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Neural Computing and Applications
Published in: Neural Computing and Applications 10/2020

13-03-2019 | Original Article

A conceptual comparison of several metaheuristic algorithms on continuous optimisation problems

Authors: Absalom E. Ezugwu, Olawale J. Adeleke, Andronicus A. Akinyelu, Serestina Viriri

Published in: Neural Computing and Applications | Issue 10/2020

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Abstract

The field of continuous optimisation has witnessed an explosion of the so-called new or novel metaheuristic algorithms. Though not all of these algorithms are efficient as proclaimed by their inventors, a few of them have proved to be very efficient and thus have become popular tools for solving complex optimisation problems. Therefore, there is a need for a systematic analysis approach to fairly evaluate and compare the results of some of these optimisation algorithms. In this paper, a set of well-known mathematical benchmark functions are compiled to provide an easily accessible collection of standard benchmark test problems for continuous global optimisation. This set of test problems are used to investigate the computational capabilities and the microscopic behaviour of twelve different metaheuristic algorithms. The required number of function evaluations for reaching the best solution and the run-time complexity of the algorithms are compared. Furthermore, statistical tests are conducted to validate the concluding remarks.
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Appendix
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Literature
1.
Zhou JX, Zou W (2008) Meshless approximation combined with implicit topology description for optimisation of continua. Struct Multidiscip Optim 36(4):347–353
2.
Luo Z, Zhang N, Wang Y, Gao W (2013) Topology optimisation of structures using meshless density variable approximants. Int J Numer Methods Eng 93(4):443–464MATH
3.
Lin J, Chen CS, Liu CS, Lu J (2016) Fast simulation of multi-dimensional wave problems by the sparse scheme of the method of fundamental solutions. Comput Math Appl 72(3):555–567MathSciNetMATH
4.
Lin J, Reutskiy SY, Lu J (2018) A novel meshless method for fully nonlinear advection–diffusion–reaction problems to model transfer in anisotropic media. Appl Math Comput 339:459–476MathSciNetMATH
5.
Fu ZJ, Xi Q, Chen W, Cheng AHD (2018) A boundary-type meshless solver for transient heat conduction analysis of slender functionally graded materials with exponential variations. Comput Math Appl 76:760–773MathSciNetMATH
6.
Fister I, JrFister I, Yang X-S, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evolut Comput 13:34–46
7.
Jamil M, Yang XS (2013) A literature survey of benchmark functions for global optimisation problems. Int J Math Model Numer Optim 4(2):150–194MATH
8.
Dieterich JM, Hartke B (2012) Empirical review of standard benchmark functions using evolutionary global optimisation. Appl Math 3:1552–1564
9.
Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73
10.
Goldbarg EF, Goldbarg MC, de Souza GR (2008) Particle swarm optimisation algorithm for the traveling salesman problem. In: Traveling salesman problem, ed: InTech
11.
Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimisation. IEEE Comput Intell Mag 1(4):28–39
12.
Cheng M-Y, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimisation algorithm. Comput Struct 139:98–112
13.
Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. In: 2009. NaBIC 2009. World Congress on nature & biologically inspired computing, pp 210–214
14.
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimisation: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471MATH
15.
Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Nature inspired cooperative strategies for optimisation (NICSO 2010). Springer, Berlin, pp 65–74
16.
Ab Wahab MN, Nefti-Meziani S, Atyabi A (2015) A comprehensive review of swarm optimisation algorithms. PLoS ONE 10(5):e0122827
17.
