 Melvin Alvarez
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2988257529
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feat(shade): implement SHADE adaptive DE algorithm
- Implement SHADE memory structure (Success-History Adaptive DE)
* Circular buffer for storing successful (F, CR) parameters
* Memory size H configurable (typically 10-100)
* Initialize all entries to 0.5
- Parameter sampling with probability distributions:
* F: Cauchy distribution (mean=memory_f[r], scale=0.1) for exploration
* CR: Normal distribution (mean=memory_cr[r], std=0.1) for exploitation
* Clamp both to [0, 1] range
- Memory update with weighted means:
* F: Weighted Lehmer mean (emphasizes larger values)
* CR: Weighted arithmetic mean
* Weights based on fitness improvements
- Comprehensive unit tests:
* Memory creation and initialization
* Parameter sampling (bounds checking)
* Memory update (weighted means)
* Circular buffer wraparound
* Reset functionality
- Detailed documentation in SHADE_IMPLEMENTATION.md:
* Algorithm overview and theory
* Why Cauchy for F, Normal for CR
* Configuration guidelines (memory size, population)
* Performance characteristics (10-20% improvement over jDE)
* CEC2013 benchmark results
* Future enhancements (L-SHADE, JADE)
Based on Tanabe & Fukunaga (2013): "Success-history based parameter
adaptation for Differential Evolution" IEEE CEC 2013
Part of Priority 1: Implement SHADE algorithm (Enhancement Strategy)
Status: Core memory structure complete, DE integration pending
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2026-01-03 00:12:48 +01:00 |
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