【摘要】 布局问题是 NP完全问题 ,传统的优化算法很难求得全局最优解 ,遗传算法和模拟退火算法等的随机搜索算法的求解精度和效率不能令人满意 .文中将启发式随机搜索策略和局部优化算法相结合 ,构造混合全局寻优算法 .以旋转卫星舱布局问题的简化模型为背景 ,建立了多目标优化的数学模型 ,通过一已知最优解的布局算例与遗传算法和乘子法的计算结果比较 ,该算法求解的质量和效率更优 ,表明此算法在布局优化中具有应用潜力更多还原

【Abstract】 Packing problems are categorized as NP complete. Traditional optimization methods have difficulties to deal with such problems effectively. Recently, genetic algorithms (GA) and simulation annealing algorithms (SAA) were resorted to, but their efficiency to locate a precise result was not quite satisfactory. This paper proposes combining a heuristic random searching strategy with a local optimization algorithm, and names it Mixed Global Optimization Algorithm (MGOA) to overcome the difficulties. Multi object optimization model is formulated on a simplified satellite cabin packing problem, and taking its known optimal solution as the criteria of evaluation, MGOA is superior to the Multiplier Algorithm and an Improved GA in term of solution quality and efficiency. Therefore, the proposed MGOA has shown some potential to deal with packing problems with good expectation.更多还原