On q-steepest descent method for unconstrained multiobjective optimization problems

被引：5
|
作者：
Lai, Kin Keung [1 ]
Mishra, Shashi Kant [2 ]
Panda, Geetanjali [3 ]
Ansary, Md Abu Talhamainuddin [4 ]
Ram, Bhagwat [5 ]
机构：
[1] Shenzhen Univ, Coll Econ, Shenzhen 518060, Peoples R China
[2] Banaras Hindu Univ, Inst Sci, Dept Math, Varanasi 221005, Uttar Pradesh, India
[3] Indian Inst Technol, Dept Math, Kharagpur 721302, W Bengal, India
[4] Indian Inst Technol Kanpur, Dept Econ Sci, Kanpur 208016, Uttar Pradesh, India
[5] Banaras Hindu Univ, Inst Sci, DST Ctr Interdisciplinary Math Sci, Varanasi 221005, Uttar Pradesh, India
来源：
AIMS MATHEMATICS | 2020年 / 5卷 / 06期
关键词：
multiobjective; q-calculus; steepest descent; pareto optimality; critical point; algorithms; EVOLUTIONARY ALGORITHMS; NEWTONS METHOD; VECTOR; COMPLEXITY; STRATEGIES; VARIANCE;
D O I：
10.3934/math.2020354
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The q-gradient is the generalization of the gradient based on the q-derivative. The q-version of the steepest descent method for unconstrained multiobjective optimization problems is constructed and recovered to the classical one as q equals 1. In this method, the search process moves step by step from global at the beginning to particularly neighborhood at last. This method does not depend upon a starting point. The proposed algorithm for finding critical points is verified in the numerical examples.
引用
收藏
页码：5521 / 5540
页数：20
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