  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.

Neural and Evolutionary Computing