In the context of green manufacturing, energy consumption issues have attracted widespread attention from all walks of life, especially in the manufacturing industry. In actual industrial production, the distributed blocking flow shop scheduling problem is a typical manufacturing production scenario, but its energy consumption problem has not been effectively solved. In this study, a two-stage cooperative discrete differential evolution with Q-learning (QTCDDE) is proposed to solve the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP) with total energy consumption (TEC) and total tardiness (TTD). An initialization strategy that considers both TEC and TTD is proposed to obtain an initial population with rich search space. In the first stage, a discrete differential evolution is designed to improve the quality of the solution. In the second stage, three types of local search operators are designed, and they are adaptively selected based on historical information and Q-learning. In addition, during the iterative process, the two stages cooperate and complement each other. Finally, each strategy of QTCDDE is effectively verified. Furthermore, QTCDDE is compared with state-of-the-art algorithms in the benchmark suite. Experimental results show that QTCDDE significantly outperforms state-of-the-art algorithms at the 95% confidence interval and effectively solves EEDBFSP.

In the field of message-passing interface (MPI) program path coverage test case generation, evolutionary algorithms (EAs) have been frequently utilized to generate test cases. However, relying solely on EAs will incur excessive computational costs. In this article, we improve the efficiency and quality of MPI program path coverage test cases generated by EAs based on elite individual selection. First, data within the data domain is sampled and fitness is calculated to form a shared set. Then, the population data is initialized using EAs, and the fitness of individuals is predicted using the neighbor value sharing algorithm (NVSA). Subsequently, individuals are ranked using rank-based elite selection (RES). Finally, elite individuals are chosen through ranking to run the program and verify the generation of test cases. In order to reduce computational costs, data dimensionality reduction operations are added to the above process. We demonstrate that the proposed method can effectively generate test data and reduce test costs by comparing it with several excellent methods on seven representative MPI programs. Among them, NVSA has a maximum improvement of 42.2%, RES has a maximum improvement of 31.5%, dimensionality reduction can increase by 20.2%, and the overall method has a maximum improvement of 47.4%.

The most obvious characteristic of dynamic multiobjective optimization problems (DMOPs) is the time-varying Pareto-optimal set (POS) or/and Pareto-optimal front (POF). This kind of problem poses a higher challenge to the evolutionary algorithms, as it requires populations to rapidly track and converge the updated POF in new environments. Differing from the superposition of several strategies in the literatures, we propose an adaptive multistrategy algorithm based on the extent of environmental change, called AMEEC to effectively handle various dynamic changes. AMEEC chooses the corresponding strategies for different environmental changes adaptively. When the environment changes moderately or similarly, the prediction based on the clustered center points, POS manifold prediction, and generation of random solutions based on the ideal points are employed to relocate the population individuals in the new environment. Otherwise, the trend prediction model is employed to predict the knee points of each part and the center points of each cluster, and to adaptively adjust the area of random solutions based on the ideal and nadir points focuses on enhancing the diversity of population members. The proposed AMEEC is tested comprehensively on 19 benchmark problems compared with the six state-of-the-art algorithms. All algorithms use RM-MEDA (a regularity model-based multiobjective estimation of distribution algorithm) as a static optimizer. The experimental results demonstrate that AMEEC can achieve good convergence, diversity, and distribution, and is more competitive in dealing with the dynamic problems.

In recent years, as the demand for computing power in scientific computing has progressively increased, thfige scale of scientific applications has also grown, at the same time the number of cloud platform tenants is also expanding dramatically, cloud platforms often need to handle multiple large-scale heterogeneous workflows at the same time. Moreover, the rapid increment in the scale and performance of cloud computing centers has brought about even greater energy consumption, a large number of studies for multi-objective task scheduling usually focus on cost and makespan as the optimization objectives but often neglect the optimization of energy consumption. In order to solve the above problems, a shortest workflow first adaptive fast nondominated sorting genetic algorithm (SWFAGA) is proposed. First, a shortest workflow first workflows combinatorial encoding method is proposed which can encode multiple workflows as chromosomes based on the heterogeneity of workflows. Second, an adaptive crossover strategy based on Q-learning to improve the convergence speed without adding extra time overhead. In addition, a reference point-based convergence calculation method is devised. Finally, through the heterogeneous large-scale real-world multi-workflow, SWFAGA is verified to outperform state-of-the-art multi-objective optimization scheduling algorithms in terms of operational efficiency, stability, convergence, and diversity.

