DPSO와 OCBA를 적용한 시뮬레이션 최적화

This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function includes noise effects occurring in case that fitness evaluation is accomplished by computer-based experiments such as Monte Carlo or discrete-event simulations. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation-based optimization problems. In this study, performance of DPSO is evaluated with multi-modal and multi-dimensional test functions with noise effects. From the experimental results, it is shown that sample size is more important than population size in DPSO. In addition, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step.