The Journal of Supercomputing

, Volume 72, Issue 10, pp 3764–3786 | Cite as

Recent advances in metaheuristic algorithms: Does the Makara dragon exist?

  • Simon FongEmail author
  • Xi Wang
  • Qiwen Xu
  • Raymond Wong
  • Jinan Fiaidhi
  • Sabah Mohammed


Metaheuristic algorithms (MHs) have a long history that can be traced back to genetic algorithms and evolutionary computing in the 1950s. Since February 2008, with the birth of the Firefly algorithm, MHs started to receive attention from researchers around the globe. Variants and new species of MH algorithms have bloomed like sprouts after rain. However, the necessity for creating more new species of such algorithms is questionable. It can be observed that these algorithms are fundamentally made up of several widely used core components. By explaining these components, the underlying design for a collection of the so-called modern MH optimisation algorithms is revealed. In this paper, the core components in some of the more popular MH algorithms are reviewed, thereby debunking the myths of their novelty, and perhaps dampening claims that something really ‘new’ is invented simply by branding an MH search method with the name of another living creature. Counterintuitive experimentations have shown that by taking snapshots, anyone can show some improvements of an MH over another in some situation. Mixing certain components up indeed adds advantage over the original MH. The same goes to extending MH with slight functional modification. This work also serves as a general guideline and a reference for any algorithm architect who wants to create a new MH algorithm in the future.


Metaheuristics Search methods Swarm intelligence Algorithm design 



Artificial bee colony algorithm


Ant colony optimization


Artificial immune system algorithm


Bacterial foraging algorithm


Cuckoo search algorithm


Differential evolution


Firefly algorithm


Flower pollination algorithm


Genetic algorithm


Gravitational search algorithm


Golden section search


Hooke Jeeves algorithm


Harmony search algorithm


Intelligent water drops


Local search algorithm


Monkey optimization


Memetic search algorithm


Optimal Golomb rulers


Quantum evolutionary algorithm


River formation dynamics


Simulated annealing


Shuffled frog feaping algorithm


Seeker optimization algorithm


Tree search algorithm


Tabu search algorithm



The authors are thankful for the financial supports by the Macao Science and Technology Development Fund under the EAE project (No.072/2009/A3), and MYRG2015-00128-FST, by the University of Macau and the Macau SAR government.


