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Ant Colony Optimization

Ant Colony Optimization

Ant Colony Optimization. 22c: 145, Chapter 12. Outline. Introduction (Swarm intelligence) Natural behavior of ants First Algorithm: Ant System Improvements to Ant System Applications. Swarm Intelligence.

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758 views • 38 slides
CSE 5311: Algorithm Design and Analysis

CSE 5311: Algorithm Design and Analysis

Traveling Salesman Problem is NP-Complete by Vaishnavi Balasubramanya ID: 1000-58-3834. CSE 5311: Algorithm Design and Analysis. Part I. Traveling Salesman Problem. In the traveling salesman problem, a salesman must visit n cities.

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790 views • 30 slides
Lecture 11

Lecture 11

Lecture 11. One-way analysis of variance (Chapter 15.2). Review: Relat. between One-Sided Hypothesis Tests and CIs. Suppose we are given a CI for For the one-sided hypothesis test versus at significance level , we can conclude

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368 views • 25 slides
Makespan with Sequence Dependent Setup Time (MSDST)

Makespan with Sequence Dependent Setup Time (MSDST)

Makespan with Sequence Dependent Setup Time (MSDST). 1 | s jk | C max. Introduction to MSDST. 1 machine N jobs All the jobs are released at time 0 Objective: minimize C max. Introduction to MSDST So what makes MSDST different?.

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353 views • 22 slides
Tutorial: TSP/1-tree relaxation algorithm

Tutorial: TSP/1-tree relaxation algorithm

Tutorial: TSP/1-tree relaxation algorithm. Step 0. Set ? i = 0. Pick a step size t k . Set a small ending tolerance ? > 0. Set k = 1.

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526 views • 20 slides
2 TSP algorithms: (1) AP+B&B, (2) min spanning tree.

2 TSP algorithms: (1) AP+B&B, (2) min spanning tree.

2 TSP algorithms: (1) AP+B&B, (2) min spanning tree. The assignment problem is a relaxation of the TSP Indices: i , j = city. Parameter: c ij = cost to go from city i to city j . Variables: x ij = 1 if we drive from city i to city j , else 0. Model TSP:

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427 views • 17 slides

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