Modified drosophila optimization algorithm for managing re-sources in cloud environment

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Optimizing a problem is common among the researchers in all the fields. The worst case of the optimization problem is that when it is not solved by putting lots of efforts and human capital is spoiled in dealing with the problem. So, to search for the optimal solution of a problem is becoming a tedious job for the scholars. Many algorithms have been applied to solve these long-standing complex problems. In this paper, Drosophila Food search optimization (DFO) Algorithm is applied, which explores its vision foraging behavior in the global optimization process. The objective behind the use of DFOA is to achieve fast computation, maximizing resource utilization and minimizing makespan. The survey of our work presents the state-of-the-art in recent research.



  • Keywords

    Nature-Inspired Algorithms; Drosophila Food Search Algorithm; Resource Availability; Cloud Computing

  • References

      [1] Yang, X.S.: Nature-Inspired Optimization Algorithms. ISBN 978-0-12-416743-8. First Edition, Elsevier (2014)

      [2] Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. ISBN 0-19-513159-2. Oxford University Press(1999)

      [3] Pan, W.T.: A New Evolutionary Computational Approach: Fruit Fly Optimization Algorithm. In Conference of Digital Technology and Innovation Management, Taipei (2011)

      [4] Chen, P.W., Lin, W.Y., Huang, T.H., and Pan W.T.: Using Fruit Fly Optimization Algorithm Optimized Grey Model Neural Network to Perform Satisfaction Analysis for E-Business Service. Journal of Applied Mathematics and Information Sciences. 7, pp 459-465(2013)

      [5] Das K.N., Singh, T.K.: Drosophila Food-Search Optimization. Journal of Applied Mathematics and Computation. 231, pp 566-580 (2014)

      [6] Iscan, H., Gunduz, M.: Parameter Analysis on Fruit Fly Optimization Algorithm. Journal of Computer and Communication. 2, pp 137-141 (2014)

      [7] Pan, W.T.: A New Fruit Fly Optimization Algorithm: Taking the Financial Distress Model. Knowledge-based System. 26, pp 69-74 (2012)

      [8] Li, H., Guo, C., Sun, and L.J.: A Hybrid Annual Power Load Forecasting Model Based on Generalized Regression Neural Network With Fruit Fly Optimization Algorithm. Knowledge-based System. 37, pp 378-387 (2013)

      [9] Wang, L., Zheng, X.L., Wang, S.Y.: A Novel Binary Fruit Fly Optimization Algorithm for Solving Multidimensional Knapsack problem. Knowledge-based System. 48, pp 17-23 (2013)

      [10] Lin, S.M.: Analysis of Service Satisfaction in Web Auction Logistics Service Using a Combination of Fruit Fly Optimization Algorithm and General Regression Neural Network. Journal of Neural Computing and Application. 7, pp 459-465 (2013)

      [11] Wang, L., Shi, Y., Liu, S.: An Improved Fruit Fly Optimization Algorithm and Its Application to Joint Replenishment Problems. Journal of Expert System and Application. 42, pp 4310-4323 (2015)

      [12] Sheng, W., Bao, Y.: Fruit Fly Optimization Algorithm Based Traditional Order Fuzzy-PID Controller for Electronic Throttle. Journal of Non-Linear Dynamics. 73, pp 611-619 (2013)

      [13] Pan, W.T.: Using Modified Fruit Fly Optimization Algorithm to Perform the Function Test and Case Studies. Journal of Connection Science. 25, pp 151-160 (2013)

      [14] Shan, D., Cao, G.H., Dong, H.: LGMS-FOA: An Improved Fruit Fly Optimization Algorithm for Solving Optimization Problems. Journal of Mathematical Problems in Engineering. pp 1-9 (2013)

      [15] Zhang, P., Wang, L.: Grouped Fruit Fly Optimization Algorithm for the No-Wait Lot Streaming Flow Shop Scheduling. In: International Conference on intelligent Computing. pp 664-674, Springer (2014)

      [16] Abdullahi, M., Ngadi, M.A.: Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment. PloS One. 11, pp 1-29 (2016)

      [17] Wu, L., Zuo, C., Zhang, H.: A Cloud Model Based Fruit Fly Optimization Algorithm. Knowledge-based Systems. 89, pp 603-617 (2015)

      [18] Dai, H., Zhao, G., Lu, J.: Comment and Improvement on New Fruit Fly Optimization Algorithm: Taking the Financial Distress Model as an Example. Knowledge-based Systems. 59, pp 159-160 (2014)

      [19] Pan, Q.K., Sang, H.Y., Duan, J.H., GAO, L.: An Improved Fruit Fly Optimization Algorithm for Continuous Function Optimization Problems. Knowledge-based Systems, 62, pp 69-83 (2014)

      [20] Yuan, X., Dai, X., Zhao, J., He, Q.: On A Novel Multi-Swarm Fruit Fly Optimization Algorithm and Its Application. Knowledge-based Systems, 233, pp 260-271 (2014).




Article ID: 13031
DOI: 10.14419/ijet.v7i2.4.13031

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.