An adaptive bio-inspired optimisation model based on the foraging behaviour of a social spider

References

• Abbass, H. A. (2001). MBO: Marriage in honey bees optimization a haplometrosis polygynous swarm approach. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546) Seoul, South Korea. 207–18. doi: 10.1109/CEC.2001.934391.
   Google Scholar
• Afshar, A., Haddad, B. O., Marino, M., & Adams, B. (2007). Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation. Journal of the Franklin Institute, 344, 452–462. doi:10.1016/j.jfranklin.2006.06.001
   Web of Science ®Google Scholar
• Benyus, J. M. (2002). Biomimicry: Innovation inspired by nature. New York: Perennial.
   Google Scholar
• Colorni, A., Dorigo, M., & Maniezzo, V. (1991). Distributed optimization by ant colonies. In F. Varela & P. Bourgine eds, Proceedings of ECAL91 - European Conference on Artificial Life, (pp. 134–142). Paris, France: Elsevier Publishing.
   Google Scholar
• Craig, C. L. (1991). Physical constraints on group foraging and social evolution. Functional Ecology, 5, 648–654. doi:10.2307/2389484
   Web of Science ®Google Scholar
• Cuevas, E., & Cienfuegos, M. (2014). A new algorithm inspired in the behavior of the social-spider for constrained optimization. Elsevier Journal of Expert Systems with Applications, 41, 412–425. doi:10.1016/j.eswa.2013.07.067
   Web of Science ®Google Scholar
• Cuevas, E., Cienfuegos, M., Zaldívar, D., & Pérez-Cisneros, M. (2013). A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications, 40(16), 6374–6384. doi:10.1016/j.eswa.2013.05.041
   Web of Science ®Google Scholar
• Dorigo, M., Maniezzo, V., & Alberto, C. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on System, Man, and Cybernitics-PartB: Cybernetics, 26(1), 29–41. doi:10.1109/3477.484436
   PubMed Web of Science ®Google Scholar
• Dorigo, M., Maniezzo, V., & Colorni, A. (1991). Ant system: An autocatalytic optimizing process Technical Report 91-016, Italy, 1–21.
   Google Scholar
• Elsayed, W. T., Hegazy, Y. G., Bendary, F. M., & El-Bages, M. S. (2016). Modified social spider for solving the economic dispatch problem. Engineering Science and Technology: An International Journal, 19(4), 1672–1681. doi:10.1016/j.jestch.2016.09.002
   Google Scholar
• Forbes, N. (2000). Biologically inspired computing. http://arep.med.harvard.edu/gmc/Forbes01.pdf. doi:10.1109/5992.881711
   Google Scholar
• Greta, B. J., & Rypstra, A. L. (1992). Foraging behavior of communal spider, philoponella republicana (Aranae: Uloboridae). Journal of Insect Behaviour, 5(3), 321–335. doi:10.1007/BF01049841
   Web of Science ®Google Scholar
• James, J. Y., & Victor, O. L. (2015). A social spider algorithm for global optimization. Applied Soft Computing Journal, 30, 614–627. doi:10.1016/j.asoc.2015.02.014
   Web of Science ®Google Scholar
• Karaboga, D. (2005). An Idea Based on honey bee swarm for numerical optimization (Technical Report-Tr06, October, 2005), Kayseri/Türkiye:Erciyes University, Engineering Faculty Computer Engineering Department. http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf:.
   Google Scholar
• Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of The1995 IEEE International Conference on Neural Networks, 4, 1942–1948.
   Google Scholar
• Kumar, D., & Kumar, B. (2013). Optimization of Benchmark functions using Artificial Bee Colony (ABC) algorithm. IOSR Journal of Engineering (IOSRJEN), 3(10), 09–14. doi:10.9790/3021-031040914
   Google Scholar
• Marco, D., & Alberto, C. (1996). The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics–Part B, 26(1), 1–13.
