Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

Multi-Objective Search-Based Software Microbenchmark Prioritization

Ensuring that software performance does not degrade after a code change is paramount. A potential solution, particularly for libraries and frameworks, is regularly executing software microbenchmarks, a performance testing technique similar to (functional) unit tests. This often becomes infeasible due to the extensive runtimes of microbenchmark suites, however. To address that challenge, research has investigated regression testing techniques, such as test case prioritization (TCP), which reorder the execution within a microbenchmark suite to detect larger performance changes sooner. Such techniques are either designed for unit tests and perform sub-par on microbenchmarks or require complex performance models, reducing their potential application drastically. In this paper, we propose a search-based technique based on multi-objective evolutionary algorithms (MOEAs) to improve the current state of microbenchmark prioritization. The technique utilizes three objectives, i.e., coverage to maximize, coverage overlap to minimize, and historical performance change detection to maximize. We find that our technique improves over the best coverage-based, greedy baselines in terms of average percentage of fault-detection on performance (APFD-P) and Top-3 effectiveness by 26 percentage points (pp) and 43 pp (for Additional) and 17 pp and 32 pp (for Total) to 0.77 and 0.24, respectively. Employing the Indicator-Based Evolutionary Algorithm (IBEA) as MOEA leads to the best effectiveness among six MOEAs. Finally, the technique's runtime overhead is acceptable at 19% of the overall benchmark suite runtime, if we consider the enormous runtimes often spanning multiple hours. The added overhead compared to the greedy baselines is miniscule at 1%.These results mark a step forward for universally applicable performance regression testing techniques.Comment: 17 pages, 5 figure

SOFTWARE UNDER TEST DALAM PENELITIAN SOFTWARE TESTING: SEBUAH REVIEW

Software under Test (SUT) is an essential aspect of software testing research activities. Preparation of the SUT is not simple. It requires accuracy, completeness and will affect the quality of the research conducted. Currently, there are several ways to utilize an SUT in software testing research: building an own SUT, utilization of open source to build an SUT, and SUT from the repository utilization. This article discusses the results of SUT identification in many software testing studies. The research is conducted in a systematic literature review (SLR) using the Kitchenham protocol. The review process is carried out on 86 articles published in 2017-2020. The article was selected after two selection stages: the Inclusion and Exclusion Criteria and the quality assessment. The study results show that the trend of using open source is very dominant. Some researchers use open source as the basis for developing SUT, while others use SUT from a repository that provides ready-to-use SUT. In this context, utilization of the SUT from the software infrastructure repository (SIR) and Defect4J are the most significant choice of researchers

Learning to Walk Autonomously via Reset-Free Quality-Diversity

Quality-Diversity (QD) algorithms can discover large and complex behavioural repertoires consisting of both diverse and high-performing skills. However, the generation of behavioural repertoires has mainly been limited to simulation environments instead of real-world learning. This is because existing QD algorithms need large numbers of evaluations as well as episodic resets, which require manual human supervision and interventions. This paper proposes Reset-Free Quality-Diversity optimization (RF-QD) as a step towards autonomous learning for robotics in open-ended environments. We build on Dynamics-Aware Quality-Diversity (DA-QD) and introduce a behaviour selection policy that leverages the diversity of the imagined repertoire and environmental information to intelligently select of behaviours that can act as automatic resets. We demonstrate this through a task of learning to walk within defined training zones with obstacles. Our experiments show that we can learn full repertoires of legged locomotion controllers autonomously without manual resets with high sample efficiency in spite of harsh safety constraints. Finally, using an ablation of different target objectives, we show that it is important for RF-QD to have diverse types solutions available for the behaviour selection policy over solutions optimised with a specific objective. Videos and code available at https://sites.google.com/view/rf-qd

Inferring Gene-Phenotype Associations via Global Protein Complex Network Propagation

BACKGROUND: Phenotypically similar diseases have been found to be caused by functionally related genes, suggesting a modular organization of the genetic landscape of human diseases that mirrors the modularity observed in biological interaction networks. Protein complexes, as molecular machines that integrate multiple gene products to perform biological functions, express the underlying modular organization of protein-protein interaction networks. As such, protein complexes can be useful for interrogating the networks of phenome and interactome to elucidate gene-phenotype associations of diseases. METHODOLOGY/PRINCIPAL FINDINGS: We proposed a technique called RWPCN (Random Walker on Protein Complex Network) for predicting and prioritizing disease genes. The basis of RWPCN is a protein complex network constructed using existing human protein complexes and protein interaction network. To prioritize candidate disease genes for the query disease phenotypes, we compute the associations between the protein complexes and the query phenotypes in their respective protein complex and phenotype networks. We tested RWPCN on predicting gene-phenotype associations using leave-one-out cross-validation; our method was observed to outperform existing approaches. We also applied RWPCN to predict novel disease genes for two representative diseases, namely, Breast Cancer and Diabetes. CONCLUSIONS/SIGNIFICANCE: Guilt-by-association prediction and prioritization of disease genes can be enhanced by fully exploiting the underlying modular organizations of both the disease phenome and the protein interactome. Our RWPCN uses a novel protein complex network as a basis for interrogating the human phenome-interactome network. As the protein complex network can capture the underlying modularity in the biological interaction networks better than simple protein interaction networks, RWPCN was found to be able to detect and prioritize disease genes better than traditional approaches that used only protein-phenotype associations

HYBRID DATA APPROACH FOR SELECTING EFFECTIVE TEST CASES DURING THE REGRESSION TESTING

In the software industry, software testing becomes more important in the entire software development life cycle. Software testing is one of the fundamental components of software quality assurances. Software Testing Life Cycle (STLC)is a process involved in testing the complete software, which includes Regression Testing, Unit Testing, Smoke Testing, Integration Testing, Interface Testing, System Testing & etc. In the STLC of Regression testing, test case selection is one of the most important concerns for effective testing as well as cost of the testing process. During the Regression testing, executing all the test cases from existing test suite is not possible because that takes more time to test the modified software. This paper proposes new Hybrid approach that consists of modified Greedy approach for handling the test case selection and Genetic Algorithm uses effective parameter like Initial Population, Fitness Value, Test Case Combination, Test Case Crossover and Test Case Mutation for optimizing the tied test suite. By doing this, effective test cases are selected and minimized the tied test suite to reduce the cost of the testing process. Finally the result of proposed approach compared with conventional greedy approach and proved that our approach is more effective than other existing approach