The indexes of SI-BP on Boston.

In the context of global economic austerity in the post epidemic era, housing, as one of the basic human needs, has become particularly important for accurate prediction of house prices. BP neural network is widely used in prediction tasks, but their performance is easily affected by weights and biases, and thus metaheuristic algorithms are needed to optimize the network parameters. Firstly, to address the shortcomings of the artificial Gorilla Troops Optimizer (GTO) in such optimization tasks, such as reduced population diversity, easy to fall into local optimal solutions and slow convergence, this paper proposes a fitness allocation strategy, a Cauchy variation strategy, and an elite evolution mechanism to improve the algorithm, which in turn results in an improved artificial Gorilla Troops Optimizer (IGTO). Subsequently, a BP neural network house price prediction model based on IGTO is constructed and experiments are conducted on four datasets, namely, Boston, California-Bay, California-Land and Taiwan. The experiments are first compared with eleven other swarm intelligence algorithms and then with four machine learning models, and the results show that IGTO-BPNN improved 17.66%, 18.27%, 28.10%, 49.35% and 24.83% on five evaluation metrics, namely, MAE, MAPE, R2, RMSE, and SMAPE, respectively. The improvement of these indicators fully proves the superiority and effectiveness of IGTO-BPNN in house price prediction.

后疫情时代全球经济紧缩的背景下，住房作为人类基本需求之一，精准预测房价的重要性愈发凸显。反向传播神经网络（Back Propagation Neural Network，BP）虽被广泛应用于各类预测任务，但其性能极易受权重与偏置的影响，因此需要借助元启发式算法对网络参数进行优化。针对人工大猩猩部队优化器（Artificial Gorilla Troops Optimizer，GTO）在此类优化任务中存在的种群多样性降低、易陷入局部最优解以及收敛速度缓慢等缺陷，本文提出了适应度分配策略、柯西变异策略与精英进化机制以改进该算法，进而得到改进型人工大猩猩部队优化器（Improved Artificial Gorilla Troops Optimizer，IGTO）。随后，本文构建了基于IGTO的BP神经网络房价预测模型，并在波士顿（Boston）、加利福尼亚湾（California-Bay）、加利福尼亚内陆（California-Land）以及台湾（Taiwan）四个数据集上开展实验。实验首先与十一种其他群智能算法进行对比，随后又与四种机器学习模型展开对比，结果显示，IGTO-BPNN在平均绝对误差（Mean Absolute Error，MAE）、平均绝对百分比误差（Mean Absolute Percentage Error，MAPE）、决定系数（Coefficient of Determination，R²）、均方根误差（Root Mean Square Error，RMSE）以及对称平均绝对百分比误差（Symmetric Mean Absolute Percentage Error，SMAPE）五项评估指标上分别提升了17.66%、18.27%、28.10%、49.35%与24.83%。上述指标的提升充分证明了IGTO-BPNN在房价预测任务中的优越性与有效性。