| Summary: | 碩士 === 國立高雄第一科技大學 === 運籌管理所 === 94 === As the technology of computer hardware and software advances, simulation
becomes a powerful tool for performance evaluation, but not suitable for optimization.
In recent years, researchers have attempted to combine simulation and optimization
procedures to provide a complete solution. Some commercial software provides
built-in optimization packages based on these research ideas, but none is capable of
handling multi-objective problems. Although there is abundant literature on
deterministic optimization, simulation optimization is quite different because the
computation is time-consuming and the estimation is still subject to random errors.
This research proposes an evolutionary algorithm for multi-objective optimization
based on simulation experiment. The algorithm uses random search or
Latin-Hypercube Sampling to select the initial population, generates a mating pool for
each objective, uses interpolation and extrapolation operators to create the offspring
population, and updates the non-dominance set iteratively to estimate the
Pareto-optimal front of the multi-objective optimization problem. Some of its
properties are adapted from well-known algorithms such as VEGA, NSGA-II, and
scatter search. The final results found the algorithm speed up the convergence of
Pareto-optimal set and accommodate random errors of simulation experiment. Using diversity
reference set improves to search the extreme solutions and gets higher recall rate and precision
rate than without using diversity reference set.
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