feat(quickadapter): add combined extrema weighting strategy with multi-metric aggregation

feat(quickadapter): add combined extrema weighting strategy with multi-metric aggregation

Add new 'combined' strategy to extrema weighting that aggregates multiple
metrics (amplitude, amplitude_threshold_ratio, volume_rate, speed,
efficiency_ratio, volume_weighted_efficiency_ratio) using configurable
coefficients and aggregation methods.

Features:
- New strategy type 'combined' with per-metric coefficient weighting
- Support for weighted_average and geometric_mean aggregation methods
- Normalize all metrics to [0,1] range for consistent aggregation:
  * amplitude: x/(1+x)
  * amplitude_threshold_ratio: x/(x+median)
  * volume_rate: x/(x+median)
  * speed: x/(1+x)
- Deterministic metric iteration order via COMBINED_METRICS constant
- Centralized validation in get_extrema_weighting_config()
- Comprehensive logging of new parameters

Configuration:
- metric_coefficients: dict mapping metric names to positive weights
- aggregation: 'weighted_average' (default) or 'geometric_mean'
- Empty coefficients dict defaults to equal weights (1.0) for all metrics

Documentation:
- README updated with new strategy and parameters
- Mathematical formulas for aggregation methods
- Style aligned with existing documentation conventions

Bump version: 3.10.1 -> 3.10.2