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An Enhanced Social Spider Colony Optimization for Global Optimization

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Networking, Intelligent Systems and Security

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 237))

Abstract

An improved variant of the social spider optimization algorithm is introduced. It is inspired by the hunting and mating behaviors of spiders in nature. We call it enhanced social spider colony optimization (ESSCO). The performance of ESSCO is evaluated using the benchmark CEC 2020. To validate the proposed algorithm, the obtained statistical results are compared to eleven recent state-of-the-art metaheuristic algorithms. The comparative study shows the competitiveness of ESSCO in finding efficient solutions to the considered test functions.

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References

  1. Abbass, H.A.: Mbo: Marriage in honey bees optimization-a haplometrosis polygynous swarming approach. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546). vol. 1, pp. 207–214. IEEE (2001)

    Google Scholar 

  2. Alatas, B.: Acroa: artificial chemical reaction optimization algorithm for global optimization. Expert Syst. Appl. 38(10), 13170–13180 (2011)

    Article  Google Scholar 

  3. Artin, E.: The Gamma Function. Courier Dover Publications (2015)

    Google Scholar 

  4. Ashby, W.R.: Principles of the self-organizing system. In: Facets of Systems Science, pp. 521–536. Springer (1991)

    Google Scholar 

  5. Askarzadeh, A., Rezazadeh, A.: A new heuristic optimization algorithm for modeling of proton exchange membrane fuel cell: bird mating optimizer. Int. J. Energy Res. 37(10), 1196–1204 (2013)

    Article  Google Scholar 

  6. Aviles, L.: Sex-ratio bias and possible group selection in the social spider Anelosimus eximius. Am. Nat. 128(1), 1–12 (1986)

    Article  Google Scholar 

  7. Barthelemy, P., Bertolotti, J., Wiersma, D.S.: A lévy flight for light. Nature 453(7194), 495–498 (2008)

    Article  Google Scholar 

  8. Basturk, B.: An artificial bee colony (abc) algorithm for numeric function optimization. In: IEEE Swarm Intelligence Symposium, Indianapolis, IN, USA, 2006 (2006)

    Google Scholar 

  9. Bergmann, H.W.: Optimization: Methods and Applications, Possibilities and Limitations: Proceedings of an International Seminar Organized by Deutsche Forschungsanstalt Für Luft-und Raumfahrt (DLR), Bonn, June 1989, vol. 47. Springer Science & Business Media (2012)

    Google Scholar 

  10. Biswas, P.P., Suganthan, P.N.: Large initial population and neighborhood search incorporated in lshade to solve cec2020 benchmark problems. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–7. IEEE (2020)

    Google Scholar 

  11. Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. (CSUR) 35(3), 268–308 (2003)

    Article  Google Scholar 

  12. Blum, C., Roli, A.: Hybrid metaheuristics: an introduction. In: Hybrid Metaheuristics, pp. 1–30. Springer, Berlin (2008)

    Google Scholar 

  13. Bolufé-Röhler, A., Chen, S.: A multi-population exploration-only exploitation-only hybrid on cec-2020 single objective bound constrained problems. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2020)

    Google Scholar 

  14. Borenstein, Y., Moraglio, A.: Theory and Principled Methods for the Design of Metaheuristics. Springer, Berlin (2014)

    Book  MATH  Google Scholar 

  15. Brest, J., Maučec, M.S., Bošković, B.: Differential evolution algorithm for single objective bound-constrained optimization: algorithm j2020. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2020)

    Google Scholar 

  16. Bujok, P., Kolenovsky, P., Janisch, V.: Eigenvector crossover in jde100 algorithm. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–6. IEEE (2020)

    Google Scholar 

  17. Černỳ, V.: Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J. Opt. Theor. Appl. 45(1), 41–51 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  18. Ciarleglio, M.I.: Modular abstract self-learning tabu search (masts): Metaheuristic search theory and practice (2008)

    Google Scholar 

  19. Cuevas, E., Cienfuegos, M., ZaldíVar, D., Pérez-Cisneros, M.: A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst. Appl. 40(16), 6374–6384 (2013)

    Article  Google Scholar 

  20. Dorigo, M., Birattari, M.: Ant colony optimization. In: Sammut, C., Webb, G. I. (Eds.) Encyclopedia of Machine Learning and Data Mining, pp. 56–59. Springer US, Boston, MA (2017), ISBN:978-1-4899-7687-1. https://doi.org/10.1007/978-1-4899-7687-1_22.

