An Efficient JAYA Algorithm with Levy Flight for Non-linear Channel Equalization

Neural network (NN) based equalizers are known to outperform the linear equalizers based on finite impulse response (FIR) adaptive filter for highly dispersive and non-linear channels. To overcome the limitations of the back-propagation (BP) algorithm, metaheuristic algorithms are emerging as promising alternatives for training the NN based equalizers. JAYA is a simple and efficient metaheuristic algorithm. Hence, its application to channel equalization problem is worth investigating. Despite its simplicity and efficiency, the JAYA algorithm has problems such as being trapped in local minima due to insufficient diversity of population and weak exploration capability. To alleviate these issues, in this paper the concept of Levy flight (LF) and greedy selection scheme has been incorporated into the basic JAYA algorithm. The LF concept enhances the population diversity and thus avoids the state of stagnation. The greedy selection scheme is employed to improve the exploitation ability without loss of population diversity. Furthermore, in order to maintain the balance between the exploration and exploitation capabilities of the algorithm, an adaptive Levy index is proposed based on a linear control parameter strategy. An extensive simulation-based sensitivity analysis of proposed method called JAYA algorithm with Levy flight (JAYALF) with respect to key parameters is carried out to select the optimized values for these parameters. In order to validate the local optima avoidance ability, exploitation and convergence rate of the proposed JAYALF algorithm, it is tested on seventeen well-known unimodal and multimodal benchmark functions and to verify the effectiveness of the JAYALF for non-linear channel equalization problem, three wireless communication channels with two different nonlinearities have been considered for simulation. In addition, the non-parametric pairwise Wilcoxon rank-sum test has been employed to test the statistical validity of the results obtained from JAYALF. The results of experiments and statistical test demonstrate that the proposed algorithm significantly outperforms JAYA, variants of JAYA, state-of-the-art algorithms and BP algorithm in terms of solution quality, convergence speed, and robustness. Furthermore, the results of experimental analyses conducted indicate that proposed JAYALF algorithm has a better exploration ability and rapidly converges without getting stuck in local optima. (C) 2019 Published by Elsevier Ltd.