Dynamic impact for ant colony optimization algorithm

This paper proposes an extension method for Ant Colony Optimization (ACO) algorithm called Dynamic Impact. Dynamic Impact is designed to improve convergence and solution quality solving challenging optimization problems that have a non-linear relationship between resource consumption and fitness. This proposed method is tested against the real-world Microchip Manufacturing Plant Production Floor Optimization (MMPPFO) problem and the theoretical benchmark Multidimensional Knapsack problem (MKP). Using Dynamic Impact on single-objective optimization the fitness value is improved by 33.2% over the ACO algorithm without Dynamic Impact. Furthermore, MKP benchmark instances of low complexity have been solved to a 100% success rate even when a high degree of solution sparseness is observed. Large complexity instances have shown the average gap improved by 4.26 times.

Automated summary

This paper proposes an extension method called Dynamic Impact for the Ant Colony Optimization algorithm. It aims to improve convergence and solution quality for challenging optimization problems with a non-linear relationship between resource consumption and fitness. Experimental results show that Dynamic Impact significantly improves the fitness value and success rate for both real-world and theoretical benchmark problems.