  21. Dorigo, M., Di Caro, G.: Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406). vol. 2, pp. 1470–1477. IEEE (1999)

    Google Scholar 

  22. Du, H., Wu, X., Zhuang, J.: Small-world optimization algorithm for function optimization. In: International Conference on Natural Computation, pp. 264–273. Springer, Berlin (2006)

    Google Scholar 

  23. Elias, D.O., Andrade, M.C., Kasumovic, M.M.: Dynamic population structure and the evolution of spider mating systems. In: Advances in Insect Physiology, vol. 41, pp. 65–114. Elsevier (2011)

    Google Scholar 

  24. Erol, O.K., Eksin, I.: A new optimization method: big bang-big crunch. Adv. Eng. Softw. 37(2), 106–111 (2006)

    Article  Google Scholar 

  25. Eshelman, L.J.: Crossover operator biases: exploiting the population distribution. In: Proceedings of International Conference on Genetic Algorithms, 1997 (1997)

    Google Scholar 

  26. Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval-schemata. In: Foundations of Genetic Algorithms, vol. 2, pp. 187–202. Elsevier (1993)

    Google Scholar 

  27. Fogel, D.B.: Artificial intelligence through simulated evolution. Wiley-IEEE Press (1998)

    Google Scholar 

  28. Formato, R.: Central force optimization: a new metaheuristic with applications in applied electromagnetics. prog electromagn res 77: 425–491 (2007)

    Google Scholar 

  29. Gandomi, A.H., Alavi, A.H.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  30. Glover, F.W., Kochenberger, G.A.: Handbook of metaheuristics, vol. 57. Springer Science & Business Media (2006)

    Google Scholar 

  31. Hatamlou, A.: Black hole: a new heuristic optimization approach for data clustering. Inf. Sci. 222, 175–184 (2013)

    Article  MathSciNet  Google Scholar 

  32. Helbig, M., Engelbrecht, A.P.: Population-based metaheuristics for continuous boundary-constrained dynamic multi-objective optimisation problems. Swarm Evol. Comput. 14, 31–47 (2014)

    Article  Google Scholar 

  33. Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–73 (1992)

    Article  Google Scholar 

  34. Jou, Y.C., Wang, S.Y., Yeh, J.F., Chiang, T.C.: Multi-population modified l-shade for single objective bound constrained optimization. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2020)

    Google Scholar 

  35. Joyce, T., Herrmann, J.M.: A review of no free lunch theorems, and their implications for metaheuristic optimisation. In: Nature-inspired algorithms and applied optimization, pp. 27–51. Springer, Berlin (2018)

    Google Scholar 

  36. Kadavy, T., Pluhacek, M., Viktorin, A., Senkerik, R.: Soma-cl for competition on single objective bound constrained numerical optimization benchmark: a competition entry on single objective bound constrained numerical optimization at the genetic and evolutionary computation conference (gecco) 2020. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 9–10 (2020)

    Google Scholar 

  37. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  38. Kaur, S., Awasthi, L.K., Sangal, A., Dhiman, G.: Tunicate swarm algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng. Appl. Artif. Intell. 90, 103541 (2020)

    Article  Google Scholar 

  39. Kaveh, A., Farhoudi, N.: A new optimization method: Dolphin echolocation. Adv. Eng. Softw. 59, 53–70 (2013)

    Article  Google Scholar 

  40. Kaveh, A., Khayatazad, M.: A new meta-heuristic method: ray optimization. Comput. Struct. 112, 283–294 (2012)

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  42. Keller, E.F.: Organisms, machines, and thunderstorms: a history of self-organization, part two. complexity, emergence, and stable attractors. Hist. Stud. Natural Sci. 39(1), 1–31 (2009)

    Google Scholar 

  43. Kennedy, J., et al.: Encyclopedia of machine learning. Particle Swarm Optim. 760–766 (2010)

    Google Scholar 

  44. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  45. Koza, J.R., Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection, vol. 1. MIT press (1992)

