Abstract
In this article, an evolutionary approach with different objectives based on the improvement of the hybrid GSA (golden section search algorithm) using the flower pollination algorithm (FPA) with fitness distance balance selection (FDS) is proposed. In GSFPAFDS algorithm, the optimization problem is improved in several sub-problems. The pollination of flowers in the population is enhanced in Levy flight with fitness distance to enhance convergence. Therefore, the GSA can be used as a local search in FPA. This simulation is part of the traditional GSA, FPA, GSFPA, and personnel processing to solve the consequences of sequential global optimization problems. A local update strategy is used to increase optimization capacity. Its main objectives are to minimize the overall execution time and the allocation of the balance between local and global optimal zone. It allows global parallelization with the simultaneous performance of the processor to the functions, and determine the ratio of the communication to the functions. The behavior of the GSFPAFDS with few methods such as GSA, FPA, and GSFPA understandability has been showcased to solve high-dimensional technical problems.
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References
Engin, O., Güçlü, A.: A new hybrid ant colony optimization algorithm for solving the no-wait flow shop scheduling problems. Appl. Soft Comput. 72, 166–176 (2018)
Liu, B., Li, P., Lin, W., Shu, N., Li, Y., Chang, V.: A new container scheduling algorithm based on multi-objective optimization. Soft Comput. 22, 7741–7752 (2018)
Moon, Y., Yu, H., Gil, J.M., Lim, J.: A slave ants-based ant colony optimization algorithm for task scheduling in cloud computing environments. HCIS 7(1), 1–10 (2017)
Xu, L.Z., Xie, Q.S., Yuan, Q.N., Huang, H.S.: An intelligent optimization algorithm for blocking flow-shop scheduling based on differential evolution. Int. J. Simul. Model. 18(4), 678–688 (2019)
Li, J., Tian, Q., Zhang, G., Wu, W., Xue, D., Li, L., Wang, J., Chen, L.: Task scheduling algorithm based on fireworks algorithm. EURASIP J. Wirel. Commun. Netw. 1, 1–8 (2018)
Nayak, J., Naik, B., Jena, A.K., Barik, R.K., Das, H.: Nature-inspired optimizations in cloud computing: applications and challenges. In: Cloud Computing for Optimization: Foundations, Applications, and Challenges, pp. 1–26. Springer (2018)
Guo, W., Liu, T., Dai, F., Xu, P.: An improved whale optimization algorithm for forecasting water resources demand. Appl. Soft Comput. 86, 105925 (2020)
Abdel-Basset, M., Abdle-Fatah, L., Sangaiah, A.K.: An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in the cloud computing environment. Clust. Comput. 22(4), 8319–8334 (2019)
Xin-gang, Z., Ji, L., Jin, M., Ying, Z.: An improved quantum particle swarm optimization algorithm for environmental, economic dispatch. Exp. Syst. Appl. 152, 113370 (2020)
Jana, B., Chakraborty, M., Mandal, T.: A task scheduling technique based on particle swarm optimization algorithm in the cloud environment. In: Soft Computing: Theories and Applications, pp. 525–536. Springer (2019)
Deng, W., Xu, J., Zhao, H.: An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem. IEEE Access 7, 20281–20292 (2019)
Sun, Y., Yang, T., Liu, Z.: A whale optimization algorithm based on quadratic interpolation for high-dimensional global optimization problems. Appl. Soft Comput. 85, 105744 (2019)
Pelusi, D., Mascella, R., Tallini, L., Nayak, J., Naik, B., Deng, Y.: An improved moth-flame Optimization algorithm with hybrid search phase. Knowl. -Based Syst. 191, 105277 (2020)
Hajimirzaei, B., Navimipour, N.J.: Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm. ICT Exp. 5(1), 56–59 (2019)
Abualigah, L., Diabat, A.: A novel hybrid ant lion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Cluster Comput. 1–19 (2020)
Adithiyaa, T., Chandramohan, D., Sathish, T.: Flower pollination algorithm for the optimization of stair casting parameter for the preparation of AMC. Mater. Today Proc. 21, 882–886 (2020)
Kahraman, H.T., Aras, S., Gedikli, E.: Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms. Knowl. -Based Syst. 190, 105169 (2020)
Zoubiri, F.Z., Rihani, R., Bentahar, F.: Golden section algorithm to optimize the chemical pretreatment of agro-industrial waste for sugar extraction. Fuel 266, 117028 (2020)
Yang, X.S.: Flower pollination algorithm for global optimization. In: Durand-Lose J., Jonoska, N. (eds.) Unconventional Computation and Natural Computation. UCNC 2012. Lecture Notes in Computer Science, vol. 7445. Springer, Berlin, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32894-7_27
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The authors are very grateful to the authorities of VIT University, Chennai campus for carrying out this extensive research work in a prosperous manner.
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Dhivya, S., Arul, R. (2022). Hybrid Flower Pollination Algorithm for Optimization Problems. In: Das, K.N., Das, D., Ray, A.K., Suganthan, P.N. (eds) Proceedings of the International Conference on Computational Intelligence and Sustainable Technologies. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6893-7_65
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