Using Discrete Particle Swarm Optimization to Find Optimal Non-collapsing and Space-filling Experimental Designs

Uniformity of experimental designs is an important issue in computer experiments recent years. To reduce the cost of handling experiment, we need to find usable designs effectively and effeciently. A design with good space-filling and non-collapsing properties may help us get the most information under some specific cost. Since a lot amount of real problems require grid discretization, based on the framework of the discrete particle swarm optimization (DPSO), we try several strategies and propose several applied methods to discuss the multi-objective issue, and will illustrate it by handling experiments on several regular and irregular feasible domains by some DPSO-based algorithms.