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King Cobra Algorithm (KCA) and its application in engineering optimization
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DOI：10.1016/j.knosys.2026.116896.png)
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Abstract
En 中文
<ul class="list">
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="para0001">
Mathematical KCA formulation: The King Cobra Algorithm is formulated through a Gaussian prey-scent model and its gradient, linking the hunting metaphor to the position-update mechanism.
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<div class="u-margin-s-bottom" id="para0002">
Movement strategy refinement: A sensitivity analysis compares the reference scent model and six alternative Gaussian-part formulations to refine Strategy 1, while Strategy 2 provides a complementary movement mechanism around the current best solution.
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<div class="u-margin-s-bottom" id="para0003">
Adaptive KCA variants: The sKCA and jKCA variants extend KCA through fixed and self-adaptive phase selection mechanisms while preserving the core search structure.
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<div class="u-margin-s-bottom" id="para0004">
Benchmark and engineering evaluation: KCA-based methods are evaluated on CEC-2014/CEC-2017 benchmarks and several chemical and power engineering optimization problems.
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<div class="u-margin-s-bottom" id="para0005">
Statistical performance assessment: Friedman ranking and paired Wilcoxon tests with Holm correction support a statistically grounded interpretation of the comparative results.
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</ul>
Keywords:
Global optimization
Swarm intelligence algorithms
King Cobra Algorithm (KCA)
Real-parameter numerical optimization
Engineering optimization
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W
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