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Neural Computing and Applications

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15.05.2015 | Theory and Applications of Soft Computing Methods

Attraction and diffusion in nature-inspired optimization algorithms

verfasst von: Xin-She Yang, Suash Deb, Thomas Hanne, Xingshi He

Erschienen in: Neural Computing and Applications | Ausgabe 7/2019

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Abstract

Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behavior and performance of nature-inspired algorithms. We highlight different ways of the implementations of attraction in algorithms such as the firefly algorithm, charged system search, and the gravitational search algorithm. We also analyze diffusion mechanisms such as random walks for exploration in algorithms. It is clear that attraction can be an effective way for enhancing exploitation, while diffusion is a common way for exploration. Furthermore, we also discuss the role of parameter tuning and parameter control in modern metaheuristic algorithms and then point out some key topics for further research.

Vorheriger Artikel Theory and applications of soft computing methods

Nächster Artikel Monarch butterfly optimization

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Arora S, Singh S (2013) The firefly optimization algorithm: convergence analysis and parameter selection. Int J Comput Appl 69(3):48–52

Beyer H-G (1995) Toward a theory of evolution strategies: self-adaptation. Evolut Comput 3(3):311–347CrossRef

Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35:268–308CrossRef

Branke J, Elomari JA (2012) Meta-optimization for parameter tuning with a flexible computing budget. In: Soule T (ed) Proceedings of the 14th Annual Conference on genetic and evolutionary computation (GECCO ‘12). ACM, New York, pp 1245–1252

Burke E, Gendreau K, Hyde M, Kendall M, Ochoa G, Özcan E, Qu R (2013) Hyper-heuristics: a survey of the state of the art. J Op Res Soc 64(12):1695–1724CrossRef

Črepinšek M, Liu S-H, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45(3):35:1–35:33MATH

Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35CrossRef

Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison Wesley, Reading, MassMATH

Hanne T (1999) On the convergence of multiobjective evolutionary algorithms. Eur J Op Res 117(3):553–564MathSciNetCrossRefMATH

10.

Hanne T (2001) Intelligent strategies for meta multiple criteria decision making. Springer, BerlinCrossRefMATH

11.

Hanne T (2007) A multiobjective evolutionary algorithm for approximating the efficient set. Eur J Op Res 176(3):1723–1734MathSciNetCrossRefMATH

12.

Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3–4):267–289CrossRefMATH

13.

Kaveh A, Talatahari S (2012) Charged system search for optimal design of frame structures. Appl Soft Comput 12(1):382–393CrossRef

14.

Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, pp. 1942–1948

15.

Mantegna RN (1994) Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes. Phys Rev E 49:4677–4683CrossRef

16.

Pavlyukevich I (2007) Lévy flights, non-local search and simulated annealing. J Comput Phys 226:1830–1844MathSciNetCrossRefMATH

17.

Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248CrossRefMATH

18.

Rechenberg I (1973) Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (Evolution strategy: optimization of technical systems based on concepts from biological evolution). Fromman-Holzboog, Stuttgart

19.

Talatahari S, Jahani Y (2015) Hybrid charged system search-particle swarm optimization for design of single-layer barrel vault structures. Asian J Civil Eng 16(4):515–533

20.

Tan KC, Goh CK, Yang YJ, Lee TH (2006) Evolving better population distribution and exploration in evolutionary multi-objective optimization. Eur J Op Res 171(2):463–495CrossRefMATH

21.

Tan KC, Chiam SC, Mamun AA, Goh CK (2009) Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization. Eur J Op Res 197(2):701–713CrossRefMATH

22.

Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press, Bristol

23.

Yang XS (2008) Introduction to computational mathematics. World Scientific Publishing, SingaporeCrossRefMATH

24.

Yang XS (2009) Firefly algorithms for multimodal optimization. In: Watanabe O, Zeugmann T (eds) Proceedings of 5th Symposium on Stochastic Algorithms, Foundations and Applications, SAGA 2009, Springer, Heidelberg, pp. 169–178

25.

Yang XS (2011) Metaheuristic optimization: algorithm analysis and open problems. In: Pardalos PM, Rebennack S (eds) Experimental algorithms. Springer, Berlin, pp 21–32CrossRef

26.

Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Proceedings of world congress on nature and biologically inspired computing (NaBIC 2009, India), IEEE Publications, USA, pp. 210–214

27.

Yang XS, Deb S (2010) Engineering optimization by cuckoo search. Int J Math Model Numer Optim 1(4):330–343MATH

28.

Yang XS, Deb S (2010) Eagle strategy using Lévy walk and firefly algorithms for stochastic optimization. Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, pp 101–111CrossRef

29.

Yang XS, Deb S (2012) Two-stage eagle strategy with differential evolution. Int J Bio-Inspir Comput 4(1):1–5CrossRef

30.

Yang XS, Deb S, Fong S (2011) Accelerated particle swarm optimization and support vector machine for business optimization and applications. Networked digital technologies (NDT2011), communications in computer and information science, vol 136. Springer, Berlin, pp 53–66

31.

Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483CrossRef

32.

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

Titel: Attraction and diffusion in nature-inspired optimization algorithms
verfasst von: Xin-She Yang
Suash Deb
Thomas Hanne
Xingshi He
Publikationsdatum: 15.05.2015
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-1925-9

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