Telecom carriers have announced new content services by making a partnership with content providers and offered popular video streaming to customers. Via the integration with edge networks, telecom carriers can procure various contents from content providers and place them on edge servers proximate to end users to serve real-time requests with high bandwidth and ultra-low latency. Nevertheless, it is challenging to consider the content procurement, placement, and services jointly due to the user preference, user distribution, storage capacity of edge server, economic costs, etc. Telecom carriers would like to balance the procuring, placing, and transfer costs. To address this problem, the paper formulates an optimization problem and then proposes an approximation algorithm. Finally, the simulation results manifest that our algorithm outperforms other baselines.

Dockless electric bike (E-bike) sharing has become a new urban modality of green transportation to offer convenient services. Typically, the service provider arranges a truck starting from the depot to visit multiple parking locations to replace low-energy batteries. However, visiting many parking locations may cause a considerable tour cost. One efficient way is to aggregate low-energy E-bikes together. Some incentive mechanisms are thus adopted to encourage E-bike users to move their bikes to suitable parking locations, but leading to an incentive cost. The service provider would like to balance the tour cost of the truck and the incentive cost of E-bike users. To address this problem, the paper formulates an optimization problem and then proposes an approximation algorithm. The simulation results with the real dataset show that our algorithm outperforms the other baselines.

In this paper, the resource-constrained project scheduling problem with multiple execution modes for each activity is explored. This paper aims to find a schedule of activities such that the makespan of the schedule is minimized subject to the precedence and resource constraints. We present a two-phase genetic local search algorithm that combines the genetic algorithm and the local search method to solve this problem. The first phase aims to search globally for promising areas, and the second phase aims to search more thoroughly in these promising areas. A set of elite solutions is collected during the first phase, and this set, which acts as the indication of promising areas, is utilized to construct the initial population of the second phase. By suitable applications of the mutation with a large mutation rate, the restart of the genetic local search algorithm, and the collection of good solutions in the elite set, the strength of intensification and diversification can be properly adapted and the search ability retained in a long term. Computational experiments were conducted on the standard sets of project instances, and the experimental results revealed that the proposed algorithm was effective for both the short-term (with 5000 schedules being evaluated) and the long-term (with 50000 schedules being evaluated) search in solving this problem.

The back-propagation network (BPN) is a popular data mining technique. Nevertheless, different problems may require different network architectures and parameters. Therefore, rule of thumb or "try and error" methods are usually used to determine them. However, these methods may lead worse network architectures and parameters. A dataset may contain many features; however, not all features are beneficial for classification in BPN. Therefore, a simulated annealing (SA) approach is proposed to select the beneficial subset of features and to obtain the better network architectures and parameters which result in a better classification. In order to verify the developed approach, three dataset, namely PIMA, IONOS, and CANCER from UCI (University of California, Irvine) machine learning database, are employed for evaluation, and the 10-fold cross-validation is applied to calculate the classification result. Compared with the MONNA (multiple ordinate neural network architecture) structure developed by Leazoray and Cardot, the classification accurate rates of the developed approach are superior to those of the MONNA. When the feature selection is taken into consideration, the classification accurate rates of three dataset are increased. Therefore, the developed approach can be utilized to find out the network architecture and parameters of BPN, and discover the useful attributes effectively.

Support Vector Machine (SVM) is a new technique for pattern classification, and is used in many applications. Kernel parameters set in the SVM training process, along with feature selection, will significantly impact classification accuracy. The objective of this paper was to simultaneously optimize parameters while finding a subset of features without degrading SVM classification accuracy. A simulated annealing (SA) approach for feature selection and parameters optimization was developed. Several UCI datasets are tested using the SA-based approach and the grid algorithm which is a traditional method of performing parameter searching. The developed SA-based approach was also compared with other approaches proposed by Fung and Mangasarian, and Liao et al. Results showed that the proposed SA-based approach significantly improves the classification accuracy rate and requires fewer input features for the SVM.