Design Patterns in ActionScript-Flyweight

本文探讨了ActionScript 3.0中定义字符串的多种方式,并深入介绍了对象池的概念及其如何节省内存资源。通过游戏设计中的坦克实例,展示了如何利用对象池和享元模式来高效地管理大量细粒度对象。

In Action Script 3.0 we have the following ways to define a String.

  1. var str1 : String = new String ( foo ) ;
  2. var   str2 : String = “ foo ”;
  3. var   str3 : String = String ( foo ) ;

I don’t know which way is your way, but they work the same way. Actually, all of the three String variables point to the same String object in memory. That’s to say there is only one copy of “foo” object in the memory, but three references. In other languages, such as java, are almost the same. It saves the memory. This is called object pool. And there is a similar pattern called Flyweight.

Let’s take a look at its intent.

 

 

Use sharing to support large numbers of fine-grained objects efficiently.

– By THE GOF BOOK

Do you remember the Red Alert, when I played this game with my friends; we often made many tanks, and just one or two types.

If you’re the designer of the Red Alert, what will you do? Write a tank class, and let the concrete tank inherit it. Then when the player made one, get a corresponding object in the memory. This is a solution, but maybe not a good one, because it will take many memories and the memory is a scarce resource, so, we need to fix it.

The concrete tank shares the same model. The difference is just the coordinate and the direction. If we abstract these attributes, then a kind of concrete tank can share the same object, which just including the model.

So, we create a ExtrinsicState class for the extrinsic attributes, and create a Tank interface, and let the concrete tank implements it. The concrete tank class will only have the intrinsic attributes. Note that some operations will need the extrinsic state.

So, the class diagram will be as follows.

clip_image001

And in the tank factory, we produce the concrete tank, but not always create the concrete tank; you can see the following code.

  1. public static function getTank ( key : String ) : Tank
  2. {
  3. if ( tankList [ key ] == null )
  4. {
  5. if ( key == “ Guardian )
  6. tankList [ key ] = new   GuardianTank () ;
  7. else   if ( key == “ Apocalypse )
  8. tankList [ key ] = new   ApocalypseTank () ;
  9. }
  10.  
  11. return   Tank ( tankList [ key ]) ;
  12. }

We use a tankList to hold the objects we have created, so, when the user wants a tank that we’ve already created, just return it, not need to create again.

Of course, when we do some operations, we may need the extrinsic state, so, pass the extrinsic state as a parameter, and then we get different appearance of each tank. DownloadDownload Full Project

Enjoy!

【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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