The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction

Hindawi Complexity Volume 2019, Article ID 1480392, 12 pages https://doi.org/10.1155/2019/1480392 Research Article The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction Hongping Hu 1 2 ,1 Yangyang Li,1 Yanping Bai ,1 Juping Zhang ,2 and Maoxing Liu1 School of Science, North University of China, Taiyuan, Shanxi 030051, China Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi 030006, China Correspondence should be addressed to Hongping Hu; Received 22 February 2019; Revised 20 June 2019; Accepted 11 July 2019; Published 5 August 2019 Academic Editor: Michele Scarpiniti Copyright © 2019 Hongping Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The antlion optimizer (ALO) is a new swarm-based metaheuristic algorithm for optimization, which mimics the hunting mechanism of antlions in nature. Aiming at the shortcoming that ALO has unbalanced exploration and development capability for some complex optimization problems, inspired by the particle swarm optimization (PSO), the updated position of antlions in elitism operator of ALO is improved, and thus the improved ALO (IALO) is obtained. The proposed IALO is compared against sine cosine algorithm (SCA), PSO, Moth-flame optimization algorithm (MFO), multi-verse optimizer (MVO), and ALO by performing on 23 classic benchmark functions. The experimental results show that the proposed IALO outperforms SCA, PSO, MFO, MVO, and ALO according to the average values and the convergence speeds. And the proposed IALO is tested to optimize the parameters of BP neural network for predicting the Chinese influenza and the predicted model is built, written as IALO-BPNN, which is against the models: BPNN, SCA-BPNN, PSO-BPNN, MFO-BPNN, MVO-BPNN, and ALO-BPNN. It is shown that the predicted model IALO-BPNN has smaller errors than other six predicted models, which illustrates that the IALO has potentiality to optimize the weights and basis of BP neural network for predicting the Chinese influenza effectively. Therefore, the proposed IALO is an effective and efficient algorithm suitable for optimization problems. 1. Introduction Optimization problems exist in scientific research and engineering areas [1–3], such as statistical physics [4, 5], computer science [6], artificial intelligence [7], and pattern recognition [8]. For every optimization problem, there is at least one global optimal solution and there may be some local optimal solutions as well as a global optimal solution. Many researchers wish to seek the global optimum for solving optimization problems. Therefore, many methods are created and applied to solve optimization problems. In particular, swarm intelligence algorithms proposed give strong support. Genetic algorithm (GA) proposed by Holland in 1992 [9] simulates Darwinian evolution, and particle swarm optimization (PSO) proposed in 1995 [10] simulates birds’ behavior. And GA algorithm and PSO algorithm are constantly improved and applied to many aspects, such as complex system [11], hyperlastic materials [12], radiation detectors [13], and reaction kinetic parameters of biomass pyrolysis [14]. Since then, swarm intelligence algorithms have been constantly proposed and widely applied to find the global optimum of the optimization problems in various fields. For example, Moth-flame optimization algorithm (MFO) [15] mimics the moth eventually converging towards the light. Multi-verse optimizer (MVO) [16] was proposed on the base of three concepts in cosmology: white hole, black hole, and wormhole. Sine cosine algorithm (SCA) [17] creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions. Whale optimization algorithm (WOA) [18] mimics the social behavior of humpback whales. And its improvement proposed for global optimization consists of three strategies: the chaotic initialization phase, Gaussian mutation, and a chaotic local search with a “shrinking” strategy [19]. The antlion optimizer (ALO) [20] mimics the hunting mechanism of antlions in nature, which has been improved and applied into automatic generation control [21], cluster analysis [22], photovoltaic cell [23], power systems [24], and parameter estimation of 2 photovoltaic models [25]. Harris Hawks Optimizer (HHO) [26] proposed in 2019 is a novel population-based, natureinspired optimization paradigm, whose main inspiration is the cooperative behavior and chasing style of Harris hawks in nature called surprise pounce. HHO is used to perform the function optimizations and the real-world engineering problems. Swarm intelligence algorithms can be also applied into feature selection (FS), such as gravitational search algorithm (GSA) inspired by Newton’s law of gravity which is combined with evolutionary crossover and mutation operators [27], an efficient optimizer based on the simultaneous use of the Grasshopper Optimization Algorithm (GOA), selection operators, and Evolutionary Population Dynamics (EPD) [28], the Binary Dragonfly Algorithm (BDA) using timevarying transfer functions [29], and binary Salp Swarm Algorithm (SSA) with asynchronous updating rules and a new leadership structure [30]. Swarm intelligence algorithms have been also applied to optimize the weights and basis of artificial neural networks for prediction and classification. For example, SCA and GA are used to optimize the weight and basis of artificial neural network for predicting the direction of stock market index, respectively [31, 32]. An improved dynamic particle swarm optimization with AdaBoost algorithm is used to optimize the parameters of generalized radial basis function neural network for stock market prediction [33], and an Improved Exponential Decreasing Inertia Weight-Particle Swarm Optimization Algorithm is utilized to optimize the parameters of radial basis function neural network for the air quality index (AQI) prediction [34], respectively. Artificial tree (AT) algorithm was improved and applied to optimize the parameters of artificial neural network for predicting influenza-like illness [35]. MVO algorithm was combined with PSO algorithm to optimize the parameters of Elman neural network for classification of endometrial carcinoma with gene expression [36]. Based on Gaussian mutation and a chaotic local search that are employed to increase the population diversity of MFO and the flame updating process of MFO for better exploiting the locality of the solutions, respectively, the proposed CLSGMFO approach [37] is used to perform the function optimizations and is combined with a hybrid kernel extreme learning machine (KELM) model for financial prediction. Based on oppositionbased learning (OBL) and the drawbacks of GWO, OBLGWO [38] i (...truncated)