Discrete cuckoo search algorithms for test case prioritization

Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization

The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, i.e., regression testing, before being released. The efficiency and efficacy of regression testing techniques can be improved with the use of optimization approaches. This paper proposes an improved quantum-behaved particle swarm optimization approach for regression testing. The algorithm is improved by employing a fix-up mechanism to perform perturbation for the combinatorial TCP problem. Second, the dynamic contraction-expansion coefficient is used to accelerate the convergence. It is followed by an adaptive test case selection strategy to choose the modification-revealing test cases. Finally, the superfluous test cases are removed. Furthermore, the algorithm’s robustness is analyzed for fault as well as statement coverage. The empirical results reveal that the proposed algorithm performs better than the Genetic Algorithm, Bat Algorithm, Grey Wolf Optimization, Particle Swarm Optimization and its variants for prioritizing test cases. The findings show that inclusivity, test selection percentage and cost reduction percentages are higher in the case of fault coverage compared to statement coverage but at the cost of high fault detection loss (approx. 7%) at the test case reduction stage.

Location Optimization of VTS Radar Stations Considering Environmental Occlusion and Radar Attenuation

Waterway traffic monitoring is an important content in waterway traffic management. Taking into account that the number of monitored water areas is growing and that waterway traffic management capabilities are insufficient in the current situation in China, this paper investigates the location optimization of the vessel traffic service (VTS) radar station. During the research process, radar attenuation and environmental occlusion, as well as variable coverage radius and multiple covering are all considered. In terms of the radar attenuation phenomenon in the propagation process and obstacles such as mountains and islands in the real world, judgment and evaluation methods in a three-dimensional space are proposed. Moreover, a bi-objective mathematical model is then developed, as well as a modified adaptive strategy particle swarm optimization algorithm. Finally, a numerical example and a case are given to verify the effectiveness of the proposed methods, model, and algorithm. The results show the methods, model, and algorithm proposed in this paper can solve the model efficiently and provide a method to optimize the VTS radar station location in practice.

Intelligent Optimization Based Multi-Factor Deep Learning Stock Selection Model and Quantitative Trading Strategy

With the rapid development of financial research theory and artificial intelligence technology, quantitative investment has gradually entered people’s attention. Compared with traditional investment, the advantage of quantitative investment lies in quantification and refinement. In quantitative investment technology, quantitative stock selection is the foundation. Without good stock selection ability, the effect of quantitative investment will be greatly reduced. Therefore, this paper builds an effective multi-factor stock selection model based on intelligent optimization algorithms and deep learning and proposes corresponding trading strategies based on this. First of all, this paper selects 26 effective factors of financial indicators, technical indicators and public opinion to construct the factor database. Secondly, a Gated Recurrent Unit (GRU) neural network based on the Cuckoo Search (CS) optimization algorithm is used to build a stock selection model. Finally, a quantitative investment strategy is designed, and the proposed multi-factor deep learning stock selection model based on intelligent optimization is applied to practice to test its effectiveness. The results show that the quantitative trading strategy based on this model achieved a Sharpe ratio of 127.08%, an annualized rate of return of 40.66%, an excess return of 13.13% and a maximum drawdown rate of −17.38% during the back test period. Compared with other benchmark models, the proposed stock selection model achieved better back test performance.