1. Metaheuristic algorithms: 

At the very beginning, our focus is on improving the quality of the solution of genetic algorithm and its variants, called multiple-search genetic algorithm (MSGA), and on applying it to several hard optimization problems, such as multicast routing, fixed channel assignment, data clustering, and traveling salesman problem. Since 2006, our focus has shifted to reducing the computation time of metaheuristics, based on a notion we proposed that was coined the name pattern reduction (PR). Experimental results show that with a small loss in quality, PR can successfully reduce the computation time of most of the metaheuristics in solving complex optimization problems. Since 2014, our focus has shifted to developing a brand-new metaheuristic algorithm, called search economics (SE). The basic idea of SE is in that "you get whatever solution for which you actually pay." Simulation results show that it is a much more powerful search tool than the other traditional metaheuristics for the optimization problem.

2. Applications:

In this area, our focus is on data mining, internet technology, cloud computing, and network management. In the area of data mining and internet technology, we designed a more ecient clustering search engine, and we also proposed an ecient algorithm for extracting the topic words of web documents. In the area of cloud computing and network management, we developed several ecient methods for enhancing the performance of a network environment, such as intrusion detection system. Since 2011, our focus has shifted to scheduling of cloud computing, wireless sensor network, and internet of things.