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Natural Computing

Erschienen in:

10.02.2021

Similarity in metaheuristics: a gentle step towards a comparison methodology

verfasst von: Jesica de Armas, Eduardo Lalla-Ruiz, Surafel Luleseged Tilahun, Stefan Voß

Erschienen in: Natural Computing | Ausgabe 2/2022

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Abstract

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.

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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/10.1145/937503.937505CrossRef

Brooks SH (1958) A discussion of random methods for seeking maxima. Op Res 6(2):244–251. https://doi.org/10.1287/opre.6.2.244CrossRef

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 121–133CrossRef

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.7.3.651-656CrossRef

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/10.1155/2012/638275MathSciNetCrossRef

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_1CrossRefMATH

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.2012.08.023MathSciNetCrossRef

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/s10732-016-9318-5CrossRef

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/s00521-013-1433-8CrossRef

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.2006.07.003CrossRef

Piotrowski AP, Napiorkowski JJ, Rowinski PM (2014) How novel is the “novel” black hole optimization approach? Inform Sci 267:191–200CrossRef

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.2009.03.004CrossRefMATH

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.12001MathSciNetCrossRefMATH

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/10.1007/978-1-4419-1153-7CrossRef

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/10.1007/978-1-4615-5775-3CrossRefMATH

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.2005.07.016CrossRefMATH

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.2010040104CrossRef

Weyland D (2015) A critical analysis of the harmony search algorithm–how not to solve sudoku. Op Res Perspecti 2:97–105MathSciNet

Whitley D (1994) A genetic algorithm tutorial. Stat Computing 4(2):65–85. https://doi.org/10.1007/BF00175354CrossRef

Xing B, Gao WJ (2014) Innovative computational intelligence: a rough guide to 134 clever algorithms. Springer, Berlin. https://doi.org/10.1007/978-3-319-03404-1CrossRefMATH

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_6CrossRef

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

Titel: Similarity in metaheuristics: a gentle step towards a comparison methodology
verfasst von: Jesica de Armas
Eduardo Lalla-Ruiz
Surafel Luleseged Tilahun
Stefan Voß
Publikationsdatum: 10.02.2021
Verlag: Springer Netherlands
Erschienen in: Natural Computing / Ausgabe 2/2022
Print ISSN: 1567-7818
Elektronische ISSN: 1572-9796
DOI: https://doi.org/10.1007/s11047-020-09837-9

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