ArticleEntropy (Basel, Switzerland)2023
Willow Catkin Optimization Algorithm Applied in the TDOA-FDOA Joint Location Problem.
Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Quantum-SpinalNet: a hybrid deep learning approach for mammographic breast cancer detection.Frontiers in digital health · 2026Article
- An improved crayfish optimization algorithm for solving engineering optimization problems.PloS one · 2026Article
- A quasi-opposition learning and chaos local search based on walrus optimization for global optimization problems.Scientific reports · 2025Article
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5 authors.
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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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