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Neural Computing and Applications
Published in: Neural Computing and Applications 4/2016

01-05-2016 | Original Article

Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems

Author: Seyedali Mirjalili

Published in: Neural Computing and Applications | Issue 4/2016

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Abstract

A novel swarm intelligence optimization technique is proposed called dragonfly algorithm (DA). The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours of dragonflies in nature. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically. The paper also considers the proposal of binary and multi-objective versions of DA called binary DA (BDA) and multi-objective DA (MODA), respectively. The proposed algorithms are benchmarked by several mathematical test functions and one real case study qualitatively and quantitatively. The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature. The results of MODA also show that this algorithm tends to find very accurate approximations of Pareto optimal solutions with high uniform distribution for multi-objective problems. The set of designs obtained for the submarine propeller design problem demonstrate the merits of MODA in solving challenging real problems with unknown true Pareto optimal front as well. Note that the source codes of the DA, BDA, and MODA algorithms are publicly available at http://​www.​alimirjalili.​com/​DA.​html.
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Appendix
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Literature
1.
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRef
2.
Muro C, Escobedo R, Spector L, Coppinger R (2011) Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations. Behav Process 88:192–197CrossRef
3.
Jakobsen PJ, Birkeland K, Johnsen GH (1994) Swarm location in zooplankton as an anti-predator defence mechanism. Anim Behav 47:175–178CrossRef
4.
Higdon J, Corrsin S (1978) Induced drag of a bird flock. Am Nat 112(986):727–744
5.
Goss S, Aron S, Deneubourg J-L, Pasteels JM (1989) Self-organized shortcuts in the Argentine ant. Naturwissenschaften 76:579–581CrossRef
6.
Beni G, Wang J (1993) Swarm intelligence in cellular robotic systems. In: Dario P, Sandini G, Aebischer P (eds) Robots and biological systems: towards a new bionics? NATO ASI series, vol 102. Springer, Berlin, Heidelberg, pp 703–712
7.
Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, OxfordMATH
8.
Dorigo M, Stützle T (2003) The ant colony optimization metaheuristic: algorithms, applications, and advances. In: Glover F, Kochenberger GA (eds) Handbook of metaheuristics. International series in operations research & management science, vol 57. Springer, USA, pp 250–285
9.
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. Syst Man Cybern Part B Cybern IEEE Trans 26:29–41CrossRef
10.
Colorni A, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies. In: Proceedings of the first European conference on artificial life, pp 134–142
11.
Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, pp 39–43
12.
Eberhart RC, Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 congress on evolutionary computation, pp 81–86
13.
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. In: Technical report-tr06, Erciyes university, engineering faculty, computer engineering department
14.
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39:459–471MathSciNetCrossRefMATH
15.
AlRashidi MR, El-Hawary ME (2009) A survey of particle swarm optimization applications in electric power systems. Evolut Comput IEEE Trans 13:913–918CrossRef
16.
Wei Y, Qiqiang L (2004) Survey on particle swarm optimization algorithm. Eng Sci 5:87–94
17.
Chandra Mohan B, Baskaran R (2012) A survey: ant colony optimization based recent research and implementation on several engineering domain. Expert Syst Appl 39:4618–4627CrossRef
18.
Dorigo M, Stützle T (2010) Ant colony optimization: overview and recent advances. In: Gendreau M, Potvin J-Y (eds) Handbook of metaheuristics. International series in operations research & management science, vol 146. Springer, USA, pp 227–263
19.
Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42:21–57CrossRef
20.
Sonmez M (2011) Artificial Bee Colony algorithm for optimization of truss structures. Appl Soft Comput 11:2406–2418CrossRef
21.
Wang G, Guo L, Wang H, Duan H, Liu L, Li J (2014) Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput Appl 24:853–871CrossRef
22.
Wang G-G, Gandomi AH, Alavi AH (2014) Stud krill herd algorithm. Neurocomputing 128:363–370CrossRef
23.
Wang G-G, Gandomi AH, Alavi AH (2014) An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl Math Model 38:2454–2462MathSciNetCrossRef
