دانلود پاورپوینت Ant Colony Optimisation applications Comparing PSO and ACO
نوع فایل: power point
فرمت فایل: pptx
قابل ویرایش
تعداد اسلاید : 18 صفحه
قسمتی از پاورپوینت :
Each path followed by an ant represents a candidate solution to the target problem
An ant constructs its path (candidate solution) incrementally, typically by adding one component at a time to the solution
The amount of pheromone that an ant deposits on its path is proportional to the quality of the corresponding candidate solution
When an ant has to choose between alternative paths, the larger the amount of pheromone on a path, the larger the probability of the ant choosing that path
An appropriate representation of the problem
components that an ant will use to incrementally construct a solution
A problem-dependent heuristic function () that measures the quality of each component that can be added to a solution
A rule for updating the amount of pheromone () associated with each component in a path followed by an ant
Pheromone increases in proportion to quality of the path (solution)
A probabilistic transition rule: the probability of choosing a component i is proportional to the product i  i
In a data network each data packet can follow a different route
E.g., the Internet
Problem: to find the fastest (minimum cost) path between two nodes, using routing tables at each node of the network
This is a difficult problem because:
Traffic load varies (potentially a lot) with time, in unpredictable ways
Network topology also varies with time (e.g. links break down)
The problem involves distributed processing – no central coordinator
All these characteristics suggest that ant colony algorithms are a suitable type of algorithm for this problem

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