    Article  Google Scholar 

  150. Sandgren E (1990) Nonlinear Integer and Discrete Programming in Mechanical Design Optimization. J Mech Des 112:223–229. https://doi.org/10.1115/1.2912596

    Article  Google Scholar 

  151. Bayzidi H, Talatahari S, Saraee M, Lamarche C-P (2021) Social Network Search for Solving Engineering Optimization Problems. Comput Intell Neurosci 2021:1–32. https://doi.org/10.1155/2021/8548639

    Article  Google Scholar 

  152. Coello Coello CA (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41:113–127. https://doi.org/10.1016/S0166-3615(99)00046-9

    Article  Google Scholar 

  153. Dong C, Xiong Z, Liu X et al (2019) Dual-Search Artificial Bee Colony Algorithm for Engineering Optimization. IEEE Access 7:24571–24584. https://doi.org/10.1109/ACCESS.2019.2899743

    Article  Google Scholar 

  154. Han X, Xu Q, Yue L et al (2020) An Improved Crow Search Algorithm Based on Spiral Search Mechanism for Solving Numerical and Engineering Optimization Problems. IEEE Access 8:92363–92382. https://doi.org/10.1109/ACCESS.2020.2980300

    Article  Google Scholar 

  155. Qiu Z, Qiao Y (2023) A Hybrid Moth Flame Optimization and Golden Jackal Optimization Algorithm Based Opposition for Global Optimization Problems. IEEE Access 11:129576–129600. https://doi.org/10.1109/ACCESS.2023.3332902

    Article  Google Scholar 

  156. Azizi M, Talatahari S, Giaralis A (2021) Optimization of Engineering Design Problems Using Atomic Orbital Search Algorithm. IEEE Access 9:102497–102519. https://doi.org/10.1109/ACCESS.2021.3096726

    Article  Google Scholar 

  157. Guha R, Ghosh S, Ghosh KK et al (2022) Groundwater Flow Algorithm: A Novel Hydro-Geology Based Optimization Algorithm. IEEE Access 10:132193–132211. https://doi.org/10.1109/ACCESS.2022.3222489

    Article  Google Scholar 

  158. Zitouni F, Harous S, Maamri R (2021) The Solar System Algorithm: A Novel Metaheuristic Method for Global Optimization. IEEE Access 9:4542–4565. https://doi.org/10.1109/ACCESS.2020.3047912

    Article  Google Scholar 

  159. Yan F, Xu X, Xu J (2020) Grey Wolf Optimizer With a Novel Weighted Distance for Global Optimization. IEEE Access 8:120173–120197. https://doi.org/10.1109/ACCESS.2020.3005182

    Article  Google Scholar 

  160. Zhao J, Zhang B, Guo X et al (2022) Self-Adapting Spherical Search Algorithm with Differential Evolution for Global Optimization. Mathematics 10:4519. https://doi.org/10.3390/math10234519

    Article  Google Scholar 

  161. Hijjawi M, Alshinwan M, Khashan OA et al (2023) A Novel Hybrid Prairie Dog Algorithm and Harris Hawks Algorithm for Resource Allocation of Wireless Networks. IEEE Access 11:145146–145166. https://doi.org/10.1109/ACCESS.2023.3335247

    Article  Google Scholar 

  162. Guo MW, Wang JS, Zhu LF et al (2020) An Improved Grey Wolf Optimizer Based on Tracking and Seeking Modes to Solve Function Optimization Problems. IEEE Access 8:69861–69893. https://doi.org/10.1109/ACCESS.2020.2984321

    Article  Google Scholar 

  163. Yang Y, Gao Y, Tan S et al (2022) An opposition learning and spiral modelling based arithmetic optimization algorithm for global continuous optimization problems. Eng Appl Artif Intell 113:104981. https://doi.org/10.1016/j.engappai.2022.104981

    Article  Google Scholar 

  164. Sayed GI, Darwish A, Hassanien AE (2018) A new chaotic multi-verse optimization algorithm for solving engineering optimization problems. J Exp Theor Artif Intell 30:293–317. https://doi.org/10.1080/0952813X.2018.1430858

    Article  Google Scholar 

  165. Liu J, Chen Y, Liu X et al (2024) An efficient manta ray foraging optimization algorithm with individual information interaction and fractional derivative mutation for solving complex function extremum and engineering design problems. Appl Soft Comput 150:111042. https://doi.org/10.1016/j.asoc.2023.111042

    Article  Google Scholar 

  166. Yıldız BS, Kumar S, Panagant N et al (2023) A novel hybrid arithmetic optimization algorithm for solving constrained optimization problems. Knowl-Based Syst 271:110554. https://doi.org/10.1016/j.knosys.2023.110554