Peak carbon emissions and carbon neutrality have become important initiatives for the country to solve outstanding problems of resource and environmental constraints and promote green and low-energy development, and have attracted widespread attention from the industry. The distributed flow shop scheduling problem (DPFSP) is a typical problem that mainly works by consuming energy. However, DPFSP rarely considers energy efficiency and blocking constraints. In this study, an excellent bi-population cooperative discrete differential evolution (BCDDE) is proposed, aiming to address the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP) with total energy consumption (TEC) and total tardiness (TTD) as two objectives. A bi-population cooperative strategy is constructed to enhance the diversity of BCDDE, while utilizing it to initialize the population to enhance the quality of the initial solution. An adaptive local search operator strategy is developed to improve the BCDDE convergence. Critical and noncritical paths are devised to further optimize TEC and TTD objectives. The efficiency of each strategy related to BCDDE is verified and compared with state-of-the-art algorithms in the benchmark suite. Numerical results show that BCDDE becomes an efficient optimizer for the EEDBFSP, significantly outperforming the state-of-the-art algorithms at the 95% confidence interval.

Message passing interface (MPI) is a powerful tool for parallel computing, originally designed for high-performance computing on massively parallel computers. In this paper, we combine FERPSO-IMPR (fitness Euclidean distance ratio particle swarm optimizer with information migration-based penalty and population reshaping) and surrogate-assisted models to generate test cases for MPI program path coverage testing. In our proposed method, FERPSO-IMPR employs a dual population strategy to initialize data and calculate fitness. Then, we create a sample set based on the initial data and its fitness. Subsequently, we train the master-slave surrogate models to predict individual fitness. Finally, a small number of elite individuals are selected to execute the program to decide whether to generate the required test data and guide the subsequent evolution process. We apply the proposed method to seven MPI programs and perform experimental comparisons from five directions. Experimental results show that compared with the comparative method, the time consumption of the proposed method is reduced by 33.2%, the number of evaluations is reduced by 38.8%, and the success rate is increased by 7.6%. These results prove that our method can effectively reduce the test data generation cost of MPI programs.

Solving dynamic multiobjective optimization problems (DMOPs) is very challenging due to the requirements to respond rapidly and precisely to changes in an environment. Many prediction- and memory-based algorithms have been recently proposed for meeting these requirements. However, much useful knowledge has been ignored during the historical search process, and prediction deviations could occur, thus limiting the applicability of these methods to a variety of problems. Facing these concerns, this article proposes an evolutionary algorithm named FGTTMP based on feedback-guided transfer (FGT) and trend manifold prediction (TMP) for solving DMOPs. The FGT employs an information feedback model to extract valuable knowledge using all historical environments and then identifies excellent individuals using cluster-transfer learning. This can both accelerate convergence and introduce diversity for future environments. The TMP applies the probability-based trend prediction method to estimate the mass center of the whole population and the corresponding manifold, relying on the two previous moments. Thus, the FGT and TMP strategies combine historical knowledge with prediction techniques, synergistically leading the population in promising directions. The performance of the proposed algorithm is fully investigated and compared with eight state-of-the-art algorithms. Experimental results demonstrate that the proposed FGTTMP method can achieve better convergence and diversity on 19 various benchmark problems than the state-of-the-art algorithms.

Abstract Software change‐proneness prediction can help reduce software maintenance costs. Thus, it has drawn the attention of many researchers. In this paper, we propose a CNN (convolutional neural network)‐based method for software change‐proneness prediction, aiming to utilize the powerful prediction ability to make score of the performance measure higher than other baseline methods. Moreover, to alleviate the effect of the class imbalance problem, resampling methods are employed with the CNN. To validate the performance of the proposed CNN‐based method, an empirical study was conducted. The experimental results show that the CNN‐based method together with the resampling method performs better than the baseline methods, and the scores of performance measure of CNN with the ROS (random oversampling) method are higher than other method, especially the important performance measure.

Identifying bug immediately when it is introduced can help improve the validity and effectiveness of bug fixing. Predicting bugs in software code changes makes such identification possible. Buggy changes, changes that introduce bugs into source code, can be viewed as anomalies relative to clean changes for that they are rare and irregular. Thus, anomaly detection techniques can be applied to buggy change prediction. Isolation Forest, which detects anomalies based on the hypothesis that the anomalies have the shortest average path length on the constructed random forest, has exhibited its good performance on anomaly detection compared to other anomaly detection methods. In this paper, we adopt it in predicting bugs in software code changes. Empirical study with eight practical open source projects are conducted to validate the effective of Isolation Forest in bug prediction in software code changes. Results of the empirical study show that compared to traditional classification methods used in literature, Isolation Forest can achieve better clean precision, buggy recall, buggy F-measure, AUC and Gmean.