An improved genetic algorithm for solving simulation optimization problems

Simulation optimization studies the problem of optimizing simulation-based objectives. Simulation optimization is a new and hot topic in the field of system simulation and operational research. To improve the search efficiency, this paper presents a hybrid approach which combined genetic algorithm and local optimization technique for simulation optimization problems. Through the combination of genetic algorithms and with the local optimization method, it can maximally use the good global property of random searching and the convergence rate of a local method. This study considers the sampling procedure based on orthogonal design and quantization technology, the use of orthogonal genetic algorithm with quantization for the global exploration, and the application of local optimization technique for local exploitation. The final experimental results demonstrated that the proposed approach can find optimal or close-to-optimal solutions, and is superior to other recent algorithms in simulation optimization.