24.
Wang G-G, Gandomi AH, Alavi AH, Hao G-S (2014) Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Comput Appl 25:297–308CrossRef
25.
26.
27.
28.
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98
29.
30.
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. Evolut Comput IEEE Trans 1(1):67–82CrossRef
31.
Thorp JH, Rogers DC (2014) Thorp and Covich’s freshwater invertebrates: ecology and general biology. Elsevier, Amsterdam
32.
Wikelski M, Moskowitz D, Adelman JS, Cochran J, Wilcove DS, May ML (2006) Simple rules guide dragonfly migration. Biol Lett 2:325–329CrossRef
33.
Russell RW, May ML, Soltesz KL, Fitzpatrick JW (1998) Massive swarm migrations of dragonflies (Odonata) in eastern North America. Am Midl Nat 140:325–342CrossRef
34.
Reynolds CW (1987) Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput Gr 21:25–34CrossRef
35.
Yang X-S (2010) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press
36.
Cui Z, Shi Z (2009) Boid particle swarm optimisation. Int J Innov Comput Appl 2:77–85CrossRef
37.
Kadrovach BA, Lamont GB (2002) A particle swarm model for swarm-based networked sensor systems. In: Proceedings of the 2002 ACM symposium on applied computing, pp 918–924
38.
Cui Z (2009) Alignment particle swarm optimization. In: Cognitive informatics, 2009. ICCI’09. 8th IEEE international conference on, pp 497–501
39.
Mirjalili S, Lewis A (2013) S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evolut Comput 9:1–14CrossRef
40.
Saremi S, Mirjalili S, Lewis A (2014) How important is a transfer function in discrete heuristic algorithms. Neural Comput Appl:1–16
41.
Mirjalili S, Wang G-G, Coelho LDS (2014) Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput Appl 25:1423–1435CrossRef
42.
Mirjalili S, Lewis A (2015) Novel performance metrics for robust multi-objective optimization algorithms. Swarm Evolut Comput 21:1–23CrossRef
43.
Coello CAC (2009) Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored. Front Comput Sci China 3:18–30CrossRef
44.
Ngatchou P, Zarei A, El-Sharkawi M (2005) Pareto multi objective optimization. In: Intelligent systems application to power systems, 2005. Proceedings of the 13th international conference on, pp 84–91
45.
Branke J, Kaußler T, Schmeck H (2001) Guidance in evolutionary multi-objective optimization. Adv Eng Softw 32:499–507CrossRefMATH
46.
Coello Coello CA, Lechuga MS (2002) MOPSO: A proposal for multiple objective particle swarm optimization. In: Evolutionary computation, 2002. CEC’02. Proceedings of the 2002 congress on, pp 1051–1056
47.
Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. Evolut Comput IEEE Trans 8:256–279CrossRef
48.
Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. Evolut Comput IEEE Trans 3:82–102CrossRef
49.
50.
51.
52.
Liang J, Suganthan P, Deb K (2005) Novel composition test functions for numerical global optimization. In: Swarm intelligence symposium, 2005. SIS 2005. Proceedings 2005 IEEE, pp 68–75
53.
Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y, Auger A et al (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. In: KanGAL Report, vol 2005005
54.
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Neural networks, 1995. Proceedings, IEEE International conference on, pp 1942–1948
55.
John H (1992) Holland, adaptation in natural and artificial systems. MIT Press, Cambridge
56.
Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1:3–18CrossRef
57.
58.
J. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: Systems, man, and cybernetics, 1997. computational cybernetics and simulation, 1997 IEEE international conference on, pp 4104–4108
59.
60.
Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8:173–195CrossRef
61.
Sierra MR, Coello Coello CA (2005) Improving PSO-based multi-objective optimization using crowding, mutation and ∈-dominance. In: Coello Coello CA, Hernández Aguirre A, Zitzler E (eds) Evolutionary multi-criterion optimization. Lecture notes in computer science, vol 3410. Springer, Berlin, Heidelberg, pp 505–519
62.
Van Veldhuizen DA, Lamont GB (1998) Multiobjective evolutionary algorithm research: a history and analysis (Final Draft) TR-98-03
63.
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolut Comput IEEE Trans 6:182–197CrossRef
64.
Carlton J (2012) Marine propellers and propulsion. Butterworth-Heinemann, Oxford
Metadata
Title
Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems
Author
Seyedali Mirjalili
Publication date
01-05-2016
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 4/2016
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-1920-1

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