A bat algorithm (BA) is a heuristic algorithm that operates by imitating the echolocation behavior of bats to perform global optimization. The BA is widely used in various optimization problems because of its excellent performance. In the bat algorithm, the global search capability is determined by the parameter loudness and frequency. However, experiments show that each operator in the algorithm can only improve the performance of the algorithm at a certain time. In this paper, a novel bat algorithm with multiple strategies coupling (mixBA) is proposed to solve this problem. To prove the effectiveness of the algorithm, we compared it with CEC2013 benchmarks test suits. Furthermore, the Wilcoxon and Friedman tests were conducted to distinguish the differences between it and other algorithms. The results prove that the proposed algorithm is significantly superior to others on the majority of benchmark functions.

With the development of cloud computing technology (CCT), the processing of network traffic data becomes particularly important. However, the existing intrusion detection systems (IDS) are not efficient enough in analyzing network traffic data for anomaly detection. Therefore, this paper proposes a new data processing model for network anomaly detection. The model can simultaneously optimize the number of features (NF), accuracy, recall, false alarm rate (FAR) and precision. In order to better solve the model, an integrating dominance algorithm (MaOEA-ABC) with adaptive selection probability is proposed. In model, firstly, MaOEA-ABC is used to obtain the optimal feature subset by optimizing the above five objectives. Then, K-Nearest Neighbor (KNN) is used for network anomaly classification according to the optimal feature subset. Finally, MaOEA-ABC is compared with the existing standard MaOEAs algorithm (NSGA-III, EFR-RR, MaOEA-RD and PICEAg). The experimental results show that the approach can reduce the number of features on the basis of ensuring accuracy and FAR, thereby reducing the cost of detection.

The traditional coal energy system cannot meet the requirements of modern industrial production for sustainable development because extensive economic growth has caused significant coal consumption. Therefore, it is particularly important to formulate an effective coal production decision-making scheme to support complex coal production systems for sustainable development. To address this challenge, an improved many-objective fuzzy decision-making model with human participation for coal production prediction is established in this article. The model includes five objective functions: economic, energetic, environmental, coal gangue, and safety profits. Moreover, a novel multistage many-objective optimization algorithm is designed to adjust relevant model parameters and make the obtained model solution more accurate when compared with involved algorithms. In the algorithm, there are three stages of optimization. In stage one, the model with the relatively good convergence solution is chosen. In stage two, the diversity of the model solution needs to be retained by employing a diversity maintenance mechanism. In stage three, a comprehensive measurement approach is designed to ensure the balance of convergence and diversity during population evolution. To verify the validity of the model and the superiority of the optimization algorithm, five other advanced many-objective algorithms are used for comparison. The results from simulations on a benchmark function show that the proposed optimization algorithm has better performance than involved algorithms. Model solutions obtained on different metrics have better convergence and diversity for the coal fuzzy decision-making problem.