Download presentation

Presentation is loading. Please wait.

Published byAlexandra Mundine Modified over 2 years ago

1
Applications of combinatorial optimisation Prabhas Chongstitvatana Faculty of Engineering Chulalongkorn University

2
Applications of combinatorial optimisation Prabhas Chongstitvatana Faculty of Engineering Chulalongkorn University

3
Combinatorial optimisation The domains of feasible solutions are discrete. Examples –Traveling salesman problem –Minimum spanning tree problem –Set-covering problem –Knapsack problem

4
Evolutionary Computation EC is a probabilistic search procedure to obtain solutions starting from a set of candidate solutions, using improving operators to “evolve” solutions. Improving operators are inspired by natural evolution.

5
Coincident Algorithm (COIN) Evolutionary Algorithms that makes use of models to generate solutions. “Estimation of Distribution Algorithms” “Competent Genetic Algorithms”

6
Model in COIN A joint probability matrix, H. Markov Chain. An entry in H xy is a probability of transition from a state x to a state y. xy a coincidence of the event x and event y.

7
Steps of the algorithm 1.Initialise H to a uniform distribution. 2.Sample a population from H. 3.Evaluate the population. 4.Select two groups of candidates: better, and worse. 5.Use these two groups to update H. 6.Repeate the steps 2-3-4-5 until satisfactory solutions are found.

8
Updating of H k denotes the step size, n the length of a candidate, r xy the number of occurrence of xy in the better-group candidates, p xy the number of occurrence of xy in the worse- group candidates. H xx are always zero.

9
Coincidence Algorithm Combinatorial Optimisation with Coincidence –Coincidences found in the good solution are good –Coincidences found in the not good solutions are not good –How about the Coincidences found in both good and not good solutions!

10
Coincidence Algorithm Initialize the Generator Generate the Population Evaluate the Population Selection Update the Generator X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 00.25 X2X2 0 X3X3 0 X4X4 0 X5X5 0 Prior Incidence Next Incidence The Generator

11
Coincidence Algorithm Initialize the Generator X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 00.25 X2X2 0 X3X3 0 X4X4 0 X5X5 0 P(X 1 |X 2 )= P(X 1 |X 3 ) = P(X 1 |X 4 ) = P(X 1 |X 5 ) = 1/(n -1) = 1/(5-1) = 0.25

12
X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 00.25 X2X2 0 X3X3 0 X4X4 0 X5X5 0 Coincidence Algorithm Initialize the Generator Generate the Population 1.Begin at any node X i 2.For each X i, choose its value according to the empirical probability h(X i |X j ). X2X2 X2X2 X3X3 X3X3 X1X1 X1X1 X4X4 X4X4 X5X5 X5X5

13
Coincidence Algorithm Initialize the Generator Generate the Population Evaluate the Population X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 00.25 X2X2 0 X3X3 0 X4X4 0 X5X5 0 PopulationFit X2X2 X3X3 X1X1 X4X4 X5X5 7 X1X1 X2X2 X3X3 X5X5 X4X4 6 X4X4 X3X3 X1X1 X2X2 X5X5 3 X1X1 X3X3 X2X2 X4X4 X5X5 2

14
Coincidence Algorithm Initialize the Generator Generate the Population Evaluate the Population Selection X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 00.25 X2X2 0 X3X3 0 X4X4 0 X5X5 0 PopulationFit X2X2 X3X3 X1X1 X4X4 X5X5 7 X1X1 X2X2 X3X3 X5X5 X4X4 6 X4X4 X3X3 X1X1 X2X2 X5X5 3 X1X1 X3X3 X2X2 X4X4 X5X5 2 Discard GoodNot Good Uniform Selection : selects from the top and bottom c percent of the population Adaptive Selection : selects from the population above and below the average in the band of two standard deviations. Selection Populatio n Top c% Bottom c%