   PubMed Web of Science ®Google Scholar
• Maxence, S., Carly, S., Allen, L., & Avile´S, L. (2010). Social organization of the colonial spiderLeucaugesp. In the Neotropics: Vertical stratification within colonies. The Journal of Arachnology, 38, 446–451. doi:10.1636/Hi09-99.1
   Web of Science ®Google Scholar
• Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). A grey wolf optimizer. Advances in Engineering Software, 69(69), 46–61. doi:10.1016/j.advengsoft.2013.12.007
   Google Scholar
• Momin, J., & Xin-She, Y. (2013). A literature survey of benchmark functions for global optimization problems. Int. Journal of Mathematical Modelling and Numerical Optimisation, 4(2), 150–194. doi:10.1504/IJMMNO.2013.055204
   Google Scholar
• Pekar, S., Hruskova, M., & Lubin, Y. (2005). Can solitary spiders (Araneae) cooperate in prey capture? Journal of Animal Ecology, 74, 63–70. doi:10.1111/j.1365-2656.2004.00897.x
   Web of Science ®Google Scholar
• Sangita, R., Samir, B., & Sheli, S. C. (2014). Nature-inspired swarm intelligence and its applications. International Journal of Modern Education and Computer Science, 12, 55–65.
   Google Scholar
• Sudip, K. S., Mohammad, A. F., & Prabhat, K. M. (2016). Application of Modified Ant Colony Optimization (MACO) for multicast routing problem. International Journal of Intelligent Systems and Applications, 3, 43–48.
   Google Scholar
• Sulaiman, M. H., Daniyal, H., & Mustafa, M. W. (2012). Modified firefly algorithm in solving economic dispatch problems with practical constraints: 2012. In 2012 IEEE International Conference on Power and Energy (PECon. (pp. 157–161). Kota Kinabalu: IEEE.doi: 10.1109/PECon.2012.6450197
   Google Scholar
• Uetz, G. W. (1992). Foraging strategies of spiders. Trends in Ecology and Evolution, 7(5), 155–159. doi:10.1016/0169-5347(92)90209-T
   PubMed Web of Science ®Google Scholar
• Ulrich, T., & Gunther, R. (2016). Enhancing constraint propagation in ACO-based schedulers for solving the job shop scheduling problem. Procedia CIRP, 41, 443–447. doi:10.1016/j.procir.2015.12.071
   Google Scholar
• Yang, X. (2010). A new metaheuristic bat-inspired algorithm. In J. R. Gonzalez, et al. (Ed.), Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) studies in computational intelligence (pp. 284, 65–74). Berlin: Springer.
   Google Scholar
• Yang, X. (2012a). Flower pollination algorithm for global optimization. In J. Durand-Lose & N. Jonoska (Eds.), Unconventional Computation and Natural Computation. UCNC 2012. Lecture notes in computer science (Vol. 7445, pp. 240–249). Berlin, Heidelberg: Springer.
   Google Scholar
• Yang, X. (2012b). Swarm-based metaheuristic algorthms and no-free-lunch theorems. In R. Parpinelli & H. Rijeka (Eds.), Theory and new applications of swarm intelligence (pp. 1–16). InTech. Croatia ISBN: 978-953-51-0364-6 Retrieved from http://www.intechopen.com/books/theory-and-new-applications-of-swarm-intelligence/swarmbased-metaheuristic-algorithms-and-no-free-lunch-theorems:
   Google Scholar
• Yang, X. (2014a). A framework for self-tuning algorithms. In Book: Nature-inspired optimization algorithms (pp. 175–182). Amsterdam: Elsevier Science Publishers B. V. doi: 10.1016/B978-0-12-416743-8.00012-9
   Google Scholar
• Yang, X. (2014b). Cuckoo search and firefly algorithm: Theories and application. In Yang, Xin-She (Ed.), Studies in computational intelligence (pp. 1–26). . Switzerland: Springer International Publishing. doi:10.1007/978-3-319-02141-6.
   Google Scholar
• Yip, C., & Eric, K. S. (2008). Cooperative capture of large prey solves scaling challenge faced by spider societies. Proceedings of the National Academy of Sciences of the United States of America, 105(33), 11818–11822. doi:10.1073/pnas.0710603105
   PubMed Web of Science ®Google Scholar
• Zhang, G., & Xing, K. (2018). Memetic social spider optimization algorithm for schedulling two-stage assembly flowshop in distributed environment. Computer and Industrial Engineering, 125, 423–433. doi:10.1016/j.cie.2018.09.007
   Web of Science ®Google Scholar
• Zhang, L., Qin, Y., Meng, X., Wang, L., & Zhu, T. (2016). MPSO-based model of train operation adjustment. 2016 Procedia Engineering, 137, 114–123.
   Google Scholar
• Zhou, Y., Zhou, Y., Luo, Q., & Abdel-Basset, M. (2017). A simplex method-based social spider optimization algorithm for clustering analysis. Engineering Application of Artificial Intelligence, 64, 67–68. doi:10.1016/j.engappai.2017.06.004
   Web of Science ®Google Scholar