Our Research Projects and Research Fundings：
I am a staff member of the Evolutionary Computation Research Group (leader Prof. Yuping Wang), which is active in the following areas:
Research and Application of Computational algorithms and heuristics based on iterative population-based systems. This is inspired by the Darwinian principle of survival of fitness, which is the main driving force we use for searching for novel and better solutions to a wide range of problems. Similar as other inspired algorithms, such as particle swarm optimization, are included in the scope of computational intelligence. We focus on the following problems.
1. More effective evolutionary multi-objective optimization algorithms
Many real-world problems involve multiple measures of performance, or objectives, which should be optimized simultaneously, however optimal performance according to one objective often implies unacceptably low performance in one or more of the other objective dimensions. This requires a compromise to be reached. Evolutionary algorithms are naturally suitable to this type of problem solving because EA is population-based and it is possible to generate multiple feasible solutions in a single run. In this project, you are expected to study methods that allow for more efficient computation in EMO. Standard test functions will be used for conducting experiments and analysis of the results. Comparison with at least one of the existing MO algorithms should be provided.
2. Optimization in dynamic environment
Traditionally optimization is carried out towards a single static objective, which does not change during the course of the optimization. In recent years, there have been increasing interests in using distributed evolutionary algorithms to handle an optimization task that changes its optima over time. We could take advantage of the parallel and distributed structure of a parallel evolutionary algorithm to deal with this kind of task.
3. Evolutionary multiobjective approaches to constraint handling
Constrained optimization is optimization of an objective function subject to constraints on the possible values of the domain variables. Constraints can be either equality constraints or inequality constraints. Many real-world problems must be handled with careful consideration of their constraints on variables. Evolutionary Multiobjective Optimization (EMO) shows a great promise in providing alternative efficient constraint handling techniques to the traditional methods. In EMO, constraints can be treated as secondary objectives (hard or soft constraints). By using the Pareto approach (dominance comparison), it is possible to avoid the use of penalty functions or weighted sum methods. some existing works such as NSGA II (Deb, 2002) have shown competitive performance in comparison with the traditional constraint handling methods.
4.Particles Swarm Optimisation (PSO) is a relatively new technique that searches for optimal
inspiration from flocks of animals, e.g. birds, searching for a goal. Multiple potential solutions, termed particles, are evolved based on global and local information on the worth of discovered solutions. The swarm is kept cohesive by a topological relationship between the agents, which has a velocity component to quickly home in on promising regions of search.