A Set-Based Genetic Algorithm for Interval Many-Objective Optimization Problems
A Set-Based Genetic Algorithm for Interval Many-Objective Optimization Problems
复制标题
区间多目标优化问题的基于集合的遗传算法
DOI：
10.1109/tevc.2016.2634625
复制
发表时间：
2018-02-01
影响因子：
14.3
通讯作者：
Miao, Zhuang
中科院分区：
文献类型：
--
作者：
Gong, Dunwei;Sun, Jing;Miao, Zhuang
区间多目标优化问题（ImaOPs）是一种包含三个以上目标且至少有一个目标具有区间不确定性的优化问题，在实际应用中普遍存在。然而，解决这些问题的有效方法很少。在本文中，我们提出了一种基于集合的遗传算法来有效地解决这些问题。首先将原始优化问题转化为确定性双目标问题，其中新的目标是超大容量和不精确性。然后定义了一个基于集合的Pareto优势关系来修正NSGA-II中的快速非支配排序方法。此外，提出了基于集合的进化方案。最后，我们的方法进行了实证评估，对39个基准IMAOP以及汽车驾驶室设计问题，并与两个典型的方法进行了比较。数值结果表明，我们的方法的优越性，并表明，一个折衷的近似前沿之间的收敛性和不确定性。
Interval many-objective optimization problems (IMaOPs), involving more than three objectives and at least one subjected to interval uncertainty, are ubiquitous in real-world applications. However, there have been very few effective methods for solving these problems. In this paper, we proposed a set-based genetic algorithm to effectively solve them. The original optimization problem was first transformed into a deterministic bi-objective problem, where new objectives are hyper-volume and imprecision. A set-based Pareto dominance relation was then defined to modify the fast nondominated sorting approach in NSGA-II. Additionally, set-based evolutionary schemes were suggested. Finally, our method was empirically evaluated on 39 benchmark IMaOPs as well as a car cab design problem and compared with two typical methods. The numerical results demonstrated the superiority of our method and indicated that a tradeoff approximate front between convergence and uncertainty can be produced.