OPTIMAL COMPUTING BUDGET ALLOCATION FOR SIMULATION BASED OPTIMIZATION AND COMPLEX DECISION MAKING

Optimal Computing Budget Allocation (OCBA) considers the problem how to get a best result based on the simulation output under a computing budget constraint. It is not only an efficient ranking and selection procedure for simulation problems with finite candidate solutions but also an attractive concept of resource allocation under stochastic environment. In this thesis, the framework of optimal computing budget allocation is studied in detail and improved from both theoretical aspect and practical aspect. From the perspective of problem setting, we extend OCBA to optimal subset selection problem and optimization problem with correlation between designs. From the perspective of OCBA application, we employ the concept of OCBA and derive the optimal computing budget allocation schemes for PSO and AHP respectively to improve these methods? performance. The research work of this thesis may provide a more general and more efficient computing allocation scheme for optimization problems.

Author dc:creator

• ZHANG SI

• simulation optimization, ranking and selection, optimal computing budget allocation, searching algorithms, particle swarm optimization, AHP