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Transcript of Neural Network to solve Traveling Salesman Problem Amit Goyal 01005009 Koustubh Vachhani 01005021...
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Neural Network to solve Traveling Salesman Problem
Amit Goyal 01005009 Koustubh Vachhani 01005021Ankur Jain 01D05007
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Roadmap Hopfield Neural Network
Solving TSP using Hopfield Network
Modification of Hopfield Neural Network
Solving TSP using Concurrent Neural Network
Comparison between Neural Network and SOM for solving TSP
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Background Neural Networks
Computing device composed of processing elements called neurons
Processing power comes from interconnection between neurons
Various models are Hopfield, Back propagation, Perceptron, Kohonen Net etc
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Associative memory Associative memory
Produces for any input pattern a similar stored pattern
Retrieval by part of data Noisy input can be also recognized
Original Degraded Reconstruction
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Hopfield Network Recurrent network
Feedback from output to input
Fully connected Every neuron connected
to every other neuron
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Hopfield Network Symmetric connections
Connection weights from unit i to unit j and from unit j to unit i are identical for all i and j
No self connection, so weight matrix is 0-diagonal and symmetric
Logic levels are +1 and -1
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Computation For any neuron i, at an instant t input is
Σj = 1 to n, j≠i wij σj(t) σj(t) is the activation of the jth neuron
Threshold function θ = 0 Activation σi(t+1)=sgn(Σj=1 to n, j≠i wijσj(t)) where
Sgn(x) = +1 x>0Sgn(x) = -1 x<0
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Modes of operation Synchronous
All neurons are updated simultaneously
Asynchronous Simple : Only one unit is randomly selected
at each step
General : Neurons update themselves independently and randomly based on probability distribution over time.
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Stability Issue of stability arises since there
is a feedback in Hopfield network May lead to fixed point, limit cycle
or chaos Fixed point : unique point attractor Limit cycles : state space repeats
itself in periodic cycles Chaotic : aperiodic strange attractor
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Procedure Store and stabilize the vector
which has to be part of memory.
Find the value of weight wij, for all i, j such that :<σ1, σ2, σ3 …… σN> is stable in Hopfield
Network of N neurons.
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Weight learning Weight learning is given by
wij = 1/(N-1) σi σj 1/(N-1) is Normalizing factor
σi σj derives from Hebb’s rule If two connected neurons are ON then
weight of the connection is such that mutual excitation is sustained.
Similarly, if two neurons inhibit each other then the connection should sustain the mutual inhibition.
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Multiple Vectors If multiple vectors need to be
stored in memory like<σ1
1, σ21, σ3
1 …… σN1>
<σ12, σ2
2, σ32 …… σN
2>……………………………….<σ1
p, σ2p, σ3
p …… σNp>
Then the weight are given by:wij = 1/(N-1) Σm=1 to pσi
m σj
m
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Energy Energy is associated with the state
of the system.
Some patterns need to be made stable this corresponds to minimum energy state of the system.
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Energy function Energy at state σ’ = <σ1, σ2, σ3 …… σN>
E(σ’) = -½ Σi Σj≠i wij σiσj
Let the pth neuron change its state from σp
initial to σp
final so Einitial = -½ Σj≠p wpj σp
initial σj + T Efinal = -½ Σj≠p wpj σp
final σj + T ΔE = Efinal – Einitial
T is independent of σp
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Continued…ΔE = - ½ (σp
final - σpinitial ) Σj≠p wpj σj
i.e. ΔE = -½ Δσp Σj≠p wpj σj
Thus: ΔE = -½ Δσp x (netinputp)
If p changes from +1 to -1 then Δσp is negative and netinputp is negative and vice versa.
So, ΔE is always negative. Thus energy always decreases when neuron changes state.
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Applications of Hopfield Nets Hopfield nets are applied for
Optimization problems. Optimization problems maximize
or minimize a function. In Hopfield Network the energy
gets minimized.
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Traveling Salesman Problem
Given a set of cities and the distances between them, determine the shortest closed path passing through all the cities exactly once.
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Traveling Salesman Problem One of the classic and highly researched
problem in the field of computer science.
Decision problem “Is there a tour with length less than k" is NP - Complete
Optimization problem “What is the shortest tour?” is NP - Hard
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Hopfield Net for TSP N cities are
represented by an N X N matrix of neurons
Each row has exactly one 1
Each column has exactly one 1
Matrix has exactly N 1’s
σkj = 1 if city k is in position jσkj = 0 otherwise
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Hopfield Net for TSP For each element of the matrix
take a neuron and fully connect the assembly with symmetric weights
Finding a suitable energy function E
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Determination of Energy Function E function for TSP has four components
satisfying four constraints
Each city can have no more than oneposition i.e. each row can have no morethan one activated neuron
E1= A/2 Σk Σi Σj≠i σki σkj A - Constant
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Energy Function (Contd..) Each position contains no more
than one city i.e. each column contains no more than one activated neuron
E2= B/2 Σj Σk Σr≠k σkj σrj B - constant
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Energy Function (Contd..) There are exactly N entries in the
output matrix i.e. there are N 1’s in the output matrix
E3= C/2 (n - ΣkΣi σki)2 C - constant
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Energy Function (cont..) Fourth term incorporates the
requirement of the shortest path
E4= D/2 ΣkΣr≠kΣj dkr σkj(σr(j+1) + σr(j-1))
where dkr is the distance between city-k and city-r
Etotal = E1 + E2 + E3 + E4
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Energy Function (cont..) Energy equation is also given by
E= -½ΣkiΣrj w(ki)(rj) σki σrj
σki – City k at position iσrj – City r at position j
Output function σki
σki = ½ ( 1 + tanh(uki/u0))u0 is a constantuki is the net input
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Weight Value Comparing above equations with the
energy equation obtained previouslyW(ki)(rj) = -A δkr(1 – δrj) - Bδij(1 – δkr) –C –Ddkr(δj(i+1) + δj(i-1))
Kronecker Symbol : δkr δkr = 1 when k = r δkr = 0 when k ≠ r
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Observation Choice of constants A,B,C and D that
provide a good solution vary between Always obtain legitimate loops (D is
small relative to A, B and C)
Giving heavier weights to the distances (D is large relative to A, B and C)
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Observation (cont..) Local minima
Energy function full of dips, valleys and local minima
Speed Fast due to rapid computational
capacity of network
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Concurrent Neural Network Proposed by N. Toomarian in 1988
It requires N(log(N)) neurons to compute TSP of N cities.
