research-article

Analyzing evolutionary optimization and community detection algorithms using regression line dominance

Authors:

Bhaskar BiswasAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 396, Issue C

Pages 185 - 201

https://doi.org/10.1016/j.ins.2017.02.050

Published: 01 August 2017 Publication History

Abstract

Developed regression line dominance and shifting mechanism.Proposed visual analysis method for evolutionary optimization algorithms.Community detection algorithms are also analyzed with proposed method.Developed one-to-one, one-to-many and many-to-many comparison methodology.Experimented with 25 benchmark functions and 10 real-world networks. In this paper, a visual analysis methodology is proposed to perform comparative analysis of guided random algorithms such as evolutionary optimization algorithms and community detection algorithms. Proposed methodology is designed based on quantile-quantile plot and regression analysis to compare performance of one algorithm over other algorithms. The methodology is extrapolated as one-to-one comparison, one-to-many comparison and many-to-many comparison of solution quality and convergence rate. Most of the existing approaches utilize both solution quality and convergence rate to perform comparative analysis. However, the many-to-many comparison i.e. ranking of algorithms is done only with solution quality. On the contrary, with proposed methodology ranking of algorithms is done in terms of both solution quality and convergence rate. Proposed methodology is studied with four evolutionary optimization algorithms on 25 benchmark functions. A non-parametric statistical analysis called Wilcoxon signed-rank test is also performed to verify the indication of proposed methodology. Moreover, methodology is also applied to analyze four state-of-the-art community detection algorithms on 10 real-world networks.

References

[1]

A. Auger, N. Hansen, A restart cma evolution strategy with increasing population size, IEEE, 2005.

[2]

J. Bergstra, Y. Bengio, Random search for hyper-parameter optimization, J. Mach. Learn. Res., 13 (2012) 281-305.

Digital Library

[3]

A. Biswas, B. Biswas, Visual analysis of evolutionary optimization algorithms, 2014.

[4]

A. Biswas, B. Biswas, Investigating community structure in perspective of ego network, Expert Syst. Appl., 42 (2015) 6913-6934.

Digital Library

[5]

A. Biswas, B. Biswas, Regression line shifting mechanism for analyzing evolutionary optimization algorithms, Soft Comput. (2016) 1-16.

[6]

U. Brandes, M. Gaertler, D. Wagner, Experiments on graph clustering algorithms, in: Lecture Notes in Computer Science, 2832, Springer Berlin Heidelberg, 2003, pp. 568-579.

[7]

U. Brandes, M. Gaertler, D. Wagner, Engineering graph clustering, J. Exp. Algorith., 12 (2008) 26.

[8]

Q. Cai, L. Ma, M. Gong, D. Tian, A survey on network community detection based on evolutionary computation, Int. J. Bio-Inspired Comput., 8 (2016) 84-98.

Digital Library

[9]

E.G. Carrano, R.H. Takahashi, E.F. Wanner, An enhanced statistical approach for evolutionary algorithm comparison, ACM, New York, NY, USA, 2008.

[10]

L. Cuadra, A. Aybar-Ruz, M. del Arco, J. Navo-Marco, J. Portilla-Figueras, S. Salcedo-Sanz, A lamarckian hybrid grouping genetic algorithm with repair heuristics for resource assignment in wcdma networks, Appl. Soft Comput., 43 (2016) 619-632.

Digital Library

[11]

A. Czarn, C. MacNish, K. Vijayan, B. Turlach, R. Gupta, Statistical exploratory analysis of genetic algorithms, Evol. Comput. IEEE Trans., 8 (2004) 405-421.

Digital Library

[12]

J. Derrac, S. Garca, D. Molina, F. Herrera, A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm Evol. Comput., 1 (2011) 3-18.

[13]

T. Flanders, Speakers give sound advice, syracuse post standard, 1911.

[14]

O. Francois, C. Lavergne, Design of evolutionary algorithms-a statistical perspective, Evol. Comput. IEEE Trans., 5 (2001) 129-148.

Digital Library

[15]

S. Garca, D. Molina, M. Lozano, F. Herrera, An experimental study on the use of non-parametric tests for analyzing the behaviour of evolutionary algorithms in optimization problems, 2007.

[16]

S. Garca, D. Molina, M. Lozano, F. Herrera, A study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: a case study on the cec2005 special session on real parameter optimization, J. Heuristics, 15 (2009) 617-644.

Digital Library

[17]

P. Ghosh, M. Mitchell, J.A. Tanyi, A.Y. Hung, Incorporating priors for medical image segmentation using a genetic algorithm, Neurocomputing, 195 (2016) 181-194.

