Abstract
Optimization and decision making problems in various fields of engineering have a major impact in this current era. Processing time and utilizing memory is very high for the currently available data. This is due to its size and the need for scaling from zettabyte to yottabyte. Some problems need to find solutions and there are other types of issues that need to improve their current best solution. Modelling and implementing a new heuristic algorithm may be time consuming but has some strong primary motivation - like a minimal improvement in the solution itself can reduce the computational cost. The solution thus obtained was better. In both these situations, designing heuristics and meta-heuristics algorithm has proved it’s worth. Hyper heuristic solutions will be needed to compute solutions in a much better time and space complexities. It creates a solution by combining heuristics to generate automated search space from which generalized solutions can be tuned out. This paper provides in-depth knowledge on nature-inspired computing models, meta-heuristic models, hybrid meta heuristic models and hyper heuristic model. This work’s major contribution is on building a hyper heuristics approach from a meta-heuristic algorithm for any general problem domain. Various traditional algorithms and new generation meta heuristic algorithms has also been explained for giving readers a better understanding.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Niu B, Wang H (2012) Bacterial colony optimization. Discrete Dyn Nat Soc. https://doi.org/10.1155/2012/698057
Maniezzo V, Gambardella LM, de Luigi F (2004) Ant Colony Optimization. In: New optimization techniques in engineering. Studies in fuzziness and soft computing, Springer, vol 141, Germany. https://doi.org/10.1007/978-3-540-39930-8_5
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report-TR06. Erciyes University, Engineering Faculty, Computer Engineering Department
Yang XS (2010) A new metaheuristic bat-inspired algorithm, nature inspired cooperative strategies for optimization, studies in computational intelligence, vol 284. Springer, Germany. https://doi.org/10.1007/978-3-642-12538-6_6
Hedayatzadeh R, Salmassi F Akhavan, Keshtgari M, Akbari R, Ziarati K (2010) Termite colony optimization: a novel approach for optimizing continuous problems. In: 2010 18th Iranian conference on electrical engineering, Isfahan, pp 553–558, https://doi.org/10.1109/IRANIANCEE.2010.5507009
Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38(2):129–154. https://doi.org/10.1080/03052150500384759
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680
Saju Sankar S, Vinod Chandra SS (2020) A multi-agent ACO algorithm for effective vehicular traffic management system. Lect Notes Comput Sci 12145:640–647
Saju Sankar S, Vinod Chandra SS (2020) An ant colony optimization algorithm based automated generation of software test cases. Lect Notes Comput Sci 12145:231–239
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95 - international conference on neural networks, pp 1942–1948, vol 4, Australia. https://doi.org/10.1109/ICNN.1995.488968
Vinod Chandra SS, Saju Sankar S, Anand HS (2020) Multi-objective particle swarm optimization for cargo packaging. Lect Notes Comput Sci 12145:415–422
Saritha R, Vinod Chandra SS (2016) An approach using particle swarm optimization and rational kernel for variable length data sequence optimization. Lect Notes Comput Sci 9712:401–409
Reynolds RG (1994) An introduction to cultural algorithms. In: Sebald AV, Fogel LJ (eds), Proceedings of the third annual conference on evolutionary programming, pp 131–139. World Scientific, River Edge
Woo Z, Hoon J, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68. https://doi.org/10.1177/003754970107600201
Pham D, Ghanbarzadeh A, Koç E, Otri S, Rahim S, Zaidi MB (2005) The Bees Algorithm Technical Note, Manufacturing Engineering Centre, Cardiff University, pp 1-57, UK
Saritha R, Vinod Chandra SS (2018) Multi modal foraging by honey bees toward optimizing profits at multiple colonies. IEEE Intell Syst 34:14–22
