Abstract
Solving non-linear optimization with more accuracy has become a challenge for the researchers. Evolutionary global search techniques today are treated as the alternate paradigm over the traditional methods for their simplicity and robust nature. However, if an evolutionary problem is computationally burdened both the human efforts and time will be wasted. In this paper a much simpler and more robust optimization algorithm called Drosophila Food-Search Optimization (DFO) Algorithm is proposed. This new technique is based on the food search behavior of the fruit fly called ‘Drosophila’. In order to evaluate the efficiency and efficacy of the DFO-algorithm, a set of 20 unconstrained benchmark problems have been used. The numerical results confirms the supremacy of DFO over the algorithms namely Hybrid Ant Colony-Genetic Algorithm (GAAPI), Level-Set evolution and Latin squares Algorithm (LEA), which are reported as the most efficient algorithms in the recent literature.
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References
Alam, M.S., Islam, M., Yao, X., Murase, K.: Diversity guided evolutionary programming: a novel approach for continuous optimization. Appl. Soft Comput. 12(6), 1693–1707(2012) (Elsevier)
Breast, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. Trans Evol. Comput. IEEE. 10, 646–657 (2006)
Gao, W., Liu, S.: Comput. Oper. Res. 39, 687–697 (2012)
Santoshi, K., Arakawa, M., Yamazaki, K.: Differential evolution as the global optimization technique and its application to structural optimization. Appl.Soft Comput. 11, 3792–3803 (2011)
Das, K.N., Singh, T.K.: Self adaptive hybridization of quadratic approximation with real coded genetic algorithm. In: Proceedings of Seventh international conference of Bio-Inspired Computing: Theories and Application (BICTA2012). Advances in Intelligent Systems and Computing, vol. 202, pp. 503–513. Springer, Heidelberg (2013)
Neshat, M., Sepidnam, G., Sargolzaei, M.: Swallow swarm optimization algorithm: a new method to optimize. Neural Comput. Appl. (2012). doi:10.1007/s00521-012-0939-9
Dethier, V.G.: The Hungary Fly. Harvard University Press, Cambridge (1976)
Stocker, R.F.: The organization of the chemosensory system in Drosophila melanogaster : a review. Cell Tissue Res. 275, 3–26 (1994)
Vosshal, L.B.: The molecular logic of olfaction in Drosophila. Chemo Senses 26, 207–213 (2001)
Clyne, P.J., Warr, C.G., Freeman, M.R., Lessing, D., Kim, J., Carlson, J.R.: A novel family of divergent seven-transmembrane proteins: candidate odorant receptors in Drosophila. Neuron 416, 327–338 (1999)
Lodish, H., Berk, A., Zipursky, L., Matsudaira, P., Batlimore, D., Darnell, J.: Molecular cell biology. Cell. Signal. 533–567 (2004)
Yang, Z., He, J., Yao, X.: Making a difference to differential evolution. In: Michalewicz, Z., Siaary, P. (eds.) Advances in Meta-heuristics for Hard Optimization, pp. 397–414. Springer, Berlin (2007)
Yang, Z., Tang, K., Yao, X.: Differential evolution for high-dimensional function optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC07), pp. 3523–3530. IEEE (2007)
Molina, D., Herrera, F., Lozano, M.: Adaptive local search parameters for real coded memetic algorithms. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC2005), pp. 888–895. IEEE (2005)
Lozano, M., Herra, F., Krasnogor, N., Molina, D.: A real-coded memetic algorithms with crossover hill-climbing. Evol. Comput. 12, 273–302 (2004)
Ciornei, I., Kyriakides, E.: Hybrid ant colony genetic algorithm (GAAPI) for global continuous optimization. IEEE Trans. Syst. Man Cybernetics: Part B Cybernatics 42, 234–244 (2012)
Deep, K., Das, K.N.: Performance improvement of real coded genetic algorithm with quadratic approximation based hybridization. Int. J. Intell. Defense Support Syst. 2(4), 319–334 (2009)
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Das, K.N., Singh, T.K. (2014). A Novel Search Technique for Global Optimization. In: Pant, M., Deep, K., Nagar, A., Bansal, J. (eds) Proceedings of the Third International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 259. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1768-8_44
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DOI: https://doi.org/10.1007/978-81-322-1768-8_44
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