基于自适应混合非支配个体排序策略的改进型NSGA-Ⅱ算法

针对经典快速非支配排序遗传算法(NSGA-Ⅱ)中基于拥挤距离的种群多样性保持策略不能客观反映个体间真实拥挤程度的问题,提出了一种基于自适应混合非支配个体排序策略的改进型NSGA-Ⅱ算法(NSGA-Ⅱ^h)。首先,设计一种新的循环聚类个体排序策略;然后,根据Pareto分层信息来对基于经典拥挤距离和循环聚类的两种个体排序策略进行自适应的选择;最终,实现对进化后期的种群多样性保持机制的改进。通过5个标准测试函数进行算法验证,并与经典的NSGA-Ⅱ、多目标粒子群优化算法(MOPSO)和GDE3等算法进行对比分析,NSGA-Ⅱ^h算法获得了80%的最优反向世代距离(IGD)值,且显著性水平为5%的双尾t检验结果表明,新算法具有明显统计意义上的性能优势。改进算法不仅能提高进化种群的分布性,而且能增强算法的收敛性,有效提高了优化效果。

Improved NSGA-Ⅱ algorithm based on adaptive hybrid non-dominated individual sorting strategy

In order to solve the problem that the population diversity preservation strategy only based on crowding distance of Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ) cannot reflect the real crowding degree of individuals, an improved NSGA-Ⅱ algorithm based on the adaptive hybrid non-dominated individual sorting strategy (NSGA-Ⅱ^h) was proposed. First, a novel loop-clustering individual sorting strategy was designed. Second, according to the Pareto layer-sorting information the NSGA-Ⅱ^h algorithm adaptively chose one from the two individual sorting strategies based on classical crowding distance and loop-clustering. Finally, the diversity maintain mechanism could be improved especially during the late period of evolutionary optimization. The NSGA-Ⅱ^h algorithm was compared with three classical algorithms including NSGA-Ⅱ, Multi-Objective Particle Swarm Optimization (MOPSO) and GDE3. The experiments on five multi-objective benchmark functions show that the NSGA-Ⅱ^h algorithm can acquire 80% of optimal Inverted Generational Distance (IGD) values, and the corresponding two-tailed t-test results at a 0.05 level of significance are remarkable. The proposed algorithm can not only improve convergence of the original algorithm, but also enhance the distribution of Pareto optimal set.