JRuby 0.9.9 Released

JRuby社区宣布发布JRuby 0.9.9版本,此版本主要集中在稳定性和Ruby兼容性的改进上。包括String、Array、Hash等核心类的重大兼容性和性能改进,解决了大量Java集成问题,并提升了40%的性能。
The JRuby community is pleased to announce the release of JRuby 0.9.9.

Homepage: http://www.jruby.org/
Download: http://dist.codehaus.org/jruby/

This release has largely been a stabilization release where we have spent
more focus on Ruby compatibility. We are gearing up for a 1.0 release. Here
are some of the more significant acheivements for 0.9.9:

* Major compatibility and performance overhaul of String, Array, Hash

* Many YAML and Marshalling issues have been fixed

* Java Integration overhaul fixing many outstanding issues

* 180 Jira issues resolved

* Several more bottlenecks removed

* Rails applications like Mephisto and plugins like Goldberg are running without hitches

* Performance has improved by 40% over 0.9.8 based on YARV benchmarks
With all the hard work done by Marcin Mielżyński and Bill Dortch we are adding
them as core committers. Their contributions have made a huge difference in
our progress as of late.

We also want to thank all people who hang out on IRC, triage/report/patch
issues, and communicate on our mailing lists. Their interest has really
helped shape JRuby into a better implementation.

Please take JRuby 0.9.9 for a spin and help us root out the last issues
before our 1.0 release!
【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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