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

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.

[1]  Mesut Gündüz,et al.  Parameter Analysis on Fruit Fly Optimization Algorithm , 2014 .

[2]  Shengyao Wang,et al.  A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem , 2013, Knowl. Based Syst..

[3]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[4]  Sen Guo,et al.  A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm , 2013, Knowl. Based Syst..

[5]  Xin-She Yang,et al.  Nature-Inspired Optimization Algorithms: Challenges and Open Problems , 2020, J. Comput. Sci..

[6]  Lianghong Wu,et al.  A cloud model based fruit fly optimization algorithm , 2015, Knowl. Based Syst..

[7]  Qian He,et al.  On a novel multi-swarm fruit fly optimization algorithm and its application , 2014, Appl. Math. Comput..

[8]  Mohammed Abdullahi,et al.  Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment , 2016, PloS one.

[9]  Dan Shan,et al.  LGMS-FOA: An Improved Fruit Fly Optimization Algorithm for Solving Optimization Problems , 2013 .

[10]  Kedar Nath Das,et al.  Drosophila Food-Search Optimization , 2014, Appl. Math. Comput..

[11]  Hongde Dai,et al.  Comment and improvement on "A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example" , 2014, Knowl. Based Syst..

[12]  Wei-Yuan Lin,et al.  Using Fruit Fly Optimization Algorithm Optimized Grey Model Neural Network to Perform Satisfaction Analysis for E-Business Service , 2013 .

[13]  Peng Zhang,et al.  Grouped Fruit-Fly Optimization Algorithm for the No-Wait Lot Streaming Flow Shop Scheduling , 2014, ICIC.

[14]  Su-Mei Lin,et al.  Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network , 2011, Neural Computing and Applications.

[15]  Liang Gao,et al.  An improved fruit fly optimization algorithm for continuous function optimization problems , 2014, Knowl. Based Syst..

[16]  Shan Liu,et al.  An improved fruit fly optimization algorithm and its application to joint replenishment problems , 2015, Expert Syst. Appl..

[17]  Wen-Tsao Pan,et al.  A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example , 2012, Knowl. Based Syst..

[18]  Wen-Tsao Pan,et al.  Using modified fruit fly optimisation algorithm to perform the function test and case studies , 2013, Connect. Sci..

[19]  Wang Sheng,et al.  Fruit fly optimization algorithm based fractional order fuzzy-PID controller for electronic throttle , 2013 .