Yang X-S (2012) Flower pollination algorithm for global optimisation. In: International conference on unconventional computing and natural computation, pp 240–249
18.
Mehrabian AR, Lucas C (2006) A novel numerical optimisation algorithm inspired from weed colonization. Ecol Inf 1(4):355–366
19.
Pham DT, Castellani M (2009) The bees algorithm: modelling foraging behaviour to solve continuous optimisation problems. Proc Inst Mech Eng Part C J Mech Eng Sci 223(12):2919–2938
20.
Dolan ED, Moré JJ (2002) Benchmarking optimisation software with performance profiles. Math Program 91(2):201–213MathSciNetMATH
21.
Ma H, Simon D, Fei M, Chen Z (2013) On the equivalences and differences of evolutionary algorithms. Eng Appl Artif Intell 26(10):2397–2407
22.
Ma H, Ye S, Simon D, Fei M (2017) Conceptual and numerical comparisons of swarm intelligence optimisation algorithms. Soft Comput 21(11):3081–3100
23.
Civicioglu P, Besdok EA (2013) A conceptual comparison of the Cuckoo-search, particle swarm optimisation, differential evolution and artificial bee colony algorithms. Artif Intell Rev 39(4):315–346
24.
Soler-Dominguez A, Juan AA, Kizys R (2017) A survey on financial applications of metaheuristics. ACM Comput Surv (CSUR) 50(1):15
25.
Yang XS (2010) Engineering optimisation: an introduction with metaheuristic applications. Wiley, New York
26.
Ezugwu AE, Prayogo D (2019) Symbiotic organisms search algorithm: theory, recent advances and applications. Expert Syst Appl 119(1):184–209
27.
Ali MM, Khompatraporn C, Zabinsky ZB (2005) A numerical evaluation of several stochastic algorithms on selected continuous global optimisation test problems. J Glob Optim 31(4):635–672MATH
28.
Yang XS (2010) Firefly algorithm, Lévy flights and global optimization. In: Bramer M, Ellis R, Petridis M (eds) Research and development in intelligent systems, vol XXVI. Springer, London, pp 209–218
29.
Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multiobjective optimisation. Eng Optim 46(9):1222–1237MathSciNet
30.
Gendreau M, Potvin JY (2010) Handbook of metaheuristics. Springer, New YorkMATH
31.
Amodeo L, Talbi EG, Yalaoui F (eds) (2018) Recent developments in metaheuristics. Springer, BerlinMATH
32.
Zhang L, Liu L, Yang X-S, Dai Y (2016) A novel hybrid firefly algorithm for global optimisation. PLoS ONE 11(9):e0163230
33.
34.
Silberholz J, Golden B (2010) Comparison of metaheuristics. In: Gendreau M, Potvin JY (eds) Handbook of metaheuristics. International series in operations research & management science, vol 146. Springer, BostonMATH
35.
Lobo FJ, Lima CF, Michalewicz Z (eds) (2007) Parameter setting in evolutionary algorithms, 54th edn. Springer, BerlinMATH
36.
Shi Y, Eberhart RC (1998) Parameter selection in particle swarm optimization. In: Porto VW, Saravanan N, Waagen D, Eiben AE (eds) Evolutionary programming VII. EP 1998. Lecture notes in computer science, vol 1447. Springer, Berlin, Heidelberg
37.
Rajabioun R (2011) Cuckoo optimisation algorithm. Appl Soft Comput 11(8):5508–5518
38.
Yang XS, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24(1):169–174
39.
Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver Press, Frome
40.
Dorigo M, Birattari M (2017) Ant colony optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning and data mining. Springer, Boston
41.
Dorigo M, Di Caro G (1999) Ant colony optimisation: a new meta-heuristic. In: Evolutionary computation, 1999. CEC 99. Proceedings of the 1999 Congress on, vol 2. IEEE, pp 1470–1477
42.
Darquennes D (2005) Implementation and applications of ant colony algorithms. Masters, Faculty of Computer Science, University of Namur, Belgium
43.
Yang X-S, He X (2013) Bat algorithm: literature review and applications. Int J Bio-Inspired Comput 5(3):141–149
44.
Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimisation. Eng Comput 29(5):464–483
45.
Chittka L, Thomson JD, Waser NM (1999) Flower constancy, insect psychology, and plant evolution. Naturwissenschaften 86(8):361–377
46.
