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Internet EngineeringInternet Engineering
Czesław SmutnickiCzesław Smutnicki
Discrete Mathematics Discrete Mathematics – Discrete – Discrete OptimizationOptimization
FIND EXTREMES OF THE FUNCTION
2D
1D
DE JONG TEST FUNCTION
OPTIMIZATION TROUBLES. NICE BEGINNINGS OF BAD NEWS
LANGERMANN TEST FUNCTION
FIND EXTREMES OF THE FUNCTION
2D
OPTIMIZATION TROUBLES. EXPONENTAL NUMBER OF EXTREMES
Please wait. Calculations will last 3 289 years
INSTANCE FROM PRACTICE
! ! ?
NONLINEAR FUNCTION OF 1980 VARIABLES !!!
CURSE OF DIMENSIONALITY
OPTIMIZATION TROUBLES. TIME OF CALCULATIONS/COST OF CALCULATIONS
LAB INSTANCE
5..20 VARIABLES
NP-HARDNESS
SOLUTION SPACE
The smallest practical instance FT10 of the job-shop scheduling problem (waited 25 years for the solving), consists of 10 jobs, 10 machines, 100 operations; solution space contains 1048 discrete feasible solutions; each solution has dimension 90; the greatest currently used benchmarks have dimension 1980
dimension and size
FT 10 corresponds to printed area of 1032 km2 (Jupiter has 1010 km2) if single solution is a dot 0.01 x 0.01 mm
OPTIMIZATION TROUBLES. SIZE OF THE SOLUTION SPACE
0,0
0,2
0,4
0,6
0,8
1,0
1,2
0 10 20 30 40 50 60 70
DIST [%]
frequence [%]
FEAS ALL
0,00
0,05
0,10
0,15
0,20
0,25
0 25 50 75 100 125 150 175 200
RE [%]
frequence [%}
FEAS
Example: job-shop scheduling problem; relative Hamming distances DIST between a feasible solution and the „best” solution are distributed normally in the solution space
Goal function values are distributed normally in the solution space;
OPTIMIZATION TROUBLES. DISTRIBUTION OF THE GOAL FUNCTION VALUES
BEST
BESTRANDOMRE
0
10
20
30
40
50
60
70
80
90
1 21 41 61 81 101 121 141 161 181
DIST [%]
RE [%]
Example: job-shop scheduling problem
SIMULATION OF GOAL FUNCTION VALUES TOWARDS CENTER OF THE SPACE
OPTIMIZATION TROUBLES. FUR
0
2
4
6
8
10
12
14
16
18
0,01 0,21 0,41 0,61 0,81 1,01 1,21 1,41 1,61 1,81
DIST [%]
RE [%]
OPTIMIZATION TROUBLES. ZOOM IN ON THE FUR
Example: job-shop scheduling problem
SIMULATION OF GOAL FUNCTION VALUES TOWARDS CENTER (ZOOM)
Transformation of a sample of random solutions from the 90D space into 2D space.
OPTIMIZATION TROUBLES. STONE FOREST
PROPERTIES OF SOLUTION SPACE LANDSCAPE
BIG VALLEY – positive correlation between goal function value and the distance to optimal solution (the best found solution); in the big valley the concentration of local extremes is high. The size of the valley is usually relatively small in relation to the size of the whole solution space.
RUGGEDNESS – measure of diversity of goal function values of related (neighboring) solutions; rruggedness is greater if diversity of the goal function value in the neighborhood of this point is greater; less differentiation of the goal function value means the flat landscape.
