Skip to main content
SpringerLink
Log in

Opposition-based learning multi-verse optimizer with disruption operator for optimization problems

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

Abstract

Multi-verse optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step is using opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.

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

Similar content being viewed by others

Data Availability

Enquiries about data availability should be directed to the authors.

References

  • Abdelmadjid C, Mohamed S-A, Boussad B (2013) Cfd analysis of the volute geometry effect on the turbulent air flow through the turbocharger compressor. Energy Procedia 36(2):746–755

    Article  Google Scholar 

  • Abedinpourshotorban H, Shamsuddin SM, Beheshti Z, Jawawi DN (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol Comput 26(1):8–22

    Article  Google Scholar 

  • Abualigah L, Elaziz MA, Hussien AG, Alsalibi B, Jalali SM, Gandomi AH (2020) Lightning search algorithm: a comprehensive survey. Appl Intell 1–24

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

    Article  MathSciNet  MATH  Google Scholar 

  • Abualigah L, Gandomi AH, Elaziz MA, Hussien AG, Khasawneh AM, Alshinwan M, Houssein EH (2020) Nature inspired optimization algorithms for text document clustering a comprehensive analysis. Algorithms 13(12):345

    Article  MathSciNet  Google Scholar 

  • Abualigah L, Shehab M, Alshinwan M, Alabool H (2019) Salp swarm algorithm: a comprehensive survey. Neural Comput Appl 6(2):1–21

    Google Scholar 

  • Laith Abualigah, Dalia Yousri, Abd Elaziz Mohamed, Ewees Ahmed A, Al-qaness MA, Gandomi Amir H (2021) A novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250

    Article  Google Scholar 

  • Abualigah LM, Sawaie AM, Khader AT, Rashaideh H, Al-Betar MA, Shehab M (2017) \(\beta \)-hill climbing technique for the text document clustering. New Trends in Information Technology (NTIT)–2017 6(2):60

  • Adeec UCEH (2000) Time complexity of genetic algorithms on exponentially scaled problems. Urbana 51(4):61–801

    Google Scholar 

  • Adhim Khalid K, Hudaib Amjad, Al-Shboul Bashar (2019) Efficient requirement prioritization based on enhanced multi-verse optimizer. J Theor Appl Inf Technol 97(19)

  • Altabeeb AM, Mohsen AM, Abualigah L, Ghallab A (2021) Solving capacitated vehicle routing problem using cooperative firefly algorithm. Appl Soft Comput 108:107403

    Article  Google Scholar 

  • Andersson M, Bandaru S, Ng Amos HC, Syberfeldt A (2015) Parameter tuned cma-es on the cec’15 expensive problems. In: 2015 IEEE congress on evolutionary computation (CEC), pp 1950–1957. IEEE

  • Assiri AS, Hussien AG, Amin M (2020) Ant lion optimization: variants, hybrids, and applications. IEEE Access 8:77746–77764

    Article  Google Scholar 

  • Aydilek IB (2018) A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Appl Soft Comput 66(2):232–249

    Article  MathSciNet  Google Scholar 

  • Bai Q (2010) Analysis of particle swarm optimization algorithm. Comput Inf Sci 3(1):180

    Google Scholar 

  • Ceylan H, Ceylan H (2009) Harmony search algorithm for transport energy demand modeling. In: Music-inspired harmony search algorithm. Springer, pp 163–172

  • Dhiman R (2018) Moth-flame optimization technique for optimal coordination of directional overcurrent relay system. PhD thesis

  • Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Article  Google Scholar 

  • Elaziz MA, Selim IM, Xiong S (2017) Automatic detection of galaxy type from datasets of galaxies image based on image retrieval approach. Sci Rep 7(1):4463

    Article  Google Scholar 

  • Feng Y, Wang G-G, Dong J, Wang L (2018) Opposition-based learning monarch butterfly optimization with gaussian perturbation for large-scale 0–1 knapsack problem. Comput Electr Eng 67:454–468

    Article  Google Scholar 

  • Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  • Geng K, Ye C, Cao L, Liu L (2019) Multi-objective reentrant hybrid flowshop scheduling with machines turning on and off control strategy using improved multi-verse optimizer algorithm. Math Probl Eng 27(2)

  • Glover F (1977) Heuristics for integer programming using surrogate constraints. Decis Sci 8(1):156–166

    Article  Google Scholar 

  • Guo W, Lim CJ, Bi X, Sokhansanj S, Melin S (2013) Determination of effective thermal conductivity and specific heat capacity of wood pellets. Fuel 103:347–355

    Article  Google Scholar 

  • Hassan MH, Kamel S, Abualigah L, Eid A (2021) Development and application of slime mould algorithm for optimal economic emission dispatch. Expert Syst Appl 182:115205

    Article  Google Scholar 

  • Holland J (1975) Adaptation in natural and artificial systems: an introductory analysis with application to biology. Control Artif Intell 3(1):1–15

    Google Scholar 

  • Hu C, Zhi L, Tian Z, Aijun Z, Chuanpei X (2016) A multi-verse optimizer with levy flights for numerical optimization and its application in test scheduling for network-on-chip. PLoS ONE 11(12):e0167341