Digital Library

[18]

M. Girvan, M.E. Newman, Community structure in social and biological networks, Proc. Natl. Acad. Sci., 99 (2002) 7821-7826.

[19]

P.M. Gleiser, L. Danon, Community structure in jazz, Adv. Complex Syst., 6 (2003) 565-573.

[20]

S.M.M. Golsefid, M.H.F. Zarandi, S. Bastani, Fuzzy community detection model in social networks, Int. J. Intell. Syst., 30 (2015) 1227-1244.

Digital Library

[21]

R. Guimera, L. Danon, A. Diaz-Guilera, F. Giralt, A. Arenas, Self-similar community structure in a network of human interactions, Phys. Rev. E, 68 (2003) 065103.

[22]

N. Hansen, Benchmarking a bi-population cma-es on the bbob-2009 function testbed, ACM, 2009.

Digital Library

[23]

J. He, T. Chen, Novel analysis of population scalability in evolutionary algorithms, CoRR (2013).

[24]

J. He, X. Yao, Drift analysis and average time complexity of evolutionary algorithms, Artif. Intell., 127 (2001) 57-85.

Digital Library

[25]

Idiom, One Look Is Worth a Thousand Words, Piqua Leader-Dispatch, 1913.

[26]

D.E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley Reading, 1993.

[27]

V. Krebs, Proxy networksanalyzing one network to reveal another, Bulletin de Mthodologie Sociologique, 79 (2003) 61-70.

[28]

B. Lacroix, D. Molina, F. Herrera, Region based memetic algorithm with ls chaining, IEEE, 2012.

[29]

B. Lacroix, D. Molina, F. Herrera, Dynamically updated region based memetic algorithm for the 2013 cec special session and competition on real parameter single objective optimization, IEEE, 2013.

[30]

T. Liao, T. Stuetzle, Benchmark results for a simple hybrid algorithm on the cec 2013 benchmark set for real-parameter optimization, IEEE, 2013.

[31]

A. Lockett, Measure-theoretic analysis of performance in evolutionary algorithms, 2013.

[32]

I. Loshchilov, Cma-es with restarts for solving cec 2013 benchmark problems, Ieee, 2013.

[33]

I. Loshchilov, M. Schoenauer, M. Sebag, Alternative restart strategies for cma-es, Springer, 2012.

[34]

I. Loshchilov, T. Stuetzle, T. Liao, Ranking results of cec13 special session & competition on real-parameter single objective optimization, 2013.

[35]

H.R. Loureno, O.C. Martin, T. Sttzle, Iterated local search: framework and applications, Springer, 2010.

[36]

D. Lusseau, K. Schneider, O.J. Boisseau, P. Haase, E. Slooten, S.M. Dawson, The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations, Behav. Ecol. Sociobiol., 54 (2003) 396-405.

[37]

E. Lutton, J.-D. Fekete, Visual Analytics and Experimental Analysis of Evolutionary Algorithms, INRIA, 2011.

[38]

L. Marques, U. Nunes, A.T. de Almeida, Particle swarm-based olfactory guided search, Auton. Robots, 20 (2006) 277-287.

Digital Library

[39]

O. Mersmann, M. Preuss, H. Trautmann, Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis, Springer, 2010.

[40]

J.H. Michael, Labor dispute reconciliation in a forest products manufacturing facilityl, Forest Products J., 47 (1997) 41-45.

[41]

J.H. Michael, J.G. Massey, Modeling the communication network in a sawmill, Forest Products J., 47 (1997) 25.

[42]

J. Moreno-Prez, C. Campos-Rodrguez, M. Laguna, On the comparison of metaheuristics through non-parametric statistical techniques, 2007.

[43]

H. Mhlenbein, T. Mahnig, Mathematical analysis of evolutionary algorithms for optimization (2001).

[44]

M.E. Newman, Finding community structure in networks using the eigenvectors of matrices, Phys. Rev. E, 74 (2006) 036104.

[45]

S. Nijssen, T. Back, An analysis of the behavior of simplified evolutionary algorithms on trap functions, Evol. Comput. IEEE Trans., 7 (2003) 11-22.

Digital Library

[46]

I. Rojas, J. Gonzalez, H. Pomares, J. Merelo, P. Castillo, G. Romero, Statistical analysis of the main parameters involved in the design of a genetic algorithm, Syst. Man Cybern. Part C: Appl. Rev. IEEE Trans., 32 (2002) 31-37.

Digital Library

[47]

D. Shilane, J. Martikainen, S. Dudoit, S.J. Ovaska, A general framework for statistical performance comparison of evolutionary computation algorithms, Inf. Sci., 178 (2008) 2870-2879.

