feat: add Compressor, MFAdvisor, and refactored AcqOptimizer modules

CHANGELOG.md

@@ -1,3 +1,18 @@
+# v0.9.0 - Mar 2026
+
+### Major Changes
++ Add pipeline-based Compressor module for search space compression in high-dimensional BBO,
+  supporting dimension selection (SHAP / Correlation / Adaptive / Expert), range compression,
+  projection (REMBO / HesBO / KPCA), quantization, and filling strategies.
++ Add `SpaceAdapter` for flexible configuration space transformation.
++ Add `MFAdvisor` with unified multi-fidelity scheduler and batched suggestion support in SMBO.
++ Refactor `AcquisitionOptimizer` with modular SearchGenerator and StrategySelector abstractions.
+
+### Bug Fixes
++ Fix TL surrogate robustness for multi-fidelity data edge cases.
+
+
+
 # v0.8.4 - Sep 9, 2024
 
 ### Major Changes

README.md

@@ -24,7 +24,8 @@
 ## OpenBox: Generalized and Efficient Blackbox Optimization System
 **OpenBox** is an efficient and generalized blackbox optimization (BBO) system, which supports the following 
 characteristics: 1) **BBO with multiple objectives and constraints**, 2) **BBO with transfer learning**, 3) 
-**BBO with distributed parallelization**, 4) **BBO with multi-fidelity acceleration** and 5) **BBO with early stops**.
+**BBO with distributed parallelization**, 4) **BBO with multi-fidelity acceleration**, 5) **BBO with early stops**
+and 6) **BBO with search space compression**.
 OpenBox is designed and developed by the AutoML team from the [DAIR Lab](http://net.pku.edu.cn/~cuibin/) at Peking 
 University, and its goal is to make blackbox optimization easier to apply both in industry and academia, and help 
 facilitate data science.
@@ -116,8 +117,19 @@ The design of OpenBox follows the following principles:
            <li>Interleaved RS and LS</li>
            <li>Differential Evolution</li>
            <li>L-BFGS-B</li>
+           <li>CMA-ES</li>
+           <li>Upper Bound Maximizer</li>
           </ul>
         </ul>
+      <ul>
+        <li><b>Search Space Compressor</b></li>
+        <ul>
+          <li>Dimension Selection (SHAP, Correlation, Adaptive, Expert)</li>
+          <li>Range Compression (Boundary, KDE, SHAP-weighted)</li>
+          <li>Projection (REMBO, HesBO, KPCA)</li>
+          <li>Quantization &amp; Filling</li>
+        </ul>
+      </ul>
       </td>
       <td align="left" >
         <ul>

README_zh_CN.md

@@ -21,7 +21,7 @@
 
 ## OpenBox: 通用高效的黑盒优化系统
 **OpenBox** 是解决黑盒优化（超参数优化）问题的高效且通用的开源系统，支持以下特性： 1) **多目标与带约束的黑盒优化**。2) 
-**迁移学习**。3) **分布式并行验证**。4) **多精度优化加速**。5) **早停机制**。
+**迁移学习**。3) **分布式并行验证**。4) **多精度优化加速**。5) **早停机制**。6) **搜索空间压缩**。
 OpenBox是由北京大学[DAIR实验室](http://net.pku.edu.cn/~cuibin/)自动化机器学习（AutoML）小组设计并开发的，目标是
 使黑盒优化在学术界和工业界的应用更加便捷，并促进数据科学的发展。
 
@@ -108,8 +108,19 @@ OpenBox是一个提供通用黑盒优化服务的系统。用户可以使用REST
            <li>Interleaved RS and LS</li>
            <li>Differential Evolution</li>
            <li>L-BFGS-B</li>
+           <li>CMA-ES</li>
+           <li>Upper Bound Maximizer</li>
           </ul>
         </ul>
+      <ul>
+        <li><b>Search Space Compressor</b></li>
+        <ul>
+          <li>Dimension Selection (SHAP, Correlation, Adaptive, Expert)</li>
+          <li>Range Compression (Boundary, KDE, SHAP-weighted)</li>
+          <li>Projection (REMBO, HesBO, KPCA)</li>
+          <li>Quantization &amp; Filling</li>
+        </ul>
+      </ul>
       </td>
       <td align="left" >
         <ul>