To improve the service quality of cloud computing, and aiming at the characteristics of resource scheduling optimization problems, this paper proposes a hybrid multi-objective bat algorithm. To prevent the algorithm from falling into a local minimum, the bat population is classified. The back-propagation algorithm based on the mean square error and the conjugate gradient method is used to increase the loudness in the search direction and the pulse emission rate. In addition, the random walk based on lévy flight is also used to improve the optimal solution, thereby improving the algorithm’s global search capability. The simulation results prove that the multi-objective bat algorithm proposed in this paper is superior to the multi-objective ant colony optimization algorithm, genetic algorithm, particle swarm algorithm, and cuckoo search algorithm in terms of makespan, degree of imbalance, and throughput. The cost is also slightly better than the multi-objective ant colony optimization algorithm and the multi-objective genetic algorithm.

Accurate stock market prediction models can provide investors with convenient tools to make better data-based decisions and judgments. Moreover, retail investors and institutional investors could reduce their investment risk by selecting the optimal stock index with the help of these models. Predicting stock index price is one of the most effective tools for risk management and portfolio diversification. The continuous improvement of the accuracy of stock index price forecasts can promote the improvement and maturity of China’s capital market supervision and investment. It is also an important guarantee for China to further accelerate structural reforms and manufacturing transformation and upgrading. In response to this problem, this paper introduces the bat algorithm to optimize the three free parameters of the SVR machine learning model, constructs the BA-SVR hybrid model, and forecasts the closing prices of 18 stock indexes in Chinese stock market. The total sample comes from 15 January 2016 (the 10th trading day in 2016) to 31 December 2020. We select the last 20, 60, and 250 days of whole sample data as test sets for short-term, mid-term, and long-term forecast, respectively. The empirical results show that the BA-SVR model outperforms the polynomial kernel SVR model and sigmoid kernel SVR model without optimized initial parameters. In the robustness test part, we use the stationary time series data after the first-order difference of six selected characteristics to re-predict. Compared with the random forest model and ANN model, the prediction performance of the BA-SVR model is still significant. This paper also provides a new perspective on the methods of stock index forecasting and the application of bat algorithms in the financial field.

In this paper, a hybrid bat optimization algorithm based on variable neighbourhood structure and two learning strategies is proposed to solve a three-stage distributed assembly permutation flowshop scheduling problem to minimize the makespan. The algorithm is firstly designed to increase the population diversity by classifying the populations, which solves the difficult trade-off between convergence and diversity of the bat algorithm. Secondly, a selection mechanism is used to update the bat’s velocity and location, solving the difficulty of the algorithm to trade-off exploration and mining capacity. Finally, the Gaussian learning strategy and elite learning strategy assist the whole population to jump out of the local optimal frontier. The simulation results demonstrate that the algorithm proposed in this paper can well solve the DAPFSP. In addition, compared with other metaheuristic algorithms, IHBA has better performance and gives full play to its advantage of finding optimal solutions.