It also has a much higher probability to reach a valid tour.
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Objective Function
Aim is to minimize the distance between city k at position i and city r at position i+1
Ei = Σk≠rΣrΣi δkiδr(i+1) dkr
Where δ is the Kronecers Symbol
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Cont …
Ei = 1/N2 Σk≠rΣrΣi dkr Πi= 1 to ln(N) [1 + (2עi – 1) σki] [1 + (2µi – 1) σri]
Where (2µi – 1) = (2עi – 1) [1 – Πj= 1 to i-1 עi ]Also to ensure that 2 cities don’t
occupy same positionEerror = Σk≠rΣr δkr
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Solution Eerror will have a value 0 for any
valid tour. So we have a constrained
optimization problem to solve.
E = Ei + λ Eerror λ is the Lagrange multiplier to be
calculated form the solution.
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Minimization of energy function Minimizing Energy function which is
in terms of σki Algorithm is an iterative procedure
which is usually used for minimization of quadratic functions
The iteration steps are carried out in the direction of steepest decent with respect to the energy function E
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Minimization of energy function Differentiating the energy
dUki/dt = - δE/ δσki = - δEi/ δσki - λδEerror/ δσki
dλ/dt = ± δE/ δλ = ± Eerror
σki = tanh(αUki) , α – const.
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Implementation Initial Input Matrix and the value of λ is
randomly selected and specified
At each iteration, new value of σki and λ is calculated in the direction of steepest descent of energy function
Iterations will stop either when convergence is achieved or when the number of iterations exceeds a user specified number
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Comparison – Hopfield vs Concurrent NN Converges faster than Hopfield
Network
Probability to achieve valid tour is higher than Hopfield Network
Hopfield doesn’t have systematic way to determine the constant terms.
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Comparison – SOM and Concurrent NN Data set consists of 52 cities in
Germany and its subset of 15 cities. Both algorithms were run for 80
times on 15 city data set. 52 city dataset could be analyzed
only using SOM while Concurrent Neural Net failed to analyze this dataset.
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Result Concurrent neural network always
converged and never missed any city, where as SOM is capable of missing cities.
Concurrent Neural Network is very erratic in behavior , whereas SOM has higher reliability to detect every link in smallest path.
Overall Concurrent Neural Network performed poorly as compared to SOM.
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Shortest path generatedConcurrent Neural Network (2127 km) Self Organizing Maps (1311km)
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Behavior in terms of probability
Concurrent Neural Network Self Organizing Maps
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Conclusion Hopfield Network can also be used
for optimization problems. Concurrent Neural Network
performs better than Hopfield network and uses less neurons.
Concurrent and Hopfield Neural Network are less efficient than SOM for solving TSP.
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References N. K. Bose and P. Liang, ”Neural Network
Fundamentals with Graphs, Algorithms and Applications”, Tata McGraw Hill Publication, 1996
P. D. Wasserman, “Neural computing: theory and practice”, Van Nostrand Reinhold Co., 1989
N. Toomarian, “A Concurrent Neural Network algorithm for the Traveling Salesman Problem”, ACM Proceedings of the third conference on Hypercube concurrent computers and applications, pp. 1483-1490, 1988.
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References R. Reilly, “Neural Network approach to solving the
Traveling Salesman Problem”, Journals of Computer Science in Colleges, pp. 41-61,October 2003
Wolfram Research inc., “Tutorial on Neural Networks”, http://documents.wolfram.com/applications/neuralnetworks/NeuralNetworkTheory/2.7.0.html, 2004
Prof. P. Bhattacharyya, “Introduction to computing with Neural Nets”,http://www.cse.iitb.ac.in/~pb/Teaching.html.
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NP-complete NP-hard When a decision version of a
combinatorial optimization problem is proved to belong to the class of NP-complete problems, which includes well-known problems such as satisfiability,traveling salesman, the bin packing problem, etc., then the optimization version is NP-hard.
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NP-complete NP-hard “Is there a tour with length less than
k" is NP-complete: It is easy to determine if a proposed
certificate has length less than k The optimization problem : "what is the shortest tour?", is NP-
hard Since there is no easy way to determine if a certificate is the shortest.
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Path lengths
Concurrent Neural Network Self Organizing Maps