Metaheuristics are found to be efficient in different applications where the use of exact algorithms becomes short-handed. In the last decade, many of these algorithms have been introduced and used in a wide range of applications. Nevertheless, most of those approaches share similar components leading to a concern related to their novelty or contribution. Thus, in this paper, a pool template is proposed and used to categorize algorithm components permitting to analyze them in a structured way. We exemplify its use by means of continuous optimization metaheuristics, and provide some measures and methodology to identify their similarities and novelties. Finally, a discussion at a component level is provided in order to point out possible design differences and commonalities.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
We should note that the archiving function may be extended in the sense of distinguishing long-term memory and short-term memory functions, the first accounting for recent solutions or solution attributes and the latter accounting for measures supporting to memorize an overall history of a search, e.g., by using frequency-based memory to count for occurrences of properties of solutions.
For this comparison, we rely on Algorithm 3 presented Weyland (2015).
Akbari R, Mohammadi A, Ziarati K (2009) A powerful bee swarm optimization algorithm. In: IEEE 13th international multitopic conference (INMIC), pp 1 – 6, https://doi.org/10.1109/INMIC.2009.5383155
Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Computing Surv 35(3):268–308. https://doi.org/10.1145/937503.937505
Brooks SH (1958) A discussion of random methods for seeking maxima. Op Res 6(2):244–251. https://doi.org/10.1287/opre.6.2.244
Camacho Villalón CL, Stützle T, Dorigo M (2020) Grey wolf, firefly and bat algorithms: three widespread algorithms that do not contain any novelty. In: Dorigo M, Stützle T, Blesa MJ, Blum C, Hamann H, Heinrich MK, Strobel V (eds) Swarm intelligence. Springer International Publishing, Cham, pp 121–133
Chen T, Wang Y, Li J (2012) Artificial tribe algorithm and its performance analysis. J Softw 7:651–656. https://doi.org/10.4304/jsw.7.3.651-656
Cuevas E, González M, Zaldivar D, Pérez-Cisneros M, G G, (2012) An algorithm for global optimization inspired by collective animal behavior. Discrete Dyn Nat Soc 2012638275:24. https://doi.org/10.1155/2012/638275
Duarte A, Laguna M, Martí R (2018) Introduction to spreadsheet modeling and metaheuristics. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-68119-1_1
Greistorfer P, Voß S (2005) Controlled pool maintenance for metaheuristics. In: Rego C, Alidaee B (ed) Metaheuristic optimization via memory and evolution. Kluwer, Boston, pp 387−424. https://doi.org/10.1007/0-387-23667-8_18
Hasançebi O, Azad SK (2012) An efficient metaheuristic algorithm for engineering optimization: SOPT. Int J Optim Civ Eng 2:479–487
Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inform Sci 222:175–184. https://doi.org/10.1016/j.ins.2012.08.023
Havens TC, Spain CJ, Salmon NG, Keller JM (2008) Roach infestation optimization. In: IEEE Swarm Intelligence Symposium, September 21–23, 2008, St. Louis MO, USA, pp 1–7, https://doi.org/10.1109/SIS.2008.4668317
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Neural Networks, 1995. Proceedings., IEEE International Conference on, vol 4, pp 1942–1948 vol.4, https://doi.org/10.1109/ICNN.1995.488968
Laguna M (2016) Editor’s note on the MIC 2013 special issue of the Journal of Heuristics (Volume 22, Issue 4, August 2016). J Heuristics 22(5):665–666. https://doi.org/10.1007/s10732-016-9318-5
Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Computing Appl 24:1867–1877. https://doi.org/10.1007/s00521-013-1433-8
Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1:355–366. https://doi.org/10.1016/j.ecoinf.2006.07.003
Piotrowski AP, Napiorkowski JJ, Rowinski PM (2014) How novel is the “novel” black hole optimization approach? Inform Sci 267:191–200
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inform Sci 179:2232–2248. https://doi.org/10.1016/j.ins.2009.03.004
Rastrigin LA (1963) The convergence of the random search method in the extremal control of a many parameter system. Autom Remote Control 24:1337–1342
Sörensen K (2015) Metaheuristics - the metaphor exposed. Int Trans Op Res 22:3–18. https://doi.org/10.1111/itor.12001
Sörensen K, Glover F (2013) Metaheuristics. In: Gass SI, Fu M (eds) Encyclopedia of operations research and management science. Springer, New York, pp 960–970. https://doi.org/10.1007/978-1-4419-1153-7
Swan J, Adriaensen S, Bishr M, Burke EK, Clark JA, De Causmaecker P, Durillo J, Hammond K, Hart E, Johnson CG, et al. (2015) A research agenda for metaheuristic standardization. In: Proceedings of the XI metaheuristics international conference
Voß S, Martello S, Osman I, Roucairol C (eds) (1999) Meta-Heuristics: advances and trends in local search paradigms for optimization. Kluwer, Boston. https://doi.org/10.1007/978-1-4615-5775-3
Watson JP, Howe AE, Whitley LD (2006) Deconstructing Nowicki and Smutnicki’s i-TSAB tabu search algorithm for the job-shop scheduling problem. Computers Op Res 33(9):2623–2644. https://doi.org/10.1016/j.cor.2005.07.016
Weyland D (2010) A rigorous analysis of the harmony search algorithm - how the research community can be misled by a “novel” methodology. Int J Appl Metaheuristic Computing 1–2:50–60. https://doi.org/10.4018/jamc.2010040104
Weyland D (2015) A critical analysis of the harmony search algorithm–how not to solve sudoku. Op Res Perspecti 2:97–105
Whitley D (1994) A genetic algorithm tutorial. Stat Computing 4(2):65–85. https://doi.org/10.1007/BF00175354
Xing B, Gao WJ (2014) Innovative computational intelligence: a rough guide to 134 clever algorithms. Springer, Berlin. https://doi.org/10.1007/978-3-319-03404-1
Yang C, Tu X, Chen J (2007) Algorithm of marriage in honey bees optimization based on the wolf pack search. In: IEEE International conference on intelligent pervasive computing (IPC), pp 462 – 467, https://doi.org/10.1109/IPC.2007.104
Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press, UK
Yang XS (2010) A new Metaheuristic Bat-Inspired algorithm. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Nature inspired cooperative strategies for optimization (NICSO 2010). Studies in computational intelligence, vol 284. Springer, Berlin, Heidelberg, pp 65–74. https://doi.org/10.1007/978-3-642-12538-6_6
Zhang X, Chen W, Dai C (2008) Application of oriented search algorithm in reactive power optimization of power system. In: Third international conference on electric utility deregulation and restructuring and power technologies, DRPT 2008, Nanjing, China, pp 2856 – 2861, https://doi.org/10.1109/DRPT.2008.4523896
This work was partially supported by the Ministry of Science, Innovation and Universities of the Government of Spain AEI/FEDER through the project RTI2018-095197-B-I00. Surafel Luleseged Tilahun would like to acknowledge the TWAS-DFG Cooperation Visits Programme for the support to visit University of Hamburg for a research collaboration.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
de Armas, J., Lalla-Ruiz, E., Tilahun, S.L. et al. Similarity in metaheuristics: a gentle step towards a comparison methodology. Nat Comput (2021). https://doi.org/10.1007/s11047-020-09837-9
- Metaheuristics design
- Comparison methodology
- Pool template
- Algorithm similarity