Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN: 9780133594140
Author: James Kurose, Keith Ross
Publisher: PEARSON
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Question
Using the provided Python code from the previous homework as a base, implement Simulated Annealing in code. Please address the following by including the associated Python code excerpts (as appropriate) and explanation of the code in the PDF file: • Logic to determine the initial temperature • At least two different temperature cooling schedules (the temperature update procedure) and ex- plore some options for the number of iterations performed at a given temperature (Mk) • Python logic excerpt for computing the probabilities of accepting a non-improving move Stopping criterion • Apply the code to the random problem instance and determine the best solution and objective value using the multiple variations of your algorithm (e.g., different cooling schedules, different starting temperatures, different values for Mk).
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Transcribed Image Text:Using the provided Python code from the previous homework as a base, implement Simulated Annealing in code. Please address the following by including the associated Python code excerpts (as appropriate) and explanation of the code in the PDF file: • Logic to determine the initial temperature • At least two different temperature cooling schedules (the temperature update procedure) and ex- plore some options for the number of iterations performed at a given temperature (Mk) • Python logic excerpt for computing the probabilities of accepting a non-improving move Stopping criterion • Apply the code to the random problem instance and determine the best solution and objective value using the multiple variations of your algorithm (e.g., different cooling schedules, different starting temperatures, different values for Mk).
Method Table 1: Example of results summary (numbers are not realistic) SA to Cooling, tk Mk # of temps Iterations Items selected Weight Value to 1000 10 900 2102 87 320 3180 1+0.9k 800 0.99tk-1 50 40 5333 27 230 1284 1200 0.99tk-1 50 40 5333 13 1250 2002 Pop Items GA Generations Crossover Mutation Elitism Weight size selected tel:1284%2 1000 250 0.8 0.1 top 3 23 2650 1921 500 150 0.9 0.2 top 10% 39 1650 3914 etc.
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Transcribed Image Text:Method Table 1: Example of results summary (numbers are not realistic) SA to Cooling, tk Mk # of temps Iterations Items selected Weight Value to 1000 10 900 2102 87 320 3180 1+0.9k 800 0.99tk-1 50 40 5333 27 230 1284 1200 0.99tk-1 50 40 5333 13 1250 2002 Pop Items GA Generations Crossover Mutation Elitism Weight size selected tel:1284%2 1000 250 0.8 0.1 top 3 23 2650 1921 500 150 0.9 0.2 top 10% 39 1650 3914 etc.
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