  1. 1.
    Maranville S (1992) Entrepreneurship in the business curriculum. J Educ Bus 68(1):27–31CrossRefGoogle Scholar
  2. 2.
    Gao X-Z (2014) Hybrid nature-inspired computing (NIC) methods: motivation and prospection. Int J Swarm Intel Evol Comput 3(1):1–2Google Scholar
  3. 3.
    Sorensen K (2015) Metaheuristics—the metaphor exposed. Int Trans Oper Res 22:3–18MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Christian B, Andrea R (2008) Michael Sampels, Hybrid Metaheuristics: an emerging approach to optimization, studies in computational intelligence 114. ISDN 978-3-540-78294-0Google Scholar
  5. 5.
    Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67CrossRefGoogle Scholar
  6. 6.
    Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35(3):268–308CrossRefGoogle Scholar
  7. 7.
    Blum Christian, Puchinger Jakob, Raidl Günther R, Roli Andrea (2011) Hybrid metaheuristics in combinatorial optimization: A survey. Appl Soft Comput 11(6):4135–4151CrossRefzbMATHGoogle Scholar
  8. 8.
    Cotta C, Talbi EG, Alba E (2005) Parallel Hybrid Metaheuristics. In: Alba E (ed) Parallel Metaheuristics. Wiley-Interscience, Hoboken, pp 347–370CrossRefGoogle Scholar
  9. 9.
    Gutjahr Walter J (2002) ACO Algorithms with guaranteed convergence to the optimal solution. Inf Process Lett 82(3):145–153MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Vassiliadis V, Thomaidis N, Dounias G (2009) Active portfolio management under a downside risk framework, HAIS (2009), LNAI 5572, pp 702–712Google Scholar
  11. 11.
    Chaparro I, Valdez F (2013) Variants of ant colony optimization: a metaheuristic for solving the traveling salesman problem. In: Castillo O, Melin P, Kacprzyk J (eds) Recent advances on hybrid intelligent systems. Studies in computational intelligence, vol 451. Springer, Berlin, pp 323–331Google Scholar
  12. 12.
    Gao Wei-feng, Liu San-yang (2012) A modifiedartificialbeecolonyalgorithm. Comput Oper Res Elsevier 39:687–697CrossRefGoogle Scholar
  13. 13.
    Tuba M, Jovanovic R (2013) Improved ACO algorithm with pheromone correction strategy for the traveling salesman problems. Int J Comput Commun 8(3):477–485 ISSN 1841-9836MathSciNetCrossRefGoogle Scholar
  14. 14.
    Consoli P, Alessio C, Mario P (2013) Swarm Intelligence Heuristics for Graph Coloring Problem. IEEE Congress on Evolutionary Computation, June 20–23. Cancún, México, pp 1909–1916Google Scholar
  15. 15.
    Zukhri Zainudin, Paputungan Irving Vitra (2013) A hybrid optimization algorithm based on genetic algorithm and ant colony optimization. Int J Artif Intel Appl (IJAIA) 4(5):63–75Google Scholar
  16. 16.
    Rabanal Pablo, Rodríguez Ismael, Rubio Fernando (2013) An ACO-RFD hybrid method to solve NP-complete problems. Front Comput Sci 7(5):729–744MathSciNetCrossRefGoogle Scholar
  17. 17.
    Rokbani N, Abraham A, Alimi Adel M (2013) Fuzzy ant supervised by PSO and simplified ant supervised PSO applied to TSP, 2013 13th international conference on hybrid intelligent systems (HIS), 4–6 Dec. 2013, pp 251–255Google Scholar
  18. 18.
    Angel Preethima R, Johnson Margret (2014) Hybrid ACO-IWD optimization algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments. IJRET: Int J Res Eng Technol 3(3):317–321. eISSN: 2319-1163Google Scholar
  19. 19.
    D’Andreagiovanni Fabio (2014) A hybrid exact-ACO algorithm for the joint scheduling, power and cluster assignment in cooperative wireless networks. Bio-Inspir Models Netw Inf Comput Syst Lect Notes Inst Comput Sci Social Inf Telecommun Eng 134:3–17Google Scholar
  20. 20.
    Merabet M, Benslimane SM (2014) A multi-objective hybrid particle swarm optimization-based service identification, international conference on advanced aspects of software engineering (ICAASE), November, 2–4, 2014, Constantine, Algeria, pp 52–62Google Scholar
  21. 21.
    Kumar Sandeep, Kurmi Jitendra, Tiwari Sudhanshu P (2015) Hybrid ant colony optimization and cuckoo search algorithm for travelling salesman problem. Int J Sci Res Publ 5(6):1–5. ISSN 2250-3153Google Scholar
  22. 22.
    Miranda V (2002) Nuno Fonseca, EPSO—Best-of-Two-Worlds Meta-heuristic Applied to Power System Problems. In: Proceedings of the 2002 Congress on Evolutionary Computation, 2002. CEC ’02, pp 1080–1085Google Scholar
  23. 23.
    Marinakis Yannis, Marinaki Magdalene (2010) A hybrid multi-swarm particle swarm optimization algorithm for the probabilistic traveling salesman problem. Comput Oper Res 37:432–442MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Duan Hai-Bin, Xu Chun-Fang, Xing Zhi-Hui (2010) A hybrid artificial bee colony optimization and quantum evolutionary algorithm for continuous optimization problems. Int J Neural Syst 20(1):39–50. ISSN: 0129-0657Google Scholar
