.png){kind=icon}
返回
Clouded Leopard Optimization: A New Nature-Inspired Optimization Algorithm
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2022.3208700.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 proposes a new nature-inspired metaheuristic algorithm called Clouded Leopard Optimization (CLO), which mimics the natural behavior of clouded leopards in the wild. The fundamental inspiration of CLO is derived from two ways of natural behaviors of the clouded leopard, including hunting strategy and daily resting on trees. CLO is mathematically modeled in two phases of exploration and exploitation, based on the simulation of these two natural behaviors. CLO performance is evaluated in solving sixty-eight benchmark functions, including unimodal, multimodal, CEC 2015, and CEC 2017 types. The performance of CLO in solving optimization problems is compared with the performance of ten famous metaheuristic algorithms. The simulation results show that the proposed CLO approach with high ability in exploration, exploitation, and balancing between them has a high capability in optimization applications. Simulation results show that CLO performs better in most test functions than competitor algorithms. In addition, the implementation of CLO on four engineering design issues demonstrates the capability of the proposed approach in real-world applications.
Keyword:
Metaheuristics
Behavioral sciences
Search problems
Mathematical models
Biological system modeling
Statistics
Social factors
Clouded leopard
optimization
nature-inspired
optimization algorithm
metaheuristic
exploration
exploitation
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
分享收藏分享收藏
查看更多内容