Saritha R, Vinod Chandra SS (2017) Multi dimensional honey bee foraging algorithm based on optimal energy consumption. J Inst Eng Ser B 98:517–525
Krishnanand KN, Ghose D (2009) Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intell 3:87–124. https://doi.org/10.1007/s11721-008-0021-5
Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation, pp 4661–4667, Singapore. https://doi.org/10.1109/CEC.2007.4425083
Rabanal P, Rodríguez I, Rubio F (2007) Using river formation dynamics to design heuristic algorithms. In: Unconventional computation, lecture notes in computer science, vol 4618. Springer, Germany.https://doi.org/10.1007/978-3-540-73554-0_16
Shah-Hosseini H (2008) Intelligent water drops algorithm: a new optimization method for solving the multiple knapsack problem. Int J Intell Comput Cybern 1:193–212. https://doi.org/10.1108/17563780810874717
Yang XS (2009) Firefly algorithms for multimodal optimization. In: Lecture notes in computer science, vol 5792. Springer, Germany. https://doi.org/10.1007/978-3-642-04944-6_14
Rashedi E, Nezamabadi-pour H, Saryazdi S (2010) BGSA: binary gravitational search algorithm. Nat Comput 9:727–745. https://doi.org/10.1007/s11047-009-9175-3
Yang X, Deb Suash (2009) Cuckoo search via levy flights. In: World congress on nature and biologically inspired computing (NaBIC), pp 210–214, India. https://doi.org/10.1109/NABIC.2009.5393690
Benasla L, Belmadani A, Mostefa R (2014) Spiral optimization algorithm for solving combined economic and emission dispatch. Int J Electr Power Energy Syst 62:163–174. https://doi.org/10.1016/j.ijepes.2014.04.03
Anathalakshmi Ammal R, Sajimoan PC, Vinod Chandra SS (2020) Termite inspired algorithm for traffic engineering in hybrid software defined networks. PeerJ Comput Sci 6:283
Yang XS (2012) Flower pollination algorithm for global optimization. In: Lecture notes in computer science, vol 7445. Springer, Germany. https://doi.org/10.1007/978-3-642-32894-7_27
Cuevas E, Cienfuegos M (2014) A new algorithm inspired in the behavior of the social-spider for constrained optimization. Exp Syst Appl 41:412–425. https://doi.org/10.1016/j.eswa.2013.07.067
Eesa AS, Brifcani AMA, Orman Z (2013) Cuttlefish algorithm: a novel bio-inspired optimization algorithm. Int J Sci Eng Res 4(9):1978–1986
Vinod Chandra SS (2016) Smell detection agent based optimization algorithm. J Inst Eng India Ser B 97:431–436. https://doi.org/10.1007/s40031-014-0182-0
Saju Sankar S, Vinod Chandra SS (2020) A structural testing model using SDA algorithm. Lect Notes Comput Sci 12145:405–412
Ananthalakshmi Ammal R, Sajimon PC, Vinod Chandra SS (2017) Application of smell detection agent based algorithm for optimal path identification by SDN Ccntrollers. Lect Notes Comput Sci 10386:502–510
Odili J, Kahar M, Nizam M, Shahid A (2015) African buffalo optimization a swarm-intelligence technique. Proc Comput Sci. https://doi.org/10.1016/j.procs.2015.12.291
Biyanto TR, Fibrianto HY, Nugroho G, Hatta AM, Listijorini E, Budiati T, Huda H (2016) Duelist algorithm: an algorithm inspired by how duelist improve their capabilities in a duel. In: International conference in swarm intelligence, Springer, pp 39–47
Biyanto TRM, Irawan S, Febrianto HY, Afdanny N, Rahman AH, Gunawan KS, Pratama Januar AD, Bethiana Titania N (2017) Killer whale algorithm: an algorithm inspired by the life of killer whale. Proc Comput Sci 124:151–157
Wedyan A, Whalley J, Narayanan A (2017) Hydrological cycle algorithm for continuous optimization problems. J Optim. https://doi.org/10.1155/2017/3828420
Jain M, Maurya S, Rani A, Singh V, Thampi SM, El-Alfy E-SM, Mitra S, Trajkovic L (2018) Owl search algorithm: a novel nature-inspired heuristic paradigm for global optimization. J Intell Fuzzy Syst 34:1573–1582
Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput 44:148–175
RezaFathollahi-Fard AM, Hajiaghaei-Keshteli M, Tavakkoli-Moghaddam RezaTavakkoli-Moghaddam RR (2018) The social engineering optimizer (SEO). Eng Appl Artif Intell 72:267–293
Elsisi M (2019) Future search algorithm for optimization. Evol Intell 12(1):21–31
