Research on improvement of real-coded genetic algorithm for solving constrained optimization problems

被引：0
|
作者：
Wang J.-Q. [1 ]
Cheng Z.-W. [1 ]
Zhang P.-L. [1 ]
Dai W.-T. [1 ]
机构：
[1] College of Engineering, Northeast Agricultural University, Harbin
来源：
Kongzhi yu Juece/Control and Decision | 2019年 / 34卷 / 05期
关键词：
Combinational mutation; Constrained optimization problems; Heuristic crossover operator; Penalty function method; Real-coded genetic algorithm;
D O I：
10.13195/j.kzyjc.2017.1425
中图分类号：
学科分类号：
摘要：
An improved real-coded genetic algorithm (IRCGA) is proposed to solve constrained optimization problems. Firstly, a sorting group selection method is proposed, which has good population diversity and is easy to realize. Then, a direction-based heuristic crossover operator (DBHX) is proposed, which can generate numerous crossover directions. and it also enables a great possibility to generate a crossover direction D to guide the chromosomes of participation crossover to move towards the optimal solution direction. Even if the crossover direction is inconsistent with D, it is also very closed to the direction D to the utmost so as to ensure that there is a great chance to produce better offspring chromosomes. Finally, aiming at the shortcoming that a single mutation operator cannot both take into account the local search ability and the global search ability, a combined mutation method is proposed, which makes the mutation operation not only take into account the local search ability, but also the global search ability. The computing results of ten examples show that the proposed IRCGA has a fast convergence speed, and also verify its effectiveness and feasibility. © 2019, Editorial Office of Control and Decision. All right reserved.
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页码：937 / 946
页数：9
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