    Article  Google Scholar 

  • Hussien AG (2021) An enhanced opposition-based salp swarm algorithm for global optimization and engineering problems. J Ambient Intell Humaniz Comput 1–22

  • Hussien AG, Amin M, Wang M, Liang G, Alsanad A, Gumaei A, Chen H (2020) Crow search algorithm: theory, recent advances, and applications. IEEE Access 8:173548–173565

    Article  Google Scholar 

  • Hussien AG, Oliva D, Houssein EH, AngelA Juan XY (2020) Binary whale optimization algorithm for dimensionality reduction. Mathematics 8(10):1821

    Article  Google Scholar 

  • Jain P, Saxena A (2019) An opposition theory enabled moth flame optimizer for strategic bidding in uniform spot energy market. Eng Sci Technol Int J 22(4):1047–1067

    Google Scholar 

  • Jangir P, Parmar SA, Trivedi IN, Bhesdadiya RH (2017) A novel hybrid particle swarm optimizer with multi verse optimizer for global numerical optimization and optimal reactive power dispatch problem. Eng Sci Technol Int J 20(2):570–586

    Google Scholar 

  • Kennedy J (2010) Particle swarm optimization. Encycl Mach Learn 12(5):760–766

    Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization (pso). In: Proc. IEEE international conference on neural networks, Perth, Australia, pp 1942–1948

  • Koziel S, Yang X-S (2011) Computational optimization, methods and algorithms, vol 356. Springer, Berlin

    Book  MATH  Google Scholar 

  • Kulturel-Konak S, Smith AE, Coit DW (2003) Efficiently solving the redundancy allocation problem using tabu search. IIE Trans 35(6):515–526

    Article  Google Scholar 

  • Li J, Li YX, Tian SS, Xia JL (2019) An improved cuckoo search algorithm with self-adaptive knowledge learning. Neural Comput Appl 1–31

  • Li J, Li Y, Tian S, Zou J (2019) Dynamic cuckoo search algorithm based on Taguchi opposition-based search. Int J Bio Inspir Comput 13(1):59–69

    Article  Google Scholar 

  • Li J, Xiao D, Lei H, Zhang T, Tian T (2020) Using cuckoo search algorithm with q-learning and genetic operation to solve the problem of logistics distribution center location. Mathematics 8(2):149

    Article  Google Scholar 

  • Li J, Xiao D, Zhang T, Liu C, Li Y, Wang G (2021) Multi-swarm cuckoo search algorithm with q-learning model. Comput J 64(1):108–131

    Article  MathSciNet  Google Scholar 

  • Li J, Yang Y-H, Lei H, Wang G-G (2020) Solving logistics distribution center location with improved cuckoo search algorithm. Int J Comput Intell Syst 14(1):676–692

    Article  Google Scholar 

  • Li W, Wang GG (2021) Elephant herding optimization using dynamic topology and biogeography-based optimization based on learning for numerical optimization. Eng Comput 1–29

  • Li Z, Zhou Y, Zhang S, Song J (2016) Lévy-flight moth-flame algorithm for function optimization and engineering design problems. Math Probl Eng 16:25–47

    Google Scholar 

  • Liu H, Ding G, Wang B (2014) Bare-bones particle swarm optimization with disruption operator. Appl Math Comput 238:106–122

    MathSciNet  MATH  Google Scholar 

  • Liu Y, Wang G, Chen H, Dong H, Zhu X, Wang S (2011) An improved particle swarm optimization for feature selection. J Bionic Eng 8(2):191–200

    Article  Google Scholar 

  • Milad A (2013) Harmony search algorithm: strengths and weaknesses. J Comput Eng Inf Technol 2(1):1–7

    Google Scholar 

  • Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89(2):228–249

    Article  Google Scholar 

  • Mirjalili S, Jangir P, Mirjalili SZ, Saremi S, Trivedi IN (2017) Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowl Based Syst 134:50–71

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Article  Google Scholar 

  • Mohammad Abualigah L, Al-diabat M, Al Shinwan M, Dhou K, Alsalibi B, Said Hanandeh E, Shehab M (2020) Hybrid harmony search algorithm to solve the feature selection for data mining applications. Recent Adv Hybrid Metaheuristics Data Cluster 19–37

  • Murata T, Ishibuchi H, Tanaka H (1996) Multi-objective genetic algorithm and its applications to flowshop scheduling. Comput Ind Eng 30(4):957–968

    Article  Google Scholar 

  • Ngo TT, Sadollah A, Kim JH (2016) A cooperative particle swarm optimizer with stochastic movements for computationally expensive numerical optimization problems. J Comput Sci 13(1):68–82

    Article  MathSciNet  Google Scholar 

  • Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57

    Article  Google Scholar 

  • Rahnamayan S, Tizhoosh HR, Salama MMA (2008) Opposition versus randomness in soft computing techniques. Appl Soft Comput 8(2):906–918

