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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Alatas, B.: Acroa: artificial chemical reaction optimization algorithm for global optimization. Expert Syst. Appl. 38(10), 13170–13180 (2011)
Artin, E.: The Gamma Function. Courier Dover Publications (2015)
Ashby, W.R.: Principles of the self-organizing system. In: Facets of Systems Science, pp. 521–536. Springer (1991)
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)
Aviles, L.: Sex-ratio bias and possible group selection in the social spider Anelosimus eximius. Am. Nat. 128(1), 1–12 (1986)
Barthelemy, P., Bertolotti, J., Wiersma, D.S.: A lévy flight for light. Nature 453(7194), 495–498 (2008)
Basturk, B.: An artificial bee colony (abc) algorithm for numeric function optimization. In: IEEE Swarm Intelligence Symposium, Indianapolis, IN, USA, 2006 (2006)
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)
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)
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. (CSUR) 35(3), 268–308 (2003)
Blum, C., Roli, A.: Hybrid metaheuristics: an introduction. In: Hybrid Metaheuristics, pp. 1–30. Springer, Berlin (2008)
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)
Borenstein, Y., Moraglio, A.: Theory and Principled Methods for the Design of Metaheuristics. Springer, Berlin (2014)
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)
Bujok, P., Kolenovsky, P., Janisch, V.: Eigenvector crossover in jde100 algorithm. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–6. IEEE (2020)
Černỳ, V.: Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J. Opt. Theor. Appl. 45(1), 41–51 (1985)
Ciarleglio, M.I.: Modular abstract self-learning tabu search (masts): Metaheuristic search theory and practice (2008)
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)
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.
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)
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)
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)
Erol, O.K., Eksin, I.: A new optimization method: big bang-big crunch. Adv. Eng. Softw. 37(2), 106–111 (2006)
Eshelman, L.J.: Crossover operator biases: exploiting the population distribution. In: Proceedings of International Conference on Genetic Algorithms, 1997 (1997)
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)
Fogel, D.B.: Artificial intelligence through simulated evolution. Wiley-IEEE Press (1998)
Formato, R.: Central force optimization: a new metaheuristic with applications in applied electromagnetics. prog electromagn res 77: 425–491 (2007)
Gandomi, A.H., Alavi, A.H.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012)
Glover, F.W., Kochenberger, G.A.: Handbook of metaheuristics, vol. 57. Springer Science & Business Media (2006)
Hatamlou, A.: Black hole: a new heuristic optimization approach for data clustering. Inf. Sci. 222, 175–184 (2013)
Helbig, M., Engelbrecht, A.P.: Population-based metaheuristics for continuous boundary-constrained dynamic multi-objective optimisation problems. Swarm Evol. Comput. 14, 31–47 (2014)
Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–73 (1992)
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)
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)
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)
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)
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)
Kaveh, A., Farhoudi, N.: A new optimization method: Dolphin echolocation. Adv. Eng. Softw. 59, 53–70 (2013)
Kaveh, A., Khayatazad, M.: A new meta-heuristic method: ray optimization. Comput. Struct. 112, 283–294 (2012)
Kaveh, A., Talatahari, S.: A novel heuristic optimization method: charged system search. Acta Mechanica 213(3–4), 267–289 (2010)
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)
Kennedy, J., et al.: Encyclopedia of machine learning. Particle Swarm Optim. 760–766 (2010)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Koza, J.R., Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection, vol. 1. MIT press (1992)
Koziel, S., Yang, X.S.: Computational optimization, methods and algorithms, vol. 356. Springer, Berlin (2011)
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)
Li, X.: A new intelligent optimization-artificial fish swarm algorithm. Doctor thesis, Zhejiang University of Zhejiang, China (2003)
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)
Lubin, Y., Bilde, T.: The evolution of sociality in spiders. Adv. Study Behavior 37, 83–145 (2007)
Mantegna, R.N.: Fast, accurate algorithm for numerical simulation of levy stable stochastic processes. Phys. Rev. E 49(5), 4677 (1994)
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)
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
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)
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)
Molina, J., Rudnick, H.: Transmission expansion plan: Ordinal and metaheuristic multiobjective optimization. In: 2011 IEEE Trondheim PowerTech, pp. 1–6. IEEE (2011)
Mucherino, A., Seref, O.: Monkey search: a novel metaheuristic search for global optimization. In: AIP Conference Proceedings, vol. 953, pp. 162–173. AIP (2007)
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)
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)
Pan, W.T.: A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl.-Based Syst. 26, 69–74 (2012)
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)
Rajeev, S., Krishnamoorthy, C.: Discrete optimization of structures using genetic algorithms. J. Struct. Eng. 118(5), 1233–1250 (1992)
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)
Roth, M., Wicker, S.: Termite: A swarm intelligent routing algorithm for mobile wireless ad-hoc networks. In: Stigmergic Optimization, pp. 155–184. Springer (2006)
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)
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)
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)
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)
Shi, J., Zhang, Q.: A new cooperative framework for parallel trajectory-based metaheuristics. App. Soft Comput. 65, 374–386 (2018)
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)
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)
Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)
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)
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)
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)
Talbi, H., Draa, A.: A new real-coded quantum-inspired evolutionary algorithm for continuous optimization. Appl. Soft Comput. 61, 765–791 (2017)
Tang, K.S., Man, K.F., Kwong, S., He, Q.: Genetic algorithms and their applications. IEEE Signal Process. Mag. 13(6), 22–37 (1996)
Van Laarhoven, P.J., Aarts, E.H.: Simulated annealing. In: Simulated Annealing: Theory and Applications, pp. 7–15. Springer, Berlin (1987)
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)
Vollrath, F., Rohde-Arndt, D.: Prey capture and feeding in the social spider Anelosimus eximius. Zeitschrift für Tierpsychologie 61(4), 334–340 (1983)
Webster, B., Philip, J., Bernhard, A.: Local search optimization algorithm based on natural principles of gravitation, ike’03, las vegas, Nevada, USA (2003, June)
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)
Yang, X.S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, Hoboken (2010)
Yang, X.S.: Firefly algorithm, stochastic test functions and design optimisation. arXiv preprint arXiv:1003.1409 (2010)
Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2010)
Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Berlin (2010)
Yang, X.S.: Swarm-based metaheuristic algorithms and no-free-lunch theorems. Theor. New Appl. Swarm Intell. 9, 1–16 (2012)
Yang, X.S.: Optimization and metaheuristic algorithms in engineering. In Metaheuristics in Water, Geotechnical and Transport Engineering, pp. 1–23 (2013)
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)
Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)
Zitouni, F., Harous, S., Maamri, R.: The solar system algorithm: a novel metaheuristic method for global optimization. IEEE Access (2020)
Acknowledgements
This research work is supported by UAEU Grant: 31T102-UPAR-1-2017.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-16-3637-0_55
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3636-3
Online ISBN: 978-981-16-3637-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)