This study analyzes multiobjective d‐dimensional knapsack problems (MOd‐KP) within a comparative analysis of three multiobjective evolutionary algorithms (MOEAs): the e‐nondominated sorted genetic algorithm II (e‐NSGAII), the strength Pareto evolutionary algorithm 2 (SPEA2) and the e‐nondominated hierarchical Bayesian optimization algorithm (e‐hBOA). This study contributes new insights into the challenges posed by correlated instances of the MOd‐KP that better capture the decision interdependencies often present in real world applications. A statistical performance analysis of the algorithms uses the unary e‐indicator, the hypervolume indicator and success rate plots to demonstrate their relative effectiveness, efficiency, and reliability for the MOd‐KP instances analyzed. Our results indicate that the e‐hBOA achieves superior performance relative to e‐NSGAII and SPEA2 with increasing number of objectives, number of decisions, and correlative linkages between the two. Performance of the e‐hBOA suggests that probabilistic model building evolutionary algorithms have significant promise for expanding the size and scope of challenging multiobjective problems that can be explored.
