Firefly Algorithm Optimized Functional Link Artificial Neural Network for ISA-Radar Image Recognition

Abstract

Traditional neural networks are very diverse and have been used during the last decades in the fields of data classification. These networks like MLP, back propagation neural networks (BPNN) and feed forward network have shown inability to scale with problem size and with the slow convergence rate. So in order to overcome these numbers of drawbacks, the use of higher order neural networks (HONNs) becomes the solution by adding input units along with a stronger functioning of other neural units in the network and transforms easily these input units to hidden layers. In this paper, a new metaheuristic method, Firefly (FFA), is applied to calculate the optimal weights of the Functional Link Artificial Neural Network (FLANN) by using the flashing behavior of fireflies in order to classify ISA-Radar target. The average classification result of FLANN-FFA which reached 96% shows the efficiency of the process compared to other tested methods.

Simulation study of an improved firefly algorithm for solving multi-dimensional complex function optimization

“…The main classical metaheuristics are genetic algorithms, whale algorithms, artificial fish swarm algorithms, particle swarm algorithms, etc. Firefly Algorithm (FA) is a metaheuristic algorithm proposed by Yang [1] in 2008, which has a clear flow and fewer parameters, and is widely used in areas such as wireless network selection, engineering optimization, resource management, and image recognition [2][3][4][5][6] . Wang [7] et al used Tent chaotic mapping to initialise the location of firefly populations, which improved the quality of firefly population initialisation but had poor security.…”