4.7 Article

Solving interval many-objective optimization problems by combination of NSGA-III and a local fruit fly optimization algorithm

期刊

APPLIED SOFT COMPUTING
卷 114, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.asoc.2021.108096

关键词

Many-objective optimization; Fruit fly optimization algorithm; NSGA-III algorithm; Matter-element extension model; Interval

资金

  1. LiaoNing Revitalization Talents Program, China [XLYC2007091]
  2. Joint open fund project of State Key Laboratory of Coal Mine Safety Technology of Liaoning Province, China [2020-KF-13-04]

向作者/读者索取更多资源

This paper proposes an improved algorithm LFOA-NSGA-III for solving interval many-objective optimization problems (IMaOPS), which effectively enhances optimization performance and population diversity by introducing matter-element extension model, K-mean algorithm, and local fruit fly optimization algorithm. Empirical evaluation on interval benchmark test problems and unmanned aerial vehicles path planning problem shows superior results compared to other algorithms, indicating the effectiveness and applicability of LFOA-NSGA-III in IMaOPS.
Interval many-objective optimization problems (IMaOPS) are ubiquitous in practical applications. Therefore, it is of great significance to study the solving method for IMaOPS. However, there are fewer solving methods due to the uncertain interval of the objective function. In this paper, an improved NSGA-III algorithm (named LFOA-NSGA-III) is proposed to effectively solve these problems. Due to the uncertain interval in the IMaOPs, the original NSGA-III algorithm can ineffectively evaluate the relationship between the interval solution set and the reference point. So the matter-element extension model is introduced, which can make the optimized solution set close to the Pareto optimal solution. Furthermore, in order to improve the optimization performance and population diversity of the improved algorithm, the K-mean algorithm is used to solve the initial solution set, as well as a local fruit fly optimization algorithm (FOA) is combined with the genetic algorithm (GA). Finally, the LFOA-NSGA-III algorithm is empirically evaluated on eleven interval benchmark test problems and an unmanned aerial vehicles (UAVs) path planning problem. Through simulation comparisons with other different algorithms, it is concluded that the hyper-volume value, the imprecision value and the IGD value indicators are significantly better than other comparison algorithms. In addition, from a simulation experiment in application of the multi-UAVs path planning problem, it can be seen that the LFOA-NSGA-III algorithm is more effective and applicative in the IMaOPs. (C) 2021 Elsevier B.V. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据