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Exercise 6 Select an intelligent optimization method (such as tabu search, simulated annealing, genetic algorithm, ant colony optimization, particle swarm optimization, or other methods) and using programming language to implement it to solve a traveling salesman problem or a functional optimization problem.(For TŠPS, you can choose any TSP problem from https ://people.sc.fsu.edu/ jburkardt/datasets/tsp/tsp.html to solve, but I suggest a median scale is OK, such as FRI26 with 26 cities. For function optimization, search the minimal value of f(x), and f(x) = - E sin (x:) (sin (ix?/7))²m, where n = 10, m = 10, subject to 0 < x; < T, i = 1, .., n. There are n! local optima and the global minimum value for n = 10 is f(x*) = -9.660152. ) (Note, %3D .... this exercise is optional. If you complete it, also Submit the source file in .zip format)

Exercise 6 Select an intelligent optimization method (such as tabu search, simulated annealing, genetic algorithm, ant colony optimization, particle swarm optimization, or other methods) and using programming language to implement it to solve a traveling salesman problem or a functional optimization problem.(For TŠPS, you can choose any TSP problem from https ://people.sc.fsu.edu/ jburkardt/datasets/tsp/tsp.html to solve, but I suggest a median scale is OK, such as FRI26 with 26 cities. For function optimization, search the minimal value of f(x), and f(x) = - E sin (x:) (sin (ix?/7))²m, where n = 10, m = 10, subject to 0 < x; < T, i = 1, .., n. There are n! local optima and the global minimum value for n = 10 is f(x*) = -9.660152. ) (Note, %3D .... this exercise is optional. If you complete it, also Submit the source file in .zip format)

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:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Artificial Intelligence
Exercise 6 Select an intelligent optimization method (such as tabu search, simulated annealing, genetic algorithm, ant colony optimization, particle swarm optimization, or other methods) and using programming language to implement it to solve a traveling salesman problem or a functional optimization problem. (For TŠPS, you can choose any TSP problem from https ://people.sc.fsu.edu/ jburkardt/datasets/tsp/tsp.html to solve, but I suggest a median scale is OK, such as FRI26 with 26 cities. For function optimization, search the minimal 2m value of f(x), and f(x) = - E1 sin (x:) (sin (ix?/T))™, where n = 1,.., n. There are n! local optima and the global minimum value for n = 10 is f(x*) = -9.660152. ) (Note, 10, m = 10, subject to 0 < x; < T,i = this exercise is optional. If you complete it, also Submit the source file in .zip format)
Transcribed Image Text:Exercise 6 Select an intelligent optimization method (such as tabu search, simulated annealing, genetic algorithm, ant colony optimization, particle swarm optimization, or other methods) and using programming language to implement it to solve a traveling salesman problem or a functional optimization problem. (For TŠPS, you can choose any TSP problem from https ://people.sc.fsu.edu/ jburkardt/datasets/tsp/tsp.html to solve, but I suggest a median scale is OK, such as FRI26 with 26 cities. For function optimization, search the minimal 2m value of f(x), and f(x) = - E1 sin (x:) (sin (ix?/T))™, where n = 1,.., n. There are n! local optima and the global minimum value for n = 10 is f(x*) = -9.660152. ) (Note, 10, m = 10, subject to 0 < x; < T,i = this exercise is optional. If you complete it, also Submit the source file in .zip format)
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