NSGA-Ⅱ算法的改进策略研究

带精英策略的非支配排序遗传算法(NSGA-Ⅱ)在多目标优化领域具有广泛的应用,但该算法种群收敛分布不均匀,全局搜索能力较弱,算法运行速度较慢。针对这些局限性提出了改进的排序适应度策略、算术交叉算子策略、按需分层策略和设定阈值选择策略。在典型的测试函数集上的数值实验结果表明,根据这些策略改进的算法得到的非劣解集具有较好的分布性,同时收敛速度更快。

Improved strategies and researches of NSGA-H algorithm

Non-dominated Sorting Genetic Algorithm with elitism（NSGA-Ⅱ） is widely used in multi-objective optimization fields.Uneven distribution of population convergence,poor performance in global search and low running efficiency of this algorithm are analyzed in this paper.Four improved strategies are proposed according to these limitations：improved sorting strategy,arithmetic cross operator strategy,sorting rank according to the demand strategy and selecting strategy with the given threshold.The simulations prove that the non-dominated Pareto optimal solutions have better distribution and faster convergence at the same time in typical functions.