.png){kind=icon}
返回
Boosting particle swarm optimization by backtracking search algorithm for optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2023.101304.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Adjusting the search behaviors of swarm-based algorithms during their execution is a fundamental errand for addressing real-world global optimizing challenges. Along this line, scholars are actively investigating the un-visited areas of a problem domain rationally. Particle Swarm Optimization (PSO), a popular swarm-based optimization algorithm, is broadly applied to resolve different real-world problems because of its more robust searching capacity. However, in some situations, due to an unbalanced trade-off between exploitation and exploration, PSO gets stuck in a suboptimal solution. To overcome this problem, this study proposes a new ensemble algorithm called e-mPSOBSA with the aid of the reformed Backtracking Search Algorithm (BSA) and PSO. The proposed technique first integrates PSO's operational potential and then introduces BSA's exploration capability to help boost global exploration, local exploitation, and an acceptable balance during the quest process. The IEEE CEC 2014 and CEC 2017 test function suite was considered for evaluation. The outcomes were contrasted with 26 state-of-the-art algorithms, including popular PSO and BSA variants. The convergence analysis, diversity analysis, and statistical test were also executed. In addition, the projected e-mPSOBSA was employed to evaluate four unconstrained and seven constrained engineering design problems, and performances were equated with various algorithms. All these analyses endorse the better performance of the suggested e-mPSOBSA for global optimization tasks, search performance, solution accuracy, and convergence rate.
Keyword:
Particle swarm optimization
Backtracking search algorithm
Global optimization
IEEE CEC2014
Engineering design problem
期刊
IF:
8.5
论文数:
2.2K
被引数:
1.0W
机构
引用论文
.png){kind=icon}
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Ensembles strategies for backtracking search algorithm with application to engineering design optimization problems回溯搜索算法的集成策略及其在工程设计优化问题中的应用
PREAI
分享收藏A mixed sine cosine butterfly optimization algorithm for global optimization and its application一种用于全局优化的混合正弦余弦蝶形优化算法及其应用
PREAI
分享收藏
查看更多内容