A Gradient-Based Search Method for Multi-objective Optimization Problems

被引：15

作者：

Gao, Weifeng ^{[1
]}

Wang, Yiming ^{[1
]}

Liu, Lingling ^{[1
]}

Huang, Lingling ^{[1
]}

机构：

[1] Xidian Univ, Sch Math & Stat, Xian 710126, Peoples R China

来源：

INFORMATION SCIENCES | 2021年 / 578卷

关键词：

Multi-objective optimization; Descent direction; Multi-objective evolutionary algorithm; NONDOMINATED SORTING APPROACH; EVOLUTIONARY ALGORITHMS; CONSTRAINTS;

D O I：

10.1016/j.ins.2021.07.051

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A gradient-based search method (GBSM) is developed to solve multi-objective optimiza-tion problems. It uses the multi-objective gradient information to construct descent direc-tions, i.e., Pareto descent directions (PDDs), to accelerate the convergence. In addition, a multi-objective evolutionary algorithm based on decomposition is adopted to improve the diversity. The comparisons between GBSM with several selected multi-objective evolu-tionary algorithms and gradient based algorithms on benchmark functions indicate that the proposed method performs competitively and effectively. (c) 2021 Elsevier Inc. All rights reserved.

引用

页码：129 / 146

页数：18

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