论文标题
蚂蚁菌落优化算法的动态影响
Dynamic Impact for Ant Colony Optimization algorithm
论文作者
论文摘要
本文提出了一种称为动态影响的蚂蚁菌落优化（ACO）算法的扩展方法。动态影响旨在解决与优化解决方案其他部分有关的具有挑战性的优化问题，这些问题具有非线性关系和适应性之间的非线性关系。该提出的方法已针对复杂的现实世界微芯片制造工厂生产地板优化（MMPPFO）问题以及理论基准多维背包问题（MKP）进行了测试。 MMPPFO是一个非平凡的优化问题，由于解决方案适应性值的性质依赖于收集晶圆 - lot的收集而不优先考虑任何单个晶圆晶状体。使用动态影响对单个客观优化的健身值提高了33.2％。此外，在观察到高度溶液稀少度的情况下，MKP基准实例已解决至100％的成功率，并且很大的实例显示，平均差距提高了4.26倍。算法实现表明，大小数据集以及稀疏优化问题表现出了出色的性能。
This paper proposes an extension method for Ant Colony Optimization (ACO) algorithm called Dynamic Impact. Dynamic Impact is designed to solve challenging optimization problems that has nonlinear relationship between resource consumption and fitness in relation to other part of the optimized solution. This proposed method is tested against complex real-world Microchip Manufacturing Plant Production Floor Optimization (MMPPFO) problem, as well as theoretical benchmark Multi-Dimensional Knapsack problem (MKP). MMPPFO is a non-trivial optimization problem, due the nature of solution fitness value dependence on collection of wafer-lots without prioritization of any individual wafer-lot. Using Dynamic Impact on single objective optimization fitness value is improved by 33.2%. Furthermore, MKP benchmark instances of small complexity have been solved to 100% success rate where high degree of solution sparseness is observed, and large instances have showed average gap improved by 4.26 times. Algorithm implementation demonstrated superior performance across small and large datasets and sparse optimization problems.
