Dynamic Structure-Based Neural Networks Determination Approach Based on the Orthogonal Genetic Algorithm with Quantization

摘要

Simulation optimization studies the problem of optimizing simulation-based objectives. This field has a strong history in engineering but often suffers from several difficulties including being time-consuming and NP-hardness. Simulation optimization is a new and hot topic in the field of system simulation and operational research. This paper presents a hybrid approach that combines Evolutionary Algorithms with Neural Networks for solving simulation optimization problems. In our research, Neural Networks are applied to replace the known simulation model for evaluating subsequent iterative solutions. Further, we apply the dynamic structure-based neural networks to learn and replace the known simulation model. The determination of dynamic structure-based neural networks is the kernel of this paper. The experimental results demonstrated that our approach can find optimal or close-to-optimal solutions, and is superior to other recent algorithms in simulation optimization.