Fister JI, Yang XS, Fister I, Brest J, Fister D (2013) A brief review of nature-inspired algorithms for optimisation. arXiv preprint arXiv:​1307.​4186
47.
Ezugwu AE, Adewumi OA (2017) Discrete symbiotic organisms search algorithm for travelling salesman problem. Expert Syst Appl 30(87):70–78
48.
Valle YD, Venayagamoorthy GK, Mohagheghi S, Hernandez JC, Harley RG (2008) Particle swarm optimisation: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 12(2):171–195
49.
Cui Z (2009) Alignment particle swarm optimisation. In: 2009 8th IEEE international conference on cognitive informatics, pp 497–501
50.
Kennedy J, Eberhart R (1995) Particle swarm optimisation. In: Neural networks, 1995. Proceedings, IEEE international conference on, vol 4, pp 1942–1948
51.
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimisation over continuous spaces. J Glob Optim 11(4):341–359MATH
52.
Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31
53.
Goldberg D (1989) Genetic algorithms in optimisation, search and machine learning. Addison-Wesley, ReadingMATH
54.
Tereshko V, Loengarov A (2005) Collective decision making in honey-bee foraging dynamics. Comput Inf Syst 9(3):1
55.
Von Frisch K (2014) Bees: their vision, chemical senses, and language. Cornell University Press, Ithaca
56.
Bozorg-Haddad O, Solgi M, Lo HA (2017) Meta-heuristic and evolutionary algorithms for engineering optimisation, vol 294. Wiley, New York
57.
Fister I Jr, Fister D, Fister I (2013) A comprehensive review of cuckoo search: variants and hybrids. Int J Math Model Numer Optim 4(4):387–409MATH
58.
Qu C, He W (2015) A double mutation Cuckoo Search algorithm for solving systems of nonlinear equations. Int J Hybrid Inf Technol 8(12):433–448
59.
Wu Y-C, Lee W-P, Chien C-W (2011) Modified the performance of differential evolution algorithm with dual evolution strategy. In: International conference on machine learning and computing, pp 57–63
60.
61.
Bai Q (2010) Analysis of particle swarm optimisation algorithm. Comput Inf Sci 3(1):180
62.
63.
Ali N, Othman MA, Husain MN, Misran MH (2014) A review of firefly algorithm. ARPN J Eng Appl Sci 9(10):1732–1736
64.
Selvi V, Umarani DR (2010) Comparative analysis of ant colony and particle swarm optimisation techniques. Int J Comput Appl (0975–8887) 5(4):1–6
65.
66.
Mohan N, Sivaraj R, Priya RD (2016) A comprehensive review of bat algorithm and its applications to various optimisation problems. Asian J Res Soc Sci Humanit 6(11):676–690
67.
Xiao-hua S, Chun-ming Y (2013) Application of bat algorithm to permutation flow-shop scheduling problem. Ind Eng J 1:022
68.
Yuce B, Packianather MS, Mastrocinque E, Pham DT, Lambiase A (2013) Honey bees inspired optimisation method: the bees algorithm. Insects 4(4):646–662
69.
Balasubramani K, Marcus K (2014) A study on flower pollination algorithm and its applications. Int J Appl Innov Eng Manag 3(11):230–235
70.
Wang Y, Li D, Lu Y, Cheng Z, Gao Y (2017) Improved flower pollination algorithm based on mutation strategy. In: Intelligent human–machine systems and cybernetics (IHMSC), 2017 9th international conference on, 2017, pp 337–342
71.
Yan G, Li C (2011) An effective refinement artificial bee colony optimisation algorithm based on chaotic search and application for PID control tuning. J Comput Inf Syst 7(9):3309–3316
72.
Ahmadi M, Mojallali H (2012) Chaotic invasive weed optimisation algorithm with application to parameter estimation of chaotic systems. Chaos Solitons Fractals 45(9–10):1108–1120MathSciNet
73.
Toksari MD (2016) A hybrid algorithm of Ant Colony Optimisation (ACO) and Iterated Local Search (ILS) for estimating electricity domestic consumption: case of Turkey. Int J Electr Power Energy Syst 78:776–782
74.
Metadata
Title
A conceptual comparison of several metaheuristic algorithms on continuous optimisation problems
Authors
Absalom E. Ezugwu
Olawale J. Adeleke
Andronicus A. Akinyelu
Serestina Viriri
Publication date
13-03-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 10/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04132-w

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