.png){kind=icon}
返回
Enhanced superposition determination for weighted superposition attraction algorithm
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-020-04853-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper argues the efficiency enhancement study of a recent meta-heuristic algorithm, WSA, by modifying one of its operators, superposition (target point) determination procedure. The original operator is based on the weighted vector summation and has some potential disadvantages with regard to domain of the decision variables such that determining a superposition out of the search space. Such potential disadvantages may cause WSA to behave as a random search and result in an unsatisfactory performance for some problems. In order to eliminate such potential disadvantages, we propose a new superposition determination procedure for the WSA algorithm. Thus, the mWSA algorithm will be able to behave more consistent during its search and its robustness will improve significantly in comparison to its original version. The mWSA algorithm is compared against the WSA algorithm and some other algorithms taken from the existing literature on both the constrained and unconstrained optimization problems. The experimental results clearly indicate that the mWSA algorithm is an improvement for the original WSA algorithm, and also prove that the mWSA algorithm is more robust and consistent search procedure in solving complex optimization problems.
Keyword:
WSA algorithm
Superposition principle
Performance enhancement
Functional optimization
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
.png){kind=icon}
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
PREAI
分享收藏Weighted Superposition Attraction (WSA): A swarm intelligence algorithm for optimization problems - Part 2: Constrained optimization加权叠加吸引 (WSA): 优化问题的群智能算法-第2部分: 约束优化
PREAI
分享收藏Constraint-handling in genetic algorithms through the use of dominance-based tournament selection
PREAI
分享收藏A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏
查看更多内容