    Article  Google Scholar 

  167. Dalirinia E, Jalali M, Yaghoobi M, Tabatabaee H (2023) Lotus effect optimization algorithm (LEA): a lotus nature-inspired algorithm for engineering design optimization. J Supercomput. https://doi.org/10.1007/s11227-023-05513-8

    Article  Google Scholar 

  168. Qais MH, Hasanien HM, Alghuwainem S, Loo KH (2023) Propagation Search Algorithm: A Physics-Based Optimizer for Engineering Applications. Mathematics 11:4224. https://doi.org/10.3390/math11204224

    Article  Google Scholar 

  169. Trojovska E, Dehghani M, Trojovsky P (2022) Zebra Optimization Algorithm: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm. IEEE Access 10:49445–49473. https://doi.org/10.1109/ACCESS.2022.3172789

    Article  Google Scholar 

Download references

Funding

No funding was received for conducting this research.

Author information

Authors and Affiliations

  1. Electrical and Electronics Engineering Department, Nile University of Nigeria, Abuja, Nigeria

    Usman Mohammed, Tologon Karataev, Omotayo Oshiga, Oghenewvogaga Oghorada, Suleiman U. Hussein, Ahmadu Galadima & Jaafaru Sanusi

  2. Computer Engineering Department, Nile University of Nigeria, Abuja, Nigeria

    Steve A. Adeshina

  3. Civil Engineering Department, Nile University of Nigeria, Abuja, Nigeria

    Dayyabu Abubakar

  4. Computer Science Department, Al Al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  5. Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, 71491, Tabuk, Saudi Arabia

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

    Laith Abualigah

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

    Laith Abualigah

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

    Laith Abualigah

Authors
  1. Usman Mohammed
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Tologon Karataev
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Omotayo Oshiga
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Oghenewvogaga Oghorada
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Steve A. Adeshina
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Suleiman U. Hussein
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Laith Abualigah
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Ahmadu Galadima
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Dayyabu Abubakar
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Jaafaru Sanusi
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Conceptualisation: U.M., T.K., O. O., and O. O.; Methodology: U.M.; Software: U.M., A.G., A.D., J.S., and L.A.; Validation: S.A.A., S.U.H., A.G., and L.A.; Formal analysis and investigation: A.G., A.D., U.M., T.K., O.O., and O.O.; Resources: U.M.; Writing-original draft preparation: U.M.; Writing-review and editing: T.K., O.O., S.A.A., S.U.H., J.S., and L.A.; Supervision, T.K., O.O., and O.O. Jaafaru Sanusi, and Laith Abualigah; Supervision: Tologon Karataev, Omotayo Oshiga, and Oghenewvogaga Oghorada All authors reviewed the manuscript.

Corresponding author

Correspondence to Usman Mohammed.

Ethics declarations

Ethical approval

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

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

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

Appendices

Appendix 1

Tables 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 and 50

Table 35 CEC2014 description
Full size table
Table 36 CEC2017 description
Full size table
Table 37 CEC2020 description
Full size table
Table 38 CEC2022 description
Full size table
Table 39 CEC2014 results (10 dimensions)
Full size table
Table 40 Wilcoxon test on CEC2014 (10 dimensions)
Full size table
Table 41 Friedman test on CEC2014 (10 dimensions)
Full size table
Table 42 Bonferroni-Holm test on CEC2014 (10 dimensions)
Full size table
Table 43 CEC2014 results (30 dimensions)
Full size table
Table 44 Wilcoxon test on CEC2014 (30 dimensions)
Full size table
Table 45 Friedman test on CEC2014 (30 dimensions)
Full size table
Table 46 Bonferroni-Holm test on CEC2014 (30 dimensions)
Full size table
Table 47 CEC2017 results (30 dimensions)
Full size table
Table 48 Wilcoxon test on CEC2017 (30 dimensions)
Full size table
Table 49 Friedman test on CEC2017 (30 dimensions)
Full size table
Table 50 Bonferroni-Holm test on CEC2017 (30 dimensions)
Full size table

Appendix 2

Figures 18

Fig. 18
figure 18figure 18figure 18

Convergence curves CEC2014 (10 dimensions)

Full size image
Fig. 19
figure 19figure 19

Convergence curves CEC2014 (30 dimensions)

Full size image
Fig. 20
figure 20figure 20

Convergence curves CEC2017 (30 dimensions)

Full size image

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

Mohammed, U., Karataev, T., Oshiga, O. et al. ICSOMPA: A novel improved hybrid algorithm for global optimisation. Evol. Intel. 17, 3337–3440 (2024). https://doi.org/10.1007/s12065-024-00937-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-024-00937-4

Keywords