Comparative analysis of multiobjective evolutionary algorithms for random and correlated instances of multiobjective d-dimensional knapsack problems |
| |
Authors: | Ruchit Shah |
| |
Affiliation: | a Industrial and Manufacturing Engineering, Pennsylvania State University, State College, PA 16801, USA b 231M Sackett Building, Civil and Environmental Engineering, Pennsylvania State University, State College, PA 16801, USA |
| |
Abstract: | This study analyzes multiobjective d-dimensional knapsack problems (MOd-KP) within a comparative analysis of three multiobjective evolutionary algorithms (MOEAs): the ε-nondominated sorted genetic algorithm II (ε-NSGAII), the strength Pareto evolutionary algorithm 2 (SPEA2) and the ε-nondominated hierarchical Bayesian optimization algorithm (ε-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 ε-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 ε-hBOA achieves superior performance relative to ε-NSGAII and SPEA2 with increasing number of objectives, number of decisions, and correlative linkages between the two. Performance of the ε-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. |
| |
Keywords: | Combinatorial optimization Multiobjective optimization Knapsack problem Probabilistic model building evolutionary algorithms Hierarchical Bayesian networks |
本文献已被 ScienceDirect 等数据库收录! |
|