    Article  Google Scholar 

  • Reeves CR (1993) Improving the efficiency of tabu search for machine sequencing problems. J Oper Res Soc 44(4):375–382

    Article  MATH  Google Scholar 

  • Şahin CB, Abualigah L (2021) A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection. Neural Comput Appl 1–19,

  • Salgotra R, Singh U, Saha S (2018) New cuckoo search algorithms with enhanced exploration and exploitation properties. Expert Syst Appl 95(7):384–420

    Article  Google Scholar 

  • Shehab M, Khader AT, Laouchedi M (2018) A hybrid method based on cuckoo search algorithm for global optimization problems. J Inf Commun Technol 17(3):469–491

    Google Scholar 

  • Shehab M, Abualigah L, AlHamad H, Alabool H, Alshinwan M, Khasawneh AM (2019) Moth-flame optimization algorithm: variants and applications. Neural Comput Appl 15(2):1–26

    Google Scholar 

  • Shehab M, Alshawabkah H, Abualigah L, Nagham AL-M (2020) Enhanced a hybrid moth-flame optimization algorithm using new selection schemes. Eng Comput 22(3):1–26

    Google Scholar 

  • Shehab M, Daoud MS, AlMimi HM, Abualigah LM, Khader AT (2019) Hybridising cuckoo search algorithm for extracting the odf maxima in spherical harmonic representation. Int J Bio Inspired Comput 14(3):190–199

    Article  Google Scholar 

  • Shehab M, Khader AT, Al-Betar M (2016) New selection schemes for particle swarm optimization. IEEJ Trans Electron Inf Syst 136(12):1706–1711

    Google Scholar 

  • Shehab M, Khader AT, Al-Betar MA (2017) A survey on applications and variants of the cuckoo search algorithm. Appl Soft Comput 61(1):1041–1059

    Article  Google Scholar 

  • Shehab M, Khader AT, Al-Betar MA, Abualigah LM (2017) Hybridizing cuckoo search algorithm with hill climbing for numerical optimization problems. In: 2017 8th international conference on information technology (ICIT), pp 36–43. IEEE

  • Shehab M, Khader AT, Alia MA (2019) Enhancing cuckoo search algorithm by using reinforcement learning for constrained engineering optimization problems. In: 2019 IEEE Jordan international joint conference on electrical engineering and information technology (JEEIT)

  • Shehab M, Khader AT, Laouchedi M (2017) Modified cuckoo search algorithm for solving global optimization problems. In: International conference of reliable information and communication technology, no 2. Springer, pp 561–570

  • Tanweer MR, Suresh S, Sundararajan N (2015) Improved srpso algorithm for solving cec 2015 computationally expensive numerical optimization problems. In: 2015 IEEE congress on evolutionary computation (CEC), pp 1943–1949. IEEE

  • Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06), vol 1, pp 695–701. IEEE

  • Topal AO, Altun O (2016) A novel meta-heuristic algorithm: dynamic virtual bats algorithm. Inf Sci 354(5):222–235

    Article  Google Scholar 

  • Wang G-G (2018) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Comput 10(2):151–164

  • Wang G-G, Deb S, Gandomi AH, Alavi AH (2016) Opposition-based krill herd algorithm with cauchy mutation and position clamping. Neurocomputing 177:147–157

  • Wang L, Yang R, Yin X, Niu Q, Pardalos PM, Fei M (2013) An improved adaptive binary harmony search algorithm. Inf Sci 232(4):58–87

    Article  MathSciNet  Google Scholar 

  • Wang X, Yang J, Teng X, Xia W, Jensen R (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recogn Lett 28(4):459–471

    Article  Google Scholar 

  • Wright AH (1991) Genetic algorithms for real parameter optimization. In: Foundations of genetic algorithms, vol 1. Elsevier, pp 205–218

  • Yang XS, Deb S (2009) Cuckoo search via lévy flights. In: 2009 World congress on nature & biologically inspired computing (NaBIC), pp 210–214. IEEE

  • Yazdani S, Nezamabadi-pour H, Kamyab S (2014) A gravitational search algorithm for multimodal optimization. Swarm Evol Comput 14:1–14

    Article  Google Scholar 

  • Zhang H, Sun G (2002) Feature selection using tabu search method. Pattern Recogn 35(3):701–711

    Article  MATH  Google Scholar 

  • Zingg DW, Nemec M, Pulliam TH (2008) A comparative evaluation of genetic and gradient-abased algorithms applied to aerodynamic optimization. European Journal of Computational Mechanics/Revue Européenne de Mécanique Numérique 17(1–2):103–126

Download references

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

  1. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan

    Mohammad Shehab

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

    Laith Abualigah

  3. Faculty of Information Technology, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

Authors
  1. Mohammad Shehab
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laith Abualigah
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors contributed to each part of this paper equally. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Mohammad Shehab.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts 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

Shehab, M., Abualigah, L. Opposition-based learning multi-verse optimizer with disruption operator for optimization problems. Soft Comput 26, 11669–11693 (2022). https://doi.org/10.1007/s00500-022-07470-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-022-07470-5

Keywords

Search

Navigation