Heuristic Optimization Athens 2004 Department of Architecture and Technology Universidad Politécnica de Madrid Víctor Robles [email protected].

Heuristic OptimizationAthens 2004

Department of Architecture and Technology

Universidad Politécnica de Madrid

Víctor Robles

[email protected]

Teachers

Universidad Politécnica de Madrid Víctor Robles (coordinator) María S. Pérez Vanessa Herves

Universidad del País Vasco Pedro Larrañaga

Course outline and Class hours

Day 1 / 10:00-14:00 / Víctor Introduction to optimization Some optimization problems About heuristics

Greedy algorithmsHill climbing

Simulated Annealing

Course outline and Class hours

Day 2 / 9:30-13:30 / Víctor, María Learn by practice: Simulated Annealing Genetic Algorithms

Day 3 / 9:30-13:30 / Vanessa Learn by practice: Genetic Algorihtms

Day 4 / 10:00-13:30 / Pedro Estimation of Distribution Algorithms

Introduce yourself

Name University / Country Optimization experience Expectations of the course

Optimization

A optimization problem is a par being the search space (all the possible solutions), and a function,

The solution is optime if,

Combinatorial optimization Systematic search

RXf :

XxoptXxxfxf opt ),()(

),( fX

Xf

State space landscape

Objective function defines state space landscape

Some optimization problems

TSP – Travel Salesman Problem The assignment problem SAT – Satistiability problem The 0-1 knapsack problemImportant tasks Find a representation of possible solutions To be able to evaluate each of the possible

solutions “fitness function” or “objective function”

TSP

A salesman has to find a route which visits each of n cities, and which minimizes the total distance travelled

Given an integer and a n x n matrix

where each is a nonnegative integer. Which cyclic permutation of integers from 1 to n minimizes the sum ?

3n)( ijcC

ijc

n

ii ic

1

TSP representations

Binary representation Each city is encoded as a string of [log2n] bits.

Example 8 cities 3 bits Path representation

A list is represented as a list of n cities. If city i is the j-th element of the list, city i is the j-th city to be visited

Adjancecy representation City j is listed in position i if the tour leads from city i to

city j(3 5 7 6 4 8 2 1) tour: 1-3-7-2-5-4-6-8

The assignment problem

A set of n resources is available to carry out n tasks. If resource i is assigned to task j, it cost

units.

Find an assignment that minimizes

Solution: permutition of the numbers},...,1{ n

ijc},...,{ 1 n

n

ii ic

1

SAT

The satisfiability problem consists on finding a truth assignment that satisfied a well-formed Boolean expression

Many applications: VLSI test and verification, consistency maintenance, fault diagnosis, etc

MAX-SAT: Find an assignment which satisfied the maximum number of clauses

SAT

Data sets in conjunctive normal form (cnf) Example

Literals: Clauses:

Representation? Fitness function?

51,..., xx

351 )( xxx

...

)()( 12351 xxxxx

The 0-1 knapsack problem

A set of n items is available to be packed into a knapsack with capacity C units. Item i has value vi and uses up ci units of capacity. Determine the subset of items which should be packed to maximize the total value without exceding the capacity

Representation? Fitness function?

Heuristics

Faster than mathematical optimization (branch & bound, simplex, etc)

Well developed good solutions for some problems

Special heuristics: Greedy algorithms – systematic Hill-climbing (based on neighbourhood

search) – randomized

Greedy algorithms

Step-by-step algorithms Sometimes works well for optimization problems A greedy algorithm works in phases. At each

phase: You take the best you can get right now, without

regard for future consequences You hope that by choosing a local optimum at each

step, you will end up at a global optimum

Example: Counting money

Suppose you want to count out a certain amount of money, using the fewest possible bills and coins

A greedy algorithm would do this would be:At each step, take the largest possible bill or coin that does not overshoot Example: To make $6.39, you can choose:

a $5 bill a $1 bill, to make $6 a 25¢ coin, to make $6.25 A 10¢ coin, to make $6.35 four 1¢ coins, to make $6.39

For US money, the greedy algorithm always gives the optimum solution

A failure of the greedy algorithm

In some (fictional) monetary system, “krons” come in 1 kron, 7 kron, and 10 kron coins

Using a greedy algorithm to count out 15 krons, you would get

A 10 kron piece Five 1 kron pieces, for a total of 15 krons This requires six coins

A better solution would be to use two 7 kron pieces and one 1 kron piece

This only requires three coins The greedy algorithm results in a solution, but not in an

optimal solution

Practice

Develop a greedy algorithm for the knapsack problem. Groups of 2 persons

Local search algorithms

Based on neighbourhood system Neighbourhood system:

being X the search space, we define the neighbourhood system N in X

Examples: TSP (2-opt), SAT, assignment and knap

)(: xNxN

Local search algorithms

Basic principles: Keep only a single (complete) state in memory Generate only the neighbours of that state Keep one of the neighbours and discard others

Key features: No search paths Neither systematic nor incremental

Key advantages: Use very little memory (constant amount) Find solutions in search spaces too large for systematic

algorithms

TSP – 2 opt

A

B

C

D

New distance = Old dist – dist(A-D) – dist(B-C) + dist(A-B) + dist (C-D)

Neighbourhood search (Reeves93)

