A Comparison between Dynamic Weighted Aggregation and NSGA-II for Multi-Objective Evolutionary Algorithms

G. Drzadzewski and M. Wineberg (Canada)


Multi-Objective Evolutionary Algorithms, Genetic Algorithms


The performance of the Dynamic Weight Aggregation system as applied to a Genetic Algorithm (DWAGA) and NSGA-II are evaluated and compared against each other. The algorithms are run on 11 test functions. The performance of the algorithms is evaluated by examining the spacing, diversity and coverage of the Pareto front, as well as each algorithm’s execution time. It is discovered that, while the NSGA-II performs better on most of the test functions, the DWAGA can outperform the NSGA-II on some of the functions, including a concave one.

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