Li Zhi, Chu Shu-Chuan, Pan Jeng-Shyang, Hu Pei, Xue Xingsi
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan.
Entropy (Basel). 2022 Apr 22;24(5):586. doi: 10.3390/e24050586.
Metaheuristic algorithms are widely employed in modern engineering applications because they do not need to have the ability to study the objective function's features. However, these algorithms may spend minutes to hours or even days to acquire one solution. This paper presents a novel efficient Mahalanobis sampling surrogate model assisting Ant Lion optimization algorithm to address this problem. For expensive calculation problems, the optimization effect goes even further by using MSAALO. This model includes three surrogate models: the global model, Mahalanobis sampling surrogate model, and local surrogate model. Mahalanobis distance can also exclude the interference correlations of variables. In the Mahalanobis distance sampling model, the distance between each ant and the others could be calculated. Additionally, the algorithm sorts the average length of all ants. Then, the algorithm selects some samples to train the model from these Mahalanobis distance samples. Seven benchmark functions with various characteristics are chosen to testify to the effectiveness of this algorithm. The validation results of seven benchmark functions demonstrate that the algorithm is more competitive than other algorithms. The simulation results based on different radii and nodes show that MSAALO improves the average coverage by 2.122% and 1.718%, respectively.
元启发式算法在现代工程应用中被广泛使用，因为它们不需要具备研究目标函数特征的能力。然而，这些算法可能需要花费数分钟到数小时甚至数天才能获得一个解。本文提出了一种新颖的高效马氏采样代理模型辅助蚁狮优化算法来解决这个问题。对于计算成本高昂的问题，使用MSAALO（马氏采样代理辅助蚁狮优化算法）时优化效果会更显著。该模型包括三个代理模型：全局模型、马氏采样代理模型和局部代理模型。马氏距离还可以排除变量的干扰相关性。在马氏距离采样模型中，可以计算每只蚂蚁与其他蚂蚁之间的距离。此外，该算法对所有蚂蚁的平均长度进行排序。然后，算法从这些马氏距离样本中选择一些样本用于训练模型。选择了七个具有不同特征的基准函数来验证该算法的有效性。七个基准函数的验证结果表明，该算法比其他算法更具竞争力。基于不同半径和节点的仿真结果表明，MSAALO分别将平均覆盖率提高了2.122%和1.718%。
