Wei Shan Dong

Two-stage based ensemble optimization framework for large-scale global optimization

Large-scale global optimization (LSGO) is a very important and challenging task in optimization domain, which is embedded in many scientific and engineering applications. In order to strengthen both effectiveness and efficiency of LSGO algorithm, this paper designs a two-stage based ensemble optimization evolutionary algorithm (EOEA) framework, which serially implements two sub-optimizers. These two sub-optimizers mainly focus on exploration and exploitation separately. The EOEA framework can be easily generated, flexibly altered and modified, according to different implementation conditions. In order to analyze the effects of EOEA’s components, we compare its performance on diverse kinds of problems with its two sub-optimizers and three variants. To show its superiorities over the previous LSGO algorithms, we compare its performance with six classical LSGO algorithms on the LSGO test functions of IEEE Congress of Evolutionary Computation (CEC 2008). The performance of EOEA is further evaluated by experimental comparison with four state-of-the-art LSGO algorithms on the test functions of CEC 2010 LSGO competition. To benchmark the practical applicability of EOEA, we adopt EOEA to the parameter calibration problem of water pipeline system. Based on the experimental results on diverse scales of systems, EOEA performs steadily and robustly.

SoLoMo analytics for telco Big Data monetization

The mobile Internet brought tremendous opportunities for businesses to capitalize on the vast amount of SoLoMo (social-location-mobile) data for delivering high-quality and personalized customer services. In this paper, we describe algorithms and technologies for discovering actionable customer insights using the combined power of social network, location pattern mining, and mobile usage analysis. We illustrate our implementation using Big Data platforms including IBM InfoSphere® BigInsights, IBM InfoSphere Streams, and IBM Netezza® Data Warehouse, while addressing various Big Data-related challenges, such as context generation of unstructured data and high-performance analytics for both data at rest and data in motion. The presented system combines location, social interactions, and user behavior data to find like-minded communities. The system leverages Big Data capabilities to attempt to scale to support the subscriber base of large telecoms in an efficient manner.