    Google Scholar 

  46. Koziel, S., Yang, X.S.: Computational optimization, methods and algorithms, vol. 356. Springer, Berlin (2011)

    Book  MATH  Google Scholar 

  47. Kumar, A., Misra, R.K., Singh, D., Mishra, S., Das, S.: The spherical search algorithm for bound-constrained global optimization problems. Appl. Soft Comput. 85, 105734 (2019)

    Article  Google Scholar 

  48. Li, X.: A new intelligent optimization-artificial fish swarm algorithm. Doctor thesis, Zhejiang University of Zhejiang, China (2003)

    Google Scholar 

  49. Lu, X., Zhou, Y.: A novel global convergence algorithm: bee collecting pollen algorithm. In: International Conference on Intelligent Computing, pp. 518–525. Springer, Berlin (2008)

    Google Scholar 

  50. Lubin, Y., Bilde, T.: The evolution of sociality in spiders. Adv. Study Behavior 37, 83–145 (2007)

    Article  Google Scholar 

  51. Mantegna, R.N.: Fast, accurate algorithm for numerical simulation of levy stable stochastic processes. Phys. Rev. E 49(5), 4677 (1994)

    Article  Google Scholar 

  52. Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27(4), 1053–1073 (2016)

    Article  MathSciNet  Google Scholar 

  53. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  54. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  55. Moghaddam, F.F., Moghaddam, R.F., Cheriet, M.: Curved space optimization: a random search based on general relativity theory. arXiv preprint arXiv:1208.2214 (2012)

  56. Mohamed, A.W., Hadi, A.A., Mohamed, A.K., Awad, N.H.: Evaluating the performance of adaptive gainingsharing knowledge based algorithm on cec 2020 benchmark problems. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2020)

    Google Scholar 

  57. Molina, J., Rudnick, H.: Transmission expansion plan: Ordinal and metaheuristic multiobjective optimization. In: 2011 IEEE Trondheim PowerTech, pp. 1–6. IEEE (2011)

    Google Scholar 

  58. Mucherino, A., Seref, O.: Monkey search: a novel metaheuristic search for global optimization. In: AIP Conference Proceedings, vol. 953, pp. 162–173. AIP (2007)

    Google Scholar 

  59. Murata, K., Tanaka, K.: Spatial interaction between spiders and prey insects: horizontal and vertical distribution in a paddy field. Acta arachnologica 53(2), 75–86 (2004)

    Article  Google Scholar 

  60. Oftadeh, R., Mahjoob, M., Shariatpanahi, M.: A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput. Math. Appl. 60(7), 2087–2098 (2010)

    Article  MATH  Google Scholar 

  61. Pan, W.T.: A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl.-Based Syst. 26, 69–74 (2012)

    Article  Google Scholar 

  62. Puchinger, J., Raidl, G.R.: Combining metaheuristics and exact algorithms in combinatorial optimization: a survey and classification. In: International work-conference on the interplay between natural and artificial computation, pp. 41–53. Springer, Berlin (2005)

    Google Scholar 

  63. Rajeev, S., Krishnamoorthy, C.: Discrete optimization of structures using genetic algorithms. J. Struct. Eng. 118(5), 1233–1250 (1992)

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  65. Roth, M., Wicker, S.: Termite: A swarm intelligent routing algorithm for mobile wireless ad-hoc networks. In: Stigmergic Optimization, pp. 155–184. Springer (2006)

    Google Scholar 

  66. Salgotra, R., Singh, U., Saha, S., Gandomi, A.H.: Improving cuckoo search: incorporating changes for CEC 2017 and CEC 2020 benchmark problems. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–7. IEEE (2020)

    Google Scholar 

  67. Sallam, K.M., Elsayed, S.M., Chakrabortty, R.K., Ryan, M.J.: Improved multi-operator differential evolution algorithm for solving unconstrained problems. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2020)

    Google Scholar 

  68. Salomon, M., Sponarski, C., Larocque, A., Avilés, L.: Social organization of the colonial spider leucauge sp. in the neotropics: vertical stratification within colonies. J. Arachnology 38(3), 446–451 (2010)

    Google Scholar 

  69. Shah-Hosseini, H.: Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Int. J. Comput. Sci. Eng. 6(1–2), 132–140 (2011)

