Gravitation Search Algorithm with Two Masses for Constrained Optimization

被引：0
|
作者：
Qian Kun [1 ]
Qian Weiyi [2 ]
机构：
[1] Civil Aviat Univ China, Coll Sci, Tianjin, Peoples R China
[2] Bohai Univ, Coll Math & Phys, Jinzhou, Peoples R China
来源：
2019 6TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE 2019) | 2019年
基金：
中国国家自然科学基金;
关键词：
Evolutionary computation; heuristic algorith; gravitation search algorithm; constrained optimization; SWARM; EVOLUTIONARY;
D O I：
10.1109/ICISCE48695.2019.00070
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a new gravitation search algorithm with two masses is proposed to solve constrained optimization problems. In this algorithm, each particle has two masses, namely, "feasible mass" and "infeasible mass". If the particle in the feasible region is updated, then the feasible mass is used in gravitation search algorithm, otherwise, the infeasible mass is used. This idea is based on that a feasible particle is attracted towards better feasible particles and an infeasible particle is attracted towards the feasible region. Finally, the proposed algorithm is tested on 10 benchmark problems and compared with the other algorithms. The numerical results indicate that the proposed algorithm has the better performance in solving constrained optimization problems.
引用
收藏
页码：319 / 323
页数：5
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