The antlion optimizer (ALO) is a new swarm-based metaheuristic algorithm for optimization, which mimics the hunting\nmechanism of antlions in nature. Aiming at the shortcoming that ALO has unbalanced exploration and development capability\nfor some complex optimization problems, inspired by the particle swarm optimization (PSO), the updated position of antlions in\nelitismoperator of ALO is improved, and thus the improved ALO (IALO) is obtained.The proposed IALO is compared against sine\ncosine algorithm (SCA), PSO,Moth-flame optimization algorithm (MFO),multi-verse optimizer (MVO), and ALO by performing\non 23 classic benchmark functions.The experimental results show that the proposed IALO outperforms SCA, PSO, MFO, MVO,\nand ALO according to the average values and the convergence speeds. And the proposed IALO is tested to optimize the parameters\nof BP neural network for predicting the Chinese influenza and the predictedmodel is built, written as IALO-BPNN,which is against\nthe models: BPNN, SCA-BPNN, PSO-BPNN, MFO-BPNN,MVO-BPNN, and ALO-BPNN. It is shown that the predicted model\nIALO-BPNN has smaller errors than other six predicted models, which illustrates that the IALO has potentiality to optimize the\nweights and basis of BP neural network for predicting the Chinese influenza effectively. Therefore, the proposed IALO is an effective\nand efficient algorithm suitable for optimization problems.
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