    Google Scholar 

  70. Shi, J., Zhang, Q.: A new cooperative framework for parallel trajectory-based metaheuristics. App. Soft Comput. 65, 374–386 (2018)

    Article  Google Scholar 

  71. Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), vol. 3, pp. 1945–1950. IEEE (1999)

    Google Scholar 

  72. Shiqin, Y., Jianjun, J., Guangxing, Y.: A dolphin partner optimization. In: 2009 WRI Global Congress on Intelligent Systems, vol. 1, pp. 124–128. IEEE (2009)

    Google Scholar 

  73. Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)

    Article  Google Scholar 

  74. Stanovov, V., Akhmedova, S., Semenkin, E.: Ranked archive differential evolution with selective pressure for CEC 2020 numerical optimization. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–7. IEEE (2020)

    Google Scholar 

  75. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  76. Talbi, E.G., Jourdan, L., Garcia-Nieto, J., Alba, E.: Comparison of population based metaheuristics for feature selection: Application to microarray data classification. In: 2008 IEEE/ACS International Conference on Computer Systems and Applications, pp. 45–52. IEEE (2008)

    Google Scholar 

  77. Talbi, H., Draa, A.: A new real-coded quantum-inspired evolutionary algorithm for continuous optimization. Appl. Soft Comput. 61, 765–791 (2017)

    Article  Google Scholar 

  78. Tang, K.S., Man, K.F., Kwong, S., He, Q.: Genetic algorithms and their applications. IEEE Signal Process. Mag. 13(6), 22–37 (1996)

    Article  Google Scholar 

  79. Van Laarhoven, P.J., Aarts, E.H.: Simulated annealing. In: Simulated Annealing: Theory and Applications, pp. 7–15. Springer, Berlin (1987)

    Google Scholar 

  80. Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T., Zamuda, A.: Dish-xx solving cec2020 single objective bound constrained numerical optimization benchmark. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2020)

    Google Scholar 

  81. Vollrath, F., Rohde-Arndt, D.: Prey capture and feeding in the social spider Anelosimus eximius. Zeitschrift für Tierpsychologie 61(4), 334–340 (1983)

    Article  Google Scholar 

  82. Webster, B., Philip, J., Bernhard, A.: Local search optimization algorithm based on natural principles of gravitation, ike’03, las vegas, Nevada, USA (2003, June)

    Google Scholar 

  83. Yang, C., Tu, X., Chen, J.: Algorithm of marriage in honey bees optimization based on the wolf pack search. In: The 2007 International Conference on Intelligent Pervasive Computing (IPC 2007), pp. 462–467. IEEE (2007)

    Google Scholar 

  84. Yang, X.S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, Hoboken (2010)

    Book  Google Scholar 

  85. Yang, X.S.: Firefly algorithm, stochastic test functions and design optimisation. arXiv preprint arXiv:1003.1409 (2010)

  86. Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2010)

    Google Scholar 

  87. Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Berlin (2010)

    Google Scholar 

  88. Yang, X.S.: Swarm-based metaheuristic algorithms and no-free-lunch theorems. Theor. New Appl. Swarm Intell. 9, 1–16 (2012)

    Google Scholar 

  89. Yang, X.S.: Optimization and metaheuristic algorithms in engineering. In Metaheuristics in Water, Geotechnical and Transport Engineering, pp. 1–23 (2013)

    Google Scholar 

  90. Yang, X.S., Deb, S.: Cuckoo search via lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 210–214. IEEE (2009)

    Google Scholar 

  91. Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)

    Article  Google Scholar 

  92. Zitouni, F., Harous, S., Maamri, R.: The solar system algorithm: a novel metaheuristic method for global optimization. IEEE Access (2020)

    Google Scholar 

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Acknowledgements

This research work is supported by UAEU Grant: 31T102-UPAR-1-2017.

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Zitouni, F., Harous, S., Maamri, R. (2022). An Enhanced Social Spider Colony Optimization for Global Optimization. In: Ben Ahmed, M., Teodorescu, HN.L., Mazri, T., Subashini, P., Boudhir, A.A. (eds) Networking, Intelligent Systems and Security. Smart Innovation, Systems and Technologies, vol 237. Springer, Singapore. https://doi.org/10.1007/978-981-16-3637-0_55

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