15
Coincidence Algorithm Initialize the Generator Generate the Population Evaluate the Population Selection X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 00.25 X2X2 0 X3X3 0 X4X4 0 X5X5 0 PopulationFit X2X2 X3X3 X1X1 X4X4 X5X5 7 X1X1 X2X2 X3X3 X5X5 X4X4 6 X4X4 X3X3 X1X1 X2X2 X5X5 3 X1X1 X3X3 X2X2 X4X4 X5X5 2 Update the Generator Discard k=0.2 X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 00.25 X2X2 0.2000.400.20 X3X3 0.25 0 X4X4 0 X5X5 0 X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 0 X2X2 0.2000.400.20 X3X3 0.400.200 X4X4 0.25 0 X5X5 0 X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 00.20 0.400.20 X2X2 00.400.20 X3X3 0.400.200 X4X4 0.25 0 X5X5 0 X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 00.20 0.400.20 X2X2 00.400.20 X3X3 0.400.200 X4X4 00.40 X5X5 0.25 0 Note: Reward: If an incidence X i,X j is found in the good string, the joint probability P(X i,X j ) is rewarded by gathering the probability d from other P(X i |X else ) d = k/(n-1) = 0.05 Update the Generator

16
Coincidence Algorithm Initialize the Generator Generate the Population Evaluate the Population Selection PopulationFit X2X2 X3X3 X1X1 X4X4 X5X5 7 X1X1 X2X2 X3X3 X5X5 X4X4 6 X4X4 X3X3 X1X1 X2X2 X5X5 3 X1X1 X3X3 X2X2 X4X4 X5X5 2 Update the Generator Discard k=0.2 X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 00.20 0.400.20 X2X2 00.400.20 X3X3 0.400.200 X4X4 00.40 X5X5 0.25 0 Note: Punishment: If an incidence X i,X j is found in the not good string, the joint probability P(X i,X j ) is punished by scattering the probability d to the other P(X i |X else ) d = k/(n-1) = 0.05 Update the Generator X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 00.250.050.450.25 X2X2 0.2000.400.20 X3X3 0.400.200 X4X4 00.40 X5X5 0.25 0 X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 0 0.050.450.25 X2X2 0.2000.400.20 X3X3 0.450.0500.25 X4X4 0.20 00.40 X5X5 0.25 0 X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 0 0.050.450.25 X2X2 00.450.050.25 X3X3 0.450.0500.25 X4X4 0.20 00.40 X5X5 0.25 0 X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 0 0.050.450.25 X2X2 00.450.050.25 X3X3 0.450.0500.25 X4X4 0 X5X5 0 Note: Observation: The incidence X 4,X 5 is equally found in both good and not good strings, the joint probability P(X i,X j ) is one time rewarded and one time punished so that the joint probability P(X i,X j ) remains the same value. Update the Generator

17
X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 00.250.050.450.25 X2X2 00.450.050.25 X3X3 0.450.0500.25 X4X4 0 X5X5 0 Coincidence Algorithm Initialize the Generator Generate the Population Evaluate the Population Selection Update the Generator X1X1 X1X1 X4X4 X4X4 X3X3 X3X3 X5X5 X5X5 X2X2 X2X2

18
Computational Cost and Space 1.Generating the population requires time O(mn 2 ) and space O(mn) 2.Sorting the population requires time O(m log m) 3.The generator require space O(n 2 ) 4.Updating the joint probability matrix requires time O(mn 2 )

19
TSP

20
Multi-objective TSP The population clouds in a random 100-city 2-obj TSP

21
Role of Negative Correlation

22
U-shaped assembly line for j workers and k machines

23
Comparison for Scholl and Klein’s 297 tasks at the cycle time of 2,787 time units

24
More Information COIN homepage http://www.cp.eng.chula.ac.th/faculty/pjw/p roject/coin/index-coin.htmhttp://www.cp.eng.chula.ac.th/faculty/pjw/p roject/coin/index-coin.htm My homepage http://www.cp.eng.chula.ac.th/faculty/pjw

Similar presentations

OK

Negnevitsky, Pearson Education, 2002 1 Lecture 12 Hybrid intelligent systems: Evolutionary neural networks and fuzzy evolutionary systems n Introduction.

Negnevitsky, Pearson Education, 2002 1 Lecture 12 Hybrid intelligent systems: Evolutionary neural networks and fuzzy evolutionary systems n Introduction.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on review writing job Ppt on micro operations Ppt on solar energy collectors Ppt on intelligent manufacturing journal Ppt on latest advancement in technology Ppt on singly linked list Free ppt on natural disasters Ppt on music therapy Ppt on anorexia nervosa Ppt on earthquake for class 7