Harifi S, Khalilian M, Mohammadzadeh J, Ebrahimnejad S (2019) Emperor penguins colony: a new metaheuristic algorithm for optimization. Evol Intell 12:211–226
Kaveh A, Dadras AA (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84
Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gen Comput Syst 97:849–872
Askari Q, Younas I, Saeed M (2020) Political optimizer: a novel socio-inspired meta-heuristic for global optimization. Knowl Based Syst 195:105709
Askari Q, Saeed M, Younas I (2020) Heap-based optimizer inspired by corporate rank hierarchy for global optimization. Exp Syst Appl 161:113
Zaeimi M, Ghoddosian A (2020) Color harmony algorithm: an art-inspired metaheuristic for mathematical function optimization. Soft Comput 24(16):12027–12066
Harifi S, Mohammadzadeh J, Khalilian M, Ebrahimnejad S (2020) Giza pyramids construction: an ancient-inspired metaheuristic algorithm for optimization. Evol Intell 2020:1–19
Vinod Chandra SS, Anand HS, Saju Sankar S (2020) Optimal reservoir optimization using multiobjective genetic algorithm. Lect Notes Comput Sci 12145:445–454
Burke EK, Gendreau M, Hyde M, Kendall G, Ochoa G, Özcan E, Qu R (2013) Hyper heuristics: a survey of the state of the art. J Oper Res Soc 64(12):1695–1724
Gómez RH, Coello CAC (2017) A hyper heuristic of scalarizing functions. In: Proceedings of the genetic and evolutionary computation conference, pp 577–584
Hansen P, Mladenovic N, Pérez JAM (2010) Variable neighbourhood search: methods and applications. Ann Oper Res 175(1):367–407
Uludag G, Kiraz B, Etaner-Uyar AS, Ozcan E (2013) A hybrid multi population framework for dynamic environments combining online and offline learning. Soft Comput 17(12):2327–2348
Hsiao P-C, Chiang T-C, Fu L-C (2012) A vns based hyper heuristic with adaptive computational budget of local search. In: Proceedings of the IEEE congress on evolutionary computation, pp 1–8
Meignan D (2011) An evolutionary programming hyper heuristic with co-evolution. In: Proceedings of the 53rd annual conference of the UK operational research society
Lehrbaum A, Musliu N (2012) A new hyper heuristic algorithm for cross do main search problems. In: Proceedings of the learning and intelligent optimization, LNCS, pp 437–442
Salcedo-Sanz S, Matías-Román J, Jiménez-Fernández S, Portilla-Figueras A, Cuadra L (2014) An evolutionary based hyper heuristic approach for the jaw breaker puzzle. Appl Intell 40(3):404–414
Salhi A, Rodríguez JAV (2014) Tailoring hyper heuristics to specific instances of a scheduling problem using affinity and competence functions. Memetic Comput 6(2):77–84
Strickler A, Lima JAP, Vergilio SR, Pozo AT (2016) Deriving products for variability test of feature models with a hyper heuristic approach. Appl Soft Comput 49:1232–1242
Parejo JA, Ruiz-Cortés A, Lozano S, Fernandez P (2012) Metaheuristic optimization frameworks: a survey and benchmarking. Soft Comput 16(3):527–561
Tyasnurita R, Özcan E, John R (2017) Learning heuristic selection using a time delay neural network for open vehicle routing. In: Proceedings of the IEEE congress on evolutionary computation, pp 1474–1481
Vinod Chandra SS, Anand HS (2021) Phototropic algorithm for global optimisation problems. Applied Intelligence
Acknowledgements
The authors would like to thank the Government for India for providing Copyrights (IPR) for the Nature Inspired Algorithm developed by the authors (Registration Nos.: L-74114/2018, L-65846/2017, L-62609/2015, and L-60823/2014). The authors also extend their thanks to all the Machine Intelligent Research group, who have been a constant support during the various phases of analysis and implementation of different nature inspired algorithms.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
S. S., V.C., H. S., A. Nature inspired meta heuristic algorithms for optimization problems. Computing 104, 251–269 (2022). https://doi.org/10.1007/s00607-021-00955-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00607-021-00955-5
Keywords
- Nature inspired computing
- Meta heuristics
- Hyper heuristics
- Evolutionary computing
- Bio inspired computing
- Hybrid meta heuristics