.png){kind=icon}
返回
Levy Flight Trajectory-Based Whale Optimization Algorithm for Global Optimization
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2017.2695498.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The whale optimization algorithm (WOA) has been shown to be powerful in searching for an optimal solution. This paper proposes an improvement to the whale optimization algorithm that is based on a Levy flight trajectory and called the Levy flight trajectory-based whale optimization algorithm (LWOA). The LWOA makes the WOA faster and more robust and avoids premature convergence. The Levy flight trajectory is helpful for increasing the diversity of the population against premature convergence and enhancing the capability of jumping out of local optimal optima. This method helps obtaining a better tradeoff between the exploration and exploitation of the WOA. The proposed algorithm is characterized by quick convergence and high precision, and it can effectively get rid of a local optimum. The LWOA is further compared with other well-known nature-inspired algorithms on 23 benchmarks and solving infinite impulse response model identification. The statistical results on the benchmark functions show that the LWOA can significantly outperform others on a majority of the benchmark functions, especially in solving an optimization problem that has high dimensionality. Additionally, the superior identification capability of the proposed algorithm is evident from the results obtained through the simulation study compared with other algorithms. All the results prove the superiority of the LWOA.
Keyword:
Levy flight trajectory
whale optimization algorithm
global optimization
IIR system
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Analysis of the dynamics of a memoryless nonlinear gradient IIR adaptive notch filter无记忆非线性梯度IIR自适应陷波滤波器的动力学分析.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
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
分享收藏分享收藏
Clinical prediction models for mortality and functional outcome following ischemic stroke: A systematic review and meta-analysisPLOS ONEOAAI
PLOS ONEIF0
OAAI分享收藏
查看更多内容