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香港六合彩-六合彩

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又鸟)也值十几块钱,还在生蛋着呢,倘若香港六合彩没吃掉蛋蛋的母亲,一天让香港六合彩生两个蛋,生到现在那简直就是天文数字,这礼应该不算轻啊!要么,香港六合彩就是虽为班主任,实际上在学校连个鸟都算不上,讲话没分量,调个人当然就心有余而力不足了. …

又鸟)也值十几块钱,还在生蛋着呢,倘若香港六合彩没吃掉蛋蛋的母亲,一天让香港六合彩生两个蛋,生到现在那简直就是天文数字,这礼应该不算轻啊!要么,香港六合彩就是虽为班主任,实际上在学校连个鸟都算不上,讲话没分量,调个人当然就心有余而力不足了.
我怕家人太麻烦,也怕再花冤枉钱,便不再坚持父亲找人让我上重点班,我暗暗下定决心,一定把重点班那帮小子比下去,让香港六合彩知道我这个普通班的学生也不是个凡角.我狠狠地啐了一口:重点班算个鸟!然后就开始了我与众不同的中学生活.
2005/04/1

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  • 1. Natural Computation CO637 Swarm Intelligence III: Ant Colony Optimisation applications Comparing PSO and ACO Alex Freitas
  • 2. Recap - basic ideas of Ant Colony Optimisation (ACO) algorithms
    • 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
  • 3. Recap - main elements in the design of an ACO algorithm
    • 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
     1   1  2   2 1 2 ? component 1 component 2
  • 4. Applying Ant Colony Optimisation (ACO) to Data Network Routing: AntNet
    • 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
  • 5. AntNet’s main data structure A pheromone matrix T associated with each node i of the network Each element  ijd of T indicates the desirability for an ant in node i and with destination node d to move to node j  i,1,1 . . .  i,1,n  i,N(i),1 . . .  i,N(i),n destination nodes . . . . . . . . . Neighbour nodes Structure of transition matrix at node i : N(i) is the number of neighbours of node i n is the total number of nodes in the network
  • 6. The AntNet Algorithm for Data Network Routing (1)
    • At regular intervals, artificial ants are launched from network nodes towards destination nodes
    • Two kinds of ants, forward and backward ants:
    • Forward ant searches for fastest path from its source to its destination node
      • A forward ant shares the same queues as data packets, collecting information about the time taken to travel to each node visited in its path
      • At each node i , each ant having destination d selects the node j to move to with probability P ijd proportional to:  ijd +  ij , where:
        •  ijd = pheromone (capture current and past status of the network)
        •  ij = instantaneous state of the queue in the link from node i to node j
        • N(i)
        •  ij = 1 – q ij / (  q ik ) , q ij = number of bits waiting to be sent from i to j
        • k=1
        •  = parameter specifying the weight of heuristic  w.r.t. pheromone 
  • 7. The AntNet Algorithm for Data Network Routing (2)
    • Once it reaches its destination, a forward ant is transformed into a backward ant , which goes back to the source along the same path (but in the opposite direction)
      • When moving backwards, the information stored in the ant (collected in the forward phase) is used to update the pheromone matrix, as a function of the goodness of the path that they followed in the forward phase
      • A backward ant does not share the same link queues as data packets; it uses higher-priority queues, to speed up the updating of pheromone matrix
      • 1 2 3
      • 4 5 6
      • At each node i in the forward ant’s path before d , pheromone  ijd (suggesting to choose neighbour j when destination is d ) is incremented by an amount inversely proportional to the forward ant’s trip time from i to d
    E.g., forward ant 1  6, backward ant 6  1
  • 8. The AntNet Algorithm for Data Network Routing (3)
    • AntNet has been extensively evaluated in experiments/ simulations with models of real networks, e.g.:
      • National Science Foundation network (USA), NSFnet
      • Japanese NTT company backbone, NTTnet
    • AntNet was compared with the best-known routing algorithms
    • Overall the performance of AntNet was very competitive with the performance of those routing algorithms
    • See sections 6.3 and 6.4 of the ACO book by Dorigo & Stutzle for details
  • 9. An application of ACO in automated customer support (1)
    • An approach to provide customer support on the web consists of a set of FAQs (Frequently Asked Questions), with the corresponding answer
    • Manual maintenance of FAQs is expensive and slow in many large-scale applications
    • Alternative: providing a dynamic database of FAQs, using some intelligent method to automatically update the FAQs and their relationships
    • We will study a system – called RightNow Web– that does that in a way loosely inspired by ant colony principles
  • 10. An Application of ACO in automated customer support (2)
    • Human visitors to the site are the ant colony members
    • Assumptions about the behaviour of human visitors:
      • Each user is accessing the web site to find an answer to a specific question in a FAQ – each FAQ will also be referred to as an Answer
      • Each user follows a directed (rather than random) approach to find Answers: i.e. each user chooses at each step the FAQ/Answer which seems to be the best Answer to her/his question
    • Basic idea: to increment the usefulness score for an Answer visited by a user, loosely analogous to dropping more pheromone in a part of a path in ant colony
  • 11. An Application of ACO in automated customer support (3)
    • Knowledge base initially contains a set of FAQs, or Answers, for the most predictable questions of users
