Difficult random walk modeling

In summary, the conversation is about a scenario involving a bounded 2D plane and a spawn point where units must find and bring back randomly scattered minerals. The units diffuse randomly until they find a mineral and then return to the spawn point before diffusing again to find a new mineral. The speaker is asking for any papers or resources related to mathematically modeling this scenario, and the other person suggests looking into the "bees algorithm" as a similar concept.
  • #1
Monte_Carlo
72
0
Hi guys,

I'm doing some thinking about random walk.

Imagine there is a bounded 2D plane and a single spawn point. The spawn produces units which must bring in minerals scattered around the spawn. The locations of minerals are not known, so the units diffuse randomly away from the spawn until they find a mineral. Once the mineral is picked up, it is brought back to the spawn point (i.e. once the unit found mineral, it goes to the origin in a straight line). The unit then returns (in a straight line) to the location of the last mineral pickup point and then diffuses randomly from that point until it finds a new mineral.

Could somebody please provide some papers, urls - any kind of material - to show how to mathematically model such a scenario? I'm not afraid of calculus, differential equations, etc.

Thanks,

Monte
 
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  • #2
This is not a homework, but more towards research and just thought-experiment. Any help - just a name of canonical problems related to this one - would be helpful.
 
  • #3
Not precisely what you're talking about, but are you familiar with the "bees algorithm"? It's a recently developed optimization algorithm that is similar on a general level to what you've suggested. You should be able to turn up a few papers by doing a search for "bees algorithm" or "honeybee algorithm".
 

Related to Difficult random walk modeling

1. What is a difficult random walk modeling?

A difficult random walk modeling is a mathematical model used to study the behavior of a random walker in a complex environment. It takes into account various factors such as obstacles, uneven terrain, and other environmental conditions that make the random walk more challenging to analyze.

2. What are the applications of difficult random walk modeling?

Difficult random walk modeling has various applications in fields such as physics, biology, economics, and computer science. It can be used to study the movement of particles in a fluid, the behavior of animals in their natural habitats, the stock market trends, and the performance of algorithms in complex systems.

3. What are some common techniques used in difficult random walk modeling?

Some common techniques used in difficult random walk modeling include Monte Carlo simulation, random number generation, and numerical methods such as finite difference methods and agent-based modeling. These techniques help in simulating and analyzing the behavior of random walkers in complex environments.

4. How does difficult random walk modeling differ from simple random walk modeling?

The main difference between difficult random walk modeling and simple random walk modeling is the level of complexity in the environment. Simple random walk modeling assumes a uniform and homogeneous environment, whereas difficult random walk modeling takes into account various factors that make the random walk more challenging to study.

5. What are the limitations of difficult random walk modeling?

Some limitations of difficult random walk modeling include the assumption of a perfect model, the challenge of accurately representing real-world complexities, and the difficulty in predicting long-term behavior. Additionally, the results of the modeling may also be affected by the choice of parameters and assumptions made in the model.

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