1. (Initialization)i. Select a starting solutionii. Current best and

2. (Choice and termination)i. Choice . If choice criteria cannot

be satisfied or if other termination criteria apply, then the method stops

3. (Update)i. Re-set , and if ,

perform step 1.ii. Return to Step 2

Xxnownowbest xx )( bestxfbest_cost

)( nownext xNx

nextnow xx best_cost)f(xnow

Hill climbing

Diferent procedures depending on choice criteria and termination criteria

Hill climbing: only permit moves to neighbours that improve the current

2. (Choice and termination)i. Choose such that and terminate if no such can be found

)( nowxf

)( nownext xNx )()( nownext xcxc

nextx

Hill-climbing: 8-Queens problem

Complete state formulation: All 8 queens on the board,one per column Neighbourhood: move one queen to a

different place in the same column Fitness function: number of pairs of queens

that are attacking each other

8-Queens problem:fitness values of neighbourhood

8-Queens problem:Local minimun

Problematic landscapes

Local maximum: a peek that is higher than all its neighbours, but not a global maximum

Plateau: an area where the elevation is constant Local maximum Shoulder

Ridge: a long, narrow, almost plateau-like landscape

Random-Restart Hill-Climbing

Method: Conduct a series of hill-climbing searches

from randomly generated initial states Stop when a goal is found

Analysis: Requires 1/p restarts where p is the

probability of success(1 success + 1/p failures)

Hill-Climbing: Performance on the 8-Queen Problem

From randomly generated start state Success rate:

86% - gets stuck 14% - solves problem

Average cost: 4 steps to success 3 steps to get stuck

Hill-Climbing with Sideways Moves

Sideways moves: moves at same fitness Must limit number of sideways moves! Performance on the 8-Queen problem:

Success rate: 6% - get stucks 94% - solves problem

Average cost: 21 steps to succeed 64 steps to get stuck

Hill-climbing: Further variants

Stochastic hill-climbing: Choose at random from among uphill moves

First-choice hill-climbing: Generate neighbourhood in random order Move to first generated that represents an

uphill move

Practice

Develop a hill-climbing algorithm for the knapsack problem. Groups of 2 persons

Shape of State Space Landscape

Success of hill-climbing depends on shape of landscape

Shape of landscape depends on problem formulation and fitness function

Landscapes for realistic problems often look like a worst-case scenario

NP-hard problems typically have exponential number of local-maxima

Despite the above, hill-climbers tend to have good performance

Simulated annealing

Failing on neighbourhood search Propensity to deliver solutions which are only local

optima Solutions depend on the initial solution

Reduce the chance of getting stuck in a local optimum by allowing moves to inferior solutions

Developed by Kirkpatrick ’83: Simulation of the cooling of material in a heat bath could be used to search the feasible solutions of an optimization problem

Simulated annealing

If a move from one solution to another neighbouring but inferior solution results in a change in value , the move to is still accepted if

T (temperature) – control parameter – uniform random number

bestx

nowx 0fnowx

Re Tf

]1,0[R

Simulated annealing: Intuition

Minimization problem; imagine a state space landscape on table

Let ping-pong ball from random point local minimum

Shake table ball tends to find different minimum

Shake hard at first (high temperature) but gradually reduce intensity (lower temperature)

Simulated annealing: Algorithm

current = problem.initialSatefor t=1 to

T = schedule(t)if T=0 then return = a random neighbour of

if thenelse with probability

bestxnowx bestx

)()( nownext xfxff 0f nowbest xx

nowbest xx e Tf

Simulated annealing: Simple example

Maximize

x coded as a 5-bit binary integer in [0,31]

maximum (01010) f=4100 With ‘greedy’ we can find 3 local maxima

10090060)( 23 xxxxf

Simulated annealing: Simple example

T bit string f move? new string100 1 0 0 0 1 1 2287 112 N 1 0 0 1 190 3 1 0 1 1 1 1227 1172 N 1 0 0 1 181 5 1 0 0 1 0 2692 < 0 Y 1 0 0 1 0

72,9 2 1 1 0 1 0 516 2176 N 1 0 0 1 065,6 4 1 0 0 0 0 3236 < 0 Y 1 0 0 0 0

59 3 1 0 1 0 0 2100 1136 N 1 0 0 0 0

f

The temperature is not high enough to move out of thislocal optimum

Simulated annealing: Simple example

T bit string f move? new string500 1 0 0 0 1 1 2287 112 Y 0 0 0 1 1450 3 0 0 1 1 1 3803 < 0 Y 0 0 1 1 1405 5 0 0 1 1 0 3556 247 Y 0 0 1 1 0

364,5 2 0 1 1 1 0 3684 < 0 Y 0 1 1 1 0328 4 0 1 1 0 0 3988 < 0 Y 0 1 1 0 0

295,2 3 0 1 0 0 0 3972 16 Y 0 1 0 0 0265,7 4 0 1 0 1 0 4100 < 0 Y 0 1 0 1 0239,1 5 0 1 0 1 1 4071 29 Y 0 1 0 1 1215,2 1 1 1 0 1 1 343 3728 N 0 1 0 1 1

Optimum found!!!

f

Simulated annealing: Generic decisions

Initial temperature

Should be ‘suitable high’. Most of the initial moves must be accepted (> 60%)

Cooling schedule

Temperature is reduced after every move

Two methods:

TT T

TT

1

close to 1 close to 0

Simulated annealing: Generic decisions

Number of iterations

Other factors: Reannealing Restricted neighbourhoods Order of searching

log

loglog 0TTN

fit

f

fit

TT

TTN

0

0