Dynamic Impact for Ant Colony Optimization algorithm
It addresses incremental improvements for challenging optimization problems like microchip manufacturing and knapsack tasks, benefiting specific industrial and theoretical domains.
The paper tackles optimization problems with nonlinear resource-fitness relationships by proposing Dynamic Impact, an extension to Ant Colony Optimization, which improved fitness by 33.2% in a microchip manufacturing case and reduced the average gap by 4.26 times in knapsack benchmarks.
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.