Lucene2.3.2发布了

Lucene 2.3.2 版本已发布,此版本修复了多个在创建和修改索引过程中可能导致索引损坏或程序死锁的问题,并改进了 TermVectors 使用时的内存管理。
最新的Lucene2.3.2发布了,主要修改了一些创建和修改索引时候的Bug。

======================= Release 2.3.2 2008-05-05 =======================

Bug fixes

1. LUCENE-1191: On hitting OutOfMemoryError in any index-modifying
methods in IndexWriter, do not commit any further changes to the
index to prevent risk of possible corruption. (Mike McCandless)

2. LUCENE-1197: Fixed issue whereby IndexWriter would flush by RAM
too early when TermVectors were in use. (Mike McCandless)

3. LUCENE-1198: Don't corrupt index if an exception happens inside
DocumentsWriter.init (Mike McCandless)

4. LUCENE-1199: Added defensive check for null indexReader before
calling close in IndexModifier.close() (Mike McCandless)

5. LUCENE-1200: Fix rare deadlock case in addIndexes* when
ConcurrentMergeScheduler is in use (Mike McCandless)

6. LUCENE-1208: Fix deadlock case on hitting an exception while
processing a document that had triggered a flush (Mike McCandless)

7. LUCENE-1210: Fix deadlock case on hitting an exception while
starting a merge when using ConcurrentMergeScheduler (Mike McCandless)

8. LUCENE-1222: Fix IndexWriter.doAfterFlush to always be called on
flush (Mark Ferguson via Mike McCandless)

9. LUCENE-1226: Fixed IndexWriter.addIndexes(IndexReader[]) to commit
successfully created compound files. (Michael Busch)

10. LUCENE-1150: Re-expose StandardTokenizer's constants publicly;
this was accidentally lost with LUCENE-966. (Nicolas Lalevée via
Mike McCandless)

11. LUCENE-1262: Fixed bug in BufferedIndexReader.refill whereby on
hitting an exception in readInternal, the buffer is incorrectly
filled with stale bytes such that subsequent calls to readByte()
return incorrect results. (Trejkaz via Mike McCandless)

12. LUCENE-1270: Fixed intermittant case where IndexWriter.close()
would hang after IndexWriter.addIndexesNoOptimize had been
called. (Stu Hood via Mike McCandless)

Build

1. LUCENE-1230: Include *pom.xml* in source release files. (Michael Busch)
【无人机】基于改进粒子群算法的无人机路径规划研究[和遗传算法、粒子群算法进行比较](Matlab代码实现)内容概要:本文围绕基于改进粒子群算法的无人机路径规划展开研究,重点探讨了在复杂环境中利用改进粒子群算法(PSO)实现无人机三维路径规划的方法,并将其与遗传算法(GA)、标准粒子群算法等传统优化算法进行对比分析。研究内容涵盖路径规划的多目标优化、避障策略、航路点约束以及算法收敛性和寻优能力的评估,所有实验均通过Matlab代码实现,提供了完整的仿真验证流程。文章还提到了多种智能优化算法在无人机路径规划中的应用比较,突出了改进PSO在收敛速度和全局寻优方面的优势。; 适合人群:具备一定Matlab编程基础和优化算法知识的研究生、科研人员及从事无人机路径规划、智能优化算法研究的相关技术人员。; 使用场景及目标:①用于无人机在复杂地形或动态环境下的三维路径规划仿真研究;②比较不同智能优化算法(如PSO、GA、蚁群算法、RRT等)在路径规划中的性能差异;③为多目标优化问题提供算法选型和改进思路。; 阅读建议:建议读者结合文中提供的Matlab代码进行实践操作,重点关注算法的参数设置、适应度函数设计及路径约束处理方式,同时可参考文中提到的多种算法对比思路,拓展到其他智能优化算法的研究与改进中。
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