AI Article Synopsis
- - A new optimization method called the Willow Catkin Optimization (WCO) algorithm was introduced, which uses a two-step process: spreading seeds to explore solutions and aggregating seeds to refine them.
- - The WCO algorithm aims to find both local and global optimal solutions for various optimization problems.
- - Its effectiveness was tested on 30 benchmark functions and applied to improve the co-localization of moving nodes in Wireless Sensor Networks, demonstrating promising results.
Article Abstract
The heuristic optimization algorithm is a popular optimization method for solving optimization problems. A novel meta-heuristic algorithm was proposed in this paper, which is called the Willow Catkin Optimization (WCO) algorithm. It mainly consists of two processes: spreading seeds and aggregating seeds. In the first process, WCO tries to make the seeds explore the solution space to find the local optimal solutions. In the second process, it works to develop each optimal local solution and find the optimal global solution. In the experimental section, the performance of WCO is tested with 30 test functions from CEC 2017. WCO was applied in the Time Difference of Arrival and Frequency Difference of Arrival (TDOA-FDOA) co-localization problem of moving nodes in Wireless Sensor Networks (WSNs). Experimental results show the performance and applicability of the WCO algorithm.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858194 | PMC |
| http://dx.doi.org/10.3390/e25010171 | DOI Listing |
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