  25. 25.
    Pop Cristina Bianca, Chifu Viorica Rozina, Salomie Ioan, Baico Ramona Bianca, Dinsoreanu Mihaela, Copil Georgiana (2011) A hybrid firefly-inspired approach for optimal semantic web service composition. Sci Int J Parallel Distrib Comput 12(3):363–369Google Scholar
  26. 26.
    Kang Fei, Li Junjie, Ma Zhenyue, Li Haojin (2011) Artificial bee colony algorithm with local search for numerical optimization. J Softw 6(3):490–497CrossRefGoogle Scholar
  27. 27.
    Gao Weifeng, Liu Sanyang (2011) Improved artificial bee colony algorithm for global optimization. Inf Process Lett 111:871–882MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Shivakumar BL, Amudha T (2012) A hybrid bacterial swarming methodology for job shop scheduling environment. Global J Comput Sci Technol Hardw Comput 12(10):1–11Google Scholar
  29. 29.
    Tuba Milan, Bacanin Nebojsa, Stanarevic Nadezda (2012) Adjusted artificial bee colony (ABC) algorithm for engineering problems. WSEAS TRANSACTIONS on COMPUTERS 4(11):111–120Google Scholar
  30. 30.
    Lamartin JP, Martins J (2012) AntBeePath: a hybrid bio-inspired algorithm for path Determination, AAAI Technical Report, 2012, Human Control of Bioinspired Swarms, pp 38-43Google Scholar
  31. 31.
    Zhang Rui, Song Shiji, Wu Cheng (2013) A hybrid artificial bee colony algorithm for the job shop scheduling problem. Int J Prod Econ 141:167–178CrossRefGoogle Scholar
  32. 32.
    Sood Monica, Kaplesh Deepalika (2012) Cross-Country path finding using hybrid approach of PSO and BCO. Int J Appl Inf Syst (IJAIS) 2(1):22–24Google Scholar
  33. 33.
    Martinez-Soto R, Castillo O, Aguilar LT, Baruch IS (2012) Bio-inspired optimization of fuzzy logic controllers for autonomous mobile robots, 2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), 6–8 Aug. 2012, pp 1–6Google Scholar
  34. 34.
    Guo Zhifeng (2012) A hybrid optimization algorithm based on artificial bee colony and gravitational search algorithm. Int J Digit Content Technol Appl (JDCTA) 6(17):602–626. doi: 10.4156/jdcta.vol6.issue17.68 Google Scholar
  35. 35.
    Doraghinejad M, Nezamabadi-pour H, Sadeghian AH, Maghfoori M (2012) A hybrid algorithm based on gravitational search algorithm for unimodal optimization, 2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE), 2012, pp 129–132Google Scholar
  36. 36.
    Soneji Hetal R, Sanghvi Rajesh C (2012) Towards the improvement of cuckoo search algorithm. World Congr Inf Commun Technol (WICT) 2012:878–883Google Scholar
  37. 37.
    Layeb Abdesslem (2013) A hybrid quantum inspired harmony search algorithm for 0–1 optimization problems. J Comput Appl Math 253:14–25MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    KiRan Mustafa Servet, GüNdüZ Mesut (2013) A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems. Appl Soft Comput 13(4):2188–2203CrossRefGoogle Scholar
  39. 39.
    Tuba Milan, Brajevic Ivona, Jovanovic Raka (2013) Hybrid Seeker optimization algorithm for global optimization. Appl Math Inf Sci 7(3):867–875MathSciNetCrossRefGoogle Scholar
  40. 40.
    Lozano Manuel, Duarte Abraham, Gortázar Francisco, Martí Rafael (2013) A hybrid metaheuristic for the cyclic antibandwidth problem. Knowl Based Syst 54:103–113CrossRefGoogle Scholar
  41. 41.
    Kumar Sandeep, Sharma Vivek Kumar, Kumari Rajani (2013) A novel hybrid crossover based artificial bee colony algorithm for optimization problem. Int J Comput Appl (0975— 8887) 82(8):18–25Google Scholar
  42. 42.
    Malhotra R, Khari M (2014) Test suite optimization using mutated artificial bee colony. In: Proc. of Int. Conf. on Advances in Communication, Network, and Computing, CNC, Elsevier, pp 45–54Google Scholar
  43. 43.
    Jatoth Ravi Kumar, Kishore Kumar T (2014) Hybrid GA-PSO based tuning of unscented kalman filter for bearings only tracking. Int J Inf Comput Technol 4(3):315–328. ISSN 0974-2239Google Scholar
  44. 44.
    Feng Y, Jia K, He Y (2014) An improved hybrid encoding cuckoo search algorithm for 0-1 knapsack problems. Comput Intel Neurosci, Volume 2014, Article ID 970456, p 9Google Scholar
  45. 45.
    Abdel-Raouf O, Abdel-Baset M, El-henawy I (2014) A new hybrid flower pollination algorithm for solving constrained global optimization problems. Int J Appl Oper Res 4(2):1–13MathSciNetGoogle Scholar
  46. 46.
    Karpenko AP, Shcherbakova NO, Bulanov VA (2014) A global optimization hybrid algorithm based on the algorithm of artificial immune system and swarm of particles. Science and Education Electronic Journal, Bauman Moscow State Technical University, March 2014, pp 254–274Google Scholar
