Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem
This work addresses optimization problems with combined stochastic and dynamic elements for researchers in evolutionary computation, though it is incremental as it extends existing multi-objective methods.
The paper tackled the dynamic chance constrained knapsack problem by introducing a 3-objective evolutionary algorithm formulation that simultaneously handles stochastic weights and dynamic capacity changes, showing advantages over a 2-objective formulation in benchmark evaluations.
Real-world optimization problems often involve stochastic and dynamic components. Evolutionary algorithms are particularly effective in these scenarios, as they can easily adapt to uncertain and changing environments but often uncertainty and dynamic changes are studied in isolation. In this paper, we explore the use of 3-objective evolutionary algorithms for the chance constrained knapsack problem with dynamic constraints. In our setting, the weights of the items are stochastic and the knapsack's capacity changes over time. We introduce a 3-objective formulation that is able to deal with the stochastic and dynamic components at the same time and is independent of the confidence level required for the constraint. This new approach is then compared to the 2-objective formulation which is limited to a single confidence level. We evaluate the approach using two different multi-objective evolutionary algorithms (MOEAs), namely the global simple evolutionary multi-objective optimizer (GSEMO) and the multi-objective evolutionary algorithm based on decomposition (MOEA/D), across various benchmark scenarios. Our analysis highlights the advantages of the 3-objective formulation over the 2-objective formulation in addressing the dynamic chance constrained knapsack problem.