An efficient neural network optimized by fruit fly optimization algorithm for user equipment association in software‐defined wireless sensor network

Enhancing the energy efficiency and maximizing the networking lifetime are the major challenges in Wireless Sensor Networks (WSN).Swarm Intelligence based algorithms are very efficient in solving nonlinear design problems with real-world applications.In this paper a Swarm based Fruit Fly Optimization Algorithm (FFOA) with the concept of K-Medoid clustering and swapping is implemented to increase the energy efficiency and lifetime of WSN. A comparative analysis is performed in terms of cluster compactness,cluster error and convergence. MATLAB Simulation results show that K-Medoid Swapping and Bunching Fruit Fly optimization (KMSB-FFOA) outperforms FFOA and K-Medoid Fruit Fly Optimization Algorithm (KM-FFOA).

Download Full-text

Wireless Sensor Network Coverage Optimization Based on Fruit Fly Algorithm

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v14i06.8698 ◽

2018 ◽

Vol 14 (06) ◽

pp. 58 ◽

Cited By ~ 2

Author(s):

Ren Song ◽

Zhichao Xu ◽

Yang Liu

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Fruit Fly ◽

Effective Radius ◽

Sensor Nodes ◽

Wireless Sensor ◽

Fruit Fly Optimization Algorithm ◽

Network Coverage ◽

Coverage Optimization ◽

Improved Algorithm

To solve the defect of traditional node deployment strategy, the improved <a name="_Hlk502130691"></a>fruit fly algorithm was combined with wireless sensor network. The optimization of network coverage was implemented. Based on a new type of intelligent algorithm, the change step of fruit fly optimization algorithm (CSFOA)was proposed. At the same time, the mathematical modeling of two network models was carried out respectively. The grid coverage model was used. The network coverage and redundancy were transformed into corresponding mathematical variables by means of grid partition.Among them, the maximum effective radius of sensor nodes was fixed in mobile node wireless sensor network. The location of nodes was randomly cast. The location of sensor nodes was placed in fixed position nodes. The effective radius of nodes can be changed dynamically.Finally, combined with the corresponding network model, the improved algorithm was applied to wireless sensor network.The combination of the optimal solution of the node position and the perceptual radius was found through the algorithm. The maximum network coverage was achieved.The two models were simulated and verified. The results showed that the improved algorithm was effective and superior to the coverage optimization of wireless sensor networks.

Download Full-text

A Mutual Fund Investment Method Using Fruit Fly Optimization Algorithm and Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.571-572.318 ◽

2014 ◽

Vol 571-572 ◽

pp. 318-325 ◽

Cited By ~ 3

Author(s):

Tsu Hua Huang ◽

Yung Ho Leu

Keyword(s):

Neural Network ◽

Mutual Funds ◽

Optimization Algorithm ◽

Mutual Fund ◽

Fruit Fly ◽

Fruit Fly Optimization Algorithm ◽

Fruit Fly Optimization ◽

Asset Value ◽

Net Asset Value ◽

Fund Portfolio

This paper presents a method to construct a profitable portfolio of mutual funds for investors. This method comprises two stages. In the first stage, the DEA, Sharpe and Treynor indices of mutual funds and the monthly rates of return (ROR) of mutual funds are used to select a mutual fund portfolio. In the second stage, the linear regression model, the Fruit Fly Optimization Algorithm (FOA) and the General Regression Neural Network (GRNN) are used to construct a prediction model for the net asset values of each of the constituent mutual funds of the portfolio. The trade decision of a selected mutual fund is then made based on the rise or fall of its net asset value. The empirical results showed that, compared to other combinations, the combination of using Sharpe index for portfolio selection and the GRNN optimized with FOA for net asset value prediction offered the best accumulated return rate for the mutual fund portfolio investment.

Download Full-text

Prediction Research on the Failure of Steam Turbine Based on Fruit Fly Optimization Algorithm Support Vector Regression

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.614-615.409 ◽

2012 ◽

Vol 614-615 ◽

pp. 409-413 ◽

Cited By ~ 2

Author(s):

Zhi Biao Shi ◽

Ying Miao

Keyword(s):

Neural Network ◽

Support Vector Regression ◽

Optimization Algorithm ◽

Optimal Solution ◽

Fruit Fly ◽

Support Vector ◽

Fruit Fly Optimization Algorithm ◽

Global Optimal Solution ◽

Fruit Fly Optimization ◽

Global Optimal

In order to solve the blindness of the parameter selection in the Support Vector Regression (SVR) algorithm, we use the Fruit Fly Optimization Algorithm (FOA) to optimize the parameters in SVR, and then propose the optimization algorithm on the parameters in SVR based on FOA to fitting and simulate the experimental data of the turbine’s failures. This algorithm could optimize the parameters in SVR automatically, and achieve ideal global optimal solution. By comparing with the commonly used methods such as Support Vector Regression and Radial Basis Function neural network, it can be shown that the forecast results of FOA_SVR more accurate and the forecast speed is the fastest.

Download Full-text