  47. 47.
    Bolaji Asaju L, Khader Ahamad T, Al-Betar Mohammed A, Awadallah Mohammed A (2014) A hybrid nature-inspired artificial bee colony algorithm for uncapacitated examination timetabling problems. J IntelSyst. 24(1), pp 37-54. ISSN (Online) 2191-026X, ISSN (Print) 0334-1860, doi: 10.1515/jisys-2014-0002
  48. 48.
    Fister I Jr., Fong S, Brest J , Fister I (2014) A novel hybrid self-adaptive bat algorithm. SciWorld J 2014:12. Article ID 709738Google Scholar
  49. 49.
    Evangeline RC (2014) A modified bee colony optimization algorithm for nurse rostering problem. Int J Innov Res Adv Eng(IJIRAE) 1(2):31–35 ISSN: 2278-2311Google Scholar
  50. 50.
    Shrimal Gajendra, Rathi Rakesh (2014) A hybrid best so far artificial bee colony algorithm for function optimization. Int J Comput Sci Inf Technol 5(4):5651–5658Google Scholar
  51. 51.
    Kumar S, Kumar A , Sharma VK, Sharma H (2014) A novel hybrid memetic search in artificial bee colony algorithm, 2014 Seventh International Conference on Contemporary Computing (IC3), 7-9 Aug. 2014, pp 68–73Google Scholar
  52. 52.
    Ali Ahmed F, Hassanien Aboul E,Snasel V (2014) Memetic Artificial bee colony for integer programming, AMLTA 2014, CCIS 488, Springer, pp 268–277Google Scholar
  53. 53.
    Tuba Milan, Bacanin Nebojsa (2014) Artificial bee colony algorithm hybridized with firefly algorithm for cardinality constrained mean-variance portfolio selection problem. Appl Math Inf Sci 8(6):2831–2844MathSciNetCrossRefGoogle Scholar
  54. 54.
    Zhang C, Zhang B (2014) A hybrid artificial bee colony algorithm for the service selection problem. Discret Dyn Nat Soc 2014:13. Article ID 835071Google Scholar
  55. 55.
    Yusof Umi Kalsom, Budiarto Rahmat, Deris Safaai (2014) A hybrid of bio-inspired and musical-harmony approach for machine loading optimization in flexible manufacturing system. Int J Innov Comput Inf Control 10(6):2325–2344Google Scholar
  56. 56.
    Ellissy Abou El-Eyoun Kamel Mohamed, Abdel-hamed Alaa Mohamed (2015) A hybrid bacterial foraging-particle swarm optimization technique for optimal tuning of proportional-integral-derivative controller of a permanent magnet brushless DC motor. Electr Power Compon Syst 43(3):309–319CrossRefGoogle Scholar
  57. 57.
    Baziar Aliasghar, Jabbari Masoud, Shafiee Hassan (2015) A new method based on modified shuffled frog leaping algorithm in order to solve nonlinear large scale problem. Int J Sci Technol Res 4(3):149–154Google Scholar
  58. 58.
    RaviKumar G (2015) A novel hybrid algorithm of onlooker memetic artificial bee colony and cuckoo search using global integer power nap strategy (Gipns) for an efficient disk optimization. Aust J Basic Appl Sci 9(7):773–780MathSciNetGoogle Scholar
  59. 59.
    Lenin K, Ravindhranath Reddy B, Suryakalavathi M (2015) Modified monkey optimization algorithm for solving optimal reactive power dispatch problem. Indones J Electr Eng Inf (IJEEI) 3(2):55–62. ISSN: 2089-3272Google Scholar
  60. 60.
    Jain P, Bansal S, Singh AK, Gupta N (2015) Golomb Ruler Sequences Optimization for FWM Crosstalk Reduction: Multi-population Hybrid Flower Pollination Algorithm, Progress In Electromagnetics Research Symposium Proceedings, pp 2463–2467Google Scholar
  61. 61.
    Kaur Arshpreet, Kahlon Er Navroz Kaur (2015) Swarm Based Enhanced Hybrid Routing Protocol in VANETs. Int J Adv Res Comput Sci Softw Eng 5(5):310–316Google Scholar
  62. 62.
    Tang R, Fong S, Yang X-S, Deb S (2012) Wolf Search Algorithm with Ephemeral Memory, 2012 Seventh International Conference on Digital Information Management (ICDIM), Aug. 2012, IEEE, Macau, pp 165–172Google Scholar
  63. 63.
    Deb S, Fong S, Tian ZH (2015) Elephant Search Algorithm for Optimization Problems, 2015 Tenth International Conference on Digital Information Management (ICDIM) (Oct. 2015) IEEE. Jeju, KoreaGoogle Scholar
  64. 64.
    Fleischmann Patrick, Austvoll Ivar, Kwolek Bogdan (2012) Particle swarm optimization with soft search space partitioning for video-based markerless pose tracking. Adv Concepts Intell Vis Syst LNCS 7517:479–490CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Simon Fong
    • 1
    Email author
  • Xi Wang
    • 1
  • Qiwen Xu
    • 1
  • Raymond Wong
    • 2
  • Jinan Fiaidhi
    • 3
  • Sabah Mohammed
    • 3
  1. 1.Department of Computer and Information ScienceUniversity of MacauMacau SARChina
  2. 2.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia
  3. 3.Department of Computer ScienceLakehead UniversityThunder BayCanada

Personalised recommendations