In aerodynamic shape optimization using meta-heuristics such as evolutionary algorithms, the body-fitted mesh method based computer fluid dynamics is often employed. However, it has difficulty in efficiency and accuracy, can undermine advantages of global methods. The generation also decides success or failure individual evaluation. this study, we developed a for on computational that robustly ...

Exact optimization algorithms are not able to provide an appropriate solution in solving optimization problems with a high-dimensional search space. In these problems, the search space grows exponentially with the problem size therefore; exhaustive search is not practical. Also, classical approximate optimization methods like greedy-based algorithms make several assumptions to solve the problem...

Web service composition is one of challenging problem among research and industry groups. Lots of human efforts are required to extract useful information out of located information. There is need to composite web services semantically as number of service providers and consumers are increasing at an alarming rate. This paper is based on comparative study of various evolutionary approaches (e.g...

The internet of things (IoT) concept has been extremely investigated in many modern smart applications, which enable a set sensors to either process the collected data locally or send them cloud for remote processing. Unfortunately, datacenters are located far away from IoT devices, and consequently, transmission may be delayed. In this paper, we investigate fog computing, is new paradigm that ...

Leveraging weak or noisy supervision for building effective machine learning models has long been an important research problem. Its importance further increased recently due to the growing need large-scale datasets train deep models. Weak could originate from multiple sources including non-expert annotators automatic labeling based on heuristics user interaction signals. There is extensive amo...

Meta-heuristics are a powerful way to approximately solve hard combinatorial optimization problems. However, for a problem, the quality of results can vary considerably from one instance to another. Understanding such a behaviour is important from a theoretical point of view, but also has practical applications such as for the generation of instances during the evaluation stage of a heuristic. ...

This paper proposes a novel hybrid t-way test generation strategy (where t indicates interaction strength), called High Level Hyper-Heuristic (HHH). HHH adopts Tabu Search as its high level meta-heuristic and leverages on the strength of four low level meta-heuristics, comprising of Teaching Learning based Optimization, Global Neighborhood Algorithm, Particle Swarm Optimization, and Cuckoo Sear...