.png){kind=icon}
返回
An Improved Bare Bones Particle Swarm Optimization Algorithm Based on Sequential Update Mechanism and a Modified Structure
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2025.3525603.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The past three decades have witnessed the rapid development of nature-inspired algorithms. Among these, population-based optimization algorithms have gained significant popularity due to their effectiveness in solving a wide range of problems. Particle Swarm Optimization (PSO) stands out as a pioneering algorithm in this domain. Bare-Bones Particle Swarm Optimization (BBPSO) is a simplified variant of PSO that eliminates the velocity term and additional parameters. This study introduces a novel sequential update rule for BBPSO, along with a modification to the standard algorithm. The proposed methods were evaluated on a comprehensive benchmark suite, including 36 benchmark problems from the literature, 30 benchmark problems from CEC2021, consisting of 10 basic and 20 transformed variants and 5 engineering optimization problems. Comparative analysis with standard BBPSO and other simplified PSO variants demonstrated the effectiveness of our proposed approach.
Keyword:
Bare bones particle swarm optimization
engineering optimization problems
modified bare bones particle swarm optimization
particle swarm optimization
Bare bones particle swarm optimization
engineering optimization problems
modified bare bones particle swarm optimization
particle swarm optimization
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Kinematic and kinetic differences in the execution of vertical jumps between people with good and poor ankle joint dorsiflexion踝关节背屈良好和不良的人在执行垂直跳跃时的运动学和动力学差异
.png){kind=icon}
PREAI
分享收藏
Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm蛾焰优化算法: 一种新的自然启发启发式范式
PREAI
分享收藏
查看更多内容