Digital Library

[48]

A. Strehl, J. Ghosh, Cluster ensemblesa knowledge reuse framework for combining multiple partitions, J. Mach. Learn. Res., 3 (2002) 583-617.

Digital Library

[49]

J. Su, T.C. Havens, Fuzzy community detection in social networks using a genetic algortihm, IEEE, 2014.

[50]

J. Su, T.C. Havens, Quadratic program-based modularity maximization for fuzzy community detection in social networks, IEEE Trans. Fuzzy Syst., 23 (2015) 1356-1371.

[51]

P.N. Suganthan, N. Hansen, J.J. Liang, K. Deb, Y.-P. Chen, A. Auger, S. Tiwari, Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization, KanGAL Report, 2005005 (2005).

[52]

R. Tanabe, A. Fukunaga, Success-history based parameter adaptation for differential evolution, IEEE, 2013.

[53]

R.K. Ursem, Diversity-guided evolutionary algorithms, Springer, 2002.

[54]

D.-J. Wang, F. Liu, Y. Jin, A multi-objective evolutionary algorithm guided by directed search for dynamic scheduling, Comput. Oper. Res. (2016).

Digital Library

[55]

A. Wu, K. De Jong, D. Burke, J. Grefenstette, C. Loggia Ramsey, Visual analysis of evolutionary algorithms, 1999.

Digital Library

[56]

X.-S. Yang, Metaheuristic optimization: algorithm analysis and open problems, in: Lecture Notes in Computer Science, 6630, Springer Berlin Heidelberg, 2011, pp. 21-32.

Digital Library

[57]

W.W. Zachary, An information flow model for conflict and fission in small groups, J. Anthropol. Res. (1977) 452-473.

[58]

J.H. Zar, Biostatistical Analysis, 4th, New Jersey, USA (1999) 929.

[59]

C. Zhang, X. Hei, D. Yang, L. Wang, A memetic particle swarm optimization algorithm for community detection in complex networks, Int. J. Pattern Recognit. Artif. Intell., 30 (2016) 1659003.

[60]

Q. Zhang, J. Sun, E. Tsang, An evolutionary algorithm with guided mutation for the maximum clique problem, IEEE Trans. Evol. Comput., 9 (2005) 192-200.

Digital Library

Cited By

Golzardi ESheikhahmadi AAbdollahpouri A(2023)TRTCD: trust route prediction based on trusted community detectionMultimedia Tools and Applications10.1007/s11042-023-15096-482:27(41571-41607)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s11042-023-15096-4
Xiao JWang YXu X(2022)Fuzzy Community Detection Based on Elite Symbiotic Organisms Search and Node Neighborhood InformationIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2021.308723530:7(2500-2514)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1109/TFUZZ.2021.3087235
Wang RJia ZLi K(2021)Scheduling parallel-batching processing machines problem with learning and deterioration effect in fuzzy environmentJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21019640:6(12111-12124)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/JIFS-210196
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Analyzing evolutionary optimization and community detection algorithms using regression line dominance
1. Theory of computation
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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 396, Issue C

August 2017

218 pages

ISSN:0020-0255

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Copyright © Elsevier Inc.

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Elsevier Science Inc.

United States

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Published: 01 August 2017

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Cited By

Golzardi ESheikhahmadi AAbdollahpouri A(2023)TRTCD: trust route prediction based on trusted community detectionMultimedia Tools and Applications10.1007/s11042-023-15096-482:27(41571-41607)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s11042-023-15096-4
Xiao JWang YXu X(2022)Fuzzy Community Detection Based on Elite Symbiotic Organisms Search and Node Neighborhood InformationIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2021.308723530:7(2500-2514)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1109/TFUZZ.2021.3087235
Wang RJia ZLi K(2021)Scheduling parallel-batching processing machines problem with learning and deterioration effect in fuzzy environmentJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21019640:6(12111-12124)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/JIFS-210196
Singh SMishra SKumar ABiswas B(2020)CLP-IDInformation Sciences: an International Journal10.1016/j.ins.2019.11.026514:C(402-433)Online publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1016/j.ins.2019.11.026
Guo KHe LChen YGuo WZheng J(2019)A local community detection algorithm based on internal force between nodesApplied Intelligence10.1007/s10489-019-01541-150:2(328-340)Online publication date: 24-Jul-2019
https://dl.acm.org/doi/10.1007/s10489-019-01541-1
Li SCheng C(2017)Particle swarm optimization with fitness adjustment parametersComputers and Industrial Engineering10.1016/j.cie.2017.06.006113:C(831-841)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.cie.2017.06.006

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