Improving NSGA-II using a Dynamic Average Distance Selection Strategy

Le résumé fourni par la source

The traditional Non-dominated Sorting Genetic Algorithm II (NSGA-II) struggles to maintain a uniformly distributed solution set across the entire Pareto front when dealing with non-uniform, non-convex, or discontinuous Pareto fronts. This limitation arises because its crowding distance metric relies solely on local linear spacing, making it prone to issues such as the loss of boundary solutions or local redundant clustering. To address this problem, this paper proposes an improved NSGA-II algorithm, whose core mechanism is the introduction of a dynamic average distance selection strategy into the original framework. Instead of using the traditional local crowding distance metric, the proposed algorithm constructs an "influence rectangle" for each individual using a dynamic scaling factor. This transforms the occupancy relationship of individuals in the objective space into the degree of geometric overlap between these rectangles, enabling the identification and elimination of redundant individuals. Experiments are conducted using ZDT series test functions, and the Spacing (SP) indicator is employed to evaluate the distribution uniformity of the obtained solution sets. Simulation results demonstrate that, while maintaining good convergence, the SP indicator values of the improved algorithm on the ZDT1, ZDT2, and ZDT3 test functions are significantly reduced, with a decrease ranging from 56.10% to 59.10%. This fully verifies the effectiveness of the dynamic average distance strategy in enhancing the distribution uniformity of the solution set. When addressing problems with discontinuous and concave fronts, the algorithm exhibits excellent robustness and uniform distribution capability. By incorporating an adaptive geometric evaluation criterion, the improved NSGA-II algorithm provides more reliable and stable decision support for complex multi-objective optimization problems.

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