    • Users view a table of Answers, for instance:
    • Subject Score
    • requirements for retirement 1,801
    • calculating your pension 1,769
    • pension for your dependents 1,752
    • . . .
    • List of Answers is kept in decreasing order of score, and it is dynamically updated
      • The FAQs (Answers) are continuously changing
      • Users’ interest change with time
  • 12. An Application of ACO in automated customer support (4)
    • “ Score” is proportional to the usefulness of the Answer to the user and to the the number of user queries solved by an Answer
    • Explicit and Implicit methods to compute an answer’s score:
    • Explicit: users rates the usefulness of an answer, after reading it
      • More reliable than implicit rating, but…
      • Less than 10% of users bother to rate answers
    • Implicit: based on users’ navigation through the “network” of answers – the more users read an Answer, the more its score is increased
      • Less reliable than explicit rating, has a smaller weight, but…
      • Guaranteed participation of every user, without bothering the user
  • 13. An Application of ACO in automated customer support (5)
    • Clicking on an Answer, the user can move to a related answer:
    • Subject: calculating your pension
    • Answer: for each year of service you get 1% of your final salary as your pension, up to the limit of 40% of your final salary
    • Related Answers: pension for your dependents
    • requirements for retirement
    • As users click on related Answers, they navigate through the “network” of FAQs (Answers) – users act as “ants” in an ACO
    • A 1 A 4
    • A 3
    • A 2
    Human users are “foraging” for best answer Move from answer A 1 to answer A 2 : increasing “pheromone” (score) in the link
  • 14. An Application of ACO in automated customer support (6)
    • According to a paper describing the RightNow Web system:
      • its users experienced between 10% and 99.5% reduction in customer support load
      • In some notable cases customers reported an estimated savings of more than $1.2 million per month due to reduced phone support volume
  • 15. Summary about ACO applications
    • We studied 3 types of applications of Ant Colony algorithms:
    • Travelling Salesman Problem – original and most well-known application of ACO
    • Data network routing: ants explore the network to find less overloaded routes; shorter routes  more pheromone
    • Automated customer support: human visitors to a website are considered the “ants”; more visited FAQs  more pheromone
    • In all these applications, the ants act in a completely distributed manner (no central coordinator), and the dynamic nature of the environment is clear in the data network routing and automated customer support applications
  • 16. Comparing PSO and ACO (1)
    • Both are population-based, stochastic, approximate algorithms – their “global search” hopefully finds a near-optimal solution
    • ACO uses, in addition to its generic adaptation strategy (increasing pheromone for the best solutions), problem-dependent information as a local heuristic; unlike conventional PSO, which uses only its generic adaptation strategy (moving particles towards its best neighbours’ and personal best positions)
    • An ant incrementally constructs a solution; unlike particles in PSO
    • Both are very generic heuristic methods, but in practice:
      • PSO is mainly used in problems with continuous variables, e.g. numerical function optimization
      • ACO is mainly used in problems with discrete or nominal variables, e.g. combinatorial optimization problems
    • Main drawback of ACO for coping with continuous variables: the pheromone matrix is a discrete data structure
      • Usually requires continuous variables to be discretized
  • 17. Comparing PSO and ACO (2)
    • Conventional PSO’s equations for velocity and position updating are based on computing differences between the numerical coordinates of different particles in the search space
    • This approach tends not to be appropriate for some problems (in particular combinatorial optimization ones), where what needs to be optimized is a combination of “nominal” (non-numeric) values
      • E.g., in the case of the data network routing problem, the goal is to find the optimal sequence of nodes n 1 , n 2 , ….
    • A more effective application of PSO to nominal data requires a new approach for updating velocity and position which makes sense for the problem being solved
  • 18. References M. Dorigo and T. Stutzle. Ant Colony Optimization . MIT Press, 2004. N. Monmarche, M. Slimane, G. Venturini. AntClass: discovery of clusters in numeric data by an hybridization of an ant colony with the Kmeans algorithm. Internal Report No. 213 , E3i. Laboratoire d’Informatique. Universite de Tours, France. Jan. 1999. (URL: citeseer.ist.psu.edu/559169.html) D. Warner, J.N. Richter, S.D. Durbin and B. Banerjee. Mining user session data to facilitate user interaction with a customer service knowledge base in RightNow Web. Proc. 7th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD-2000) , pp. 467-472. ACM Press, 2001.

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