THE NUMBER OF LOCAL EXTREMES (peaks) in relation to to the size of the solution space
DISTRIBUTION OF LOCAL EXTREMES experimental
OTHER MEASURESautocorrelation function, correlation function between random trajectories, landscape statistically isotropic, fractal landscape, correlation between genes (epitasis), correlation of the distance of fitness
CURRENT STATE IN DISCRETE OPTIMIZATION
• Packages and solvers (LINDO, CPLEX, ILOG, …)• Exact methods (B&B, DP, ILP, BLP, MILP, SUB,…)• Approximate methods (…): heuristics, metaheuristics, meta2heuristics• Quality measures of approximation (absolute, relative, …)• Analysis of quality measure (worst-case, probabilistic, experimental)• Calculation cost (pessimistic, average, experimentally tested)• Approximation schemes (AS, polynomial-time PTAS, fully polynomial-time FPTAS)• Inapproximality• Useful experimental methods (…)• „No free lunch” theorem• Public benchmarks• Parallel and distributed methods: new class of algorithms
OPTIMIZATION HISTORY/TRENDS
• Priority rules• Theory of NP-completeness• Plynomial-time algorithms• Exact methods (B&B, DP, ILP, BLP,…) • Approximation methods: quality analysis• Approximation schemes (AS, PTAS, FPTAS, …)• Inapproximality theory• Competitive analysis (on-line algorithms)• Metaheuristics• Theoretical foundations of metaheuristics• Parallel metahuristics• Theoretical foundations of parallel metaheuristics
• constructive/improvement• priority rules• random search• greedy randomized adaptive • simulated annealing• simulated jumping• estimation of distribution• tabu search• adaptive memory search• variable neighborhood search• evolutionary, genetic search• differential evolution• biochemistry methods • immunological methods• ant colony optimization• particle swarm optimization• neural networks• threshold accepting
• path search• beam search • scatter search• harmony search• path relinging• adaptive search• constraint satisfaction• descending, hill climbing• multi-agent• memetic search• bee search • intelligent water drops
* * * * *
METHODS RESISTANT
TO LOCAL EXTREMES
APPROXIMATE METHODS
EVOLUTION: DARWIN’S VIEW. GENETIC ALGORITHMS
individual, gene, chromosome, traitpopulation (structure, size, composition)crossing-over (what is the key of progress?)mutation (insurance?)sex ?democracy/elitarism
theoretical properties
individual=solution=genotype≠fenotype
GOAL OF THE NATURE? optimization, fitness, continuity preservation, follow up changes
SUCCESION: genetic material carries data for body constructionEVOLUTION: crossing over, mutationSELECTION: soft/hard
EVOLUTION: DARWIN’S VIEW. COMPONENTS
SOLUTIONSOLUTION
FEASIBILITYFEASIBILITY
SELECTION SCHEMESELECTION SCHEME
CROSSING OVERCROSSING OVER
MATTING POOLMATTING POOL
CHROMOSOMCHROMOSOM
MUTATIONMUTATION
OPERATOR MSXFOPERATOR MSXF
BIG VALLEY PHENOMENONBIG VALLEY PHENOMENON
GENOTYPEGENOTYPE
FENOTYPEFENOTYPEREPAIRINGREPAIRING
CONTROL OF CONTROL OF POPULATION DYNAMICSPOPULATION DYNAMICS
INTENSIFICATIONINTENSIFICATION
MORE …MORE …
GENE EXPRESSIONGENE EXPRESSION
CODINGCODING
LETHALITYLETHALITY
EVOLUTION: DARWIN’S VIEW. COPYING FROM THE NATURE
• control of population dynamics/preserving diversity• parents matching strategies: (sharing function to prevent too close relative
parents; incest preventing by using Hamming distance to evaluate genotype similarity)
• structures of the population (migration, diffusion models)• social behavior patterns (satisfied, glad, disappointed -> clonning, crossing-
over, mutation)• adaptive mutation• gene expression• distributed populations• …
EVOLUTION: DARWIN’S VIEW. MULTISTEP FUSION MSXF
SOURCE SOLUTION (PARENT)
NEIGHBORHOOD OF THE SOURCE
TARGET SOLUTIONTARGET SOLUTION(PARENT)(PARENT)
TARGET NEIGHBORHOOD
TRAJECTORY = GOAL ORIENTED PATH
SUCCESSIVE NEIGHBOURHOODS SEARCHED IN THE STOCHASTIC WAY DEPENDING THE DISCTANCE TO TARGET
DISTANCE TO TARGET
EVOLUTION: LAMARCK/BALDWIN’S VIEW. MEMETIC ALGORITHMS
GOAL OF THE NATURE? optimization, fitness, continuity preservation, follow up changes, transfer knowledge to successors
SUCCESION: genetic material carries data for body building plus acquired knowledge
EVOLUTION: crossing over, mutation, learningSELECTION: soft/hard
• individual, meme, chromosome, trait• population (structure, size, composition, learning)• crossing-over, mutation, learning
• theoretical properties ?
individual=solution=memotype≠fenotype
DIFFERENTIAL EVOLUTION
Differential evolution is a subclass of genetic search methods. Democracy in creating successors with using crossover and mutation in GS has been replaced in DE by directed changes to fathom solution space. DE starts from the random population of individuals (solutions). In each iteration something similar to mutation and crossover is performed, however in completely different way than in GS.
For each solution x from the space, an offspring y is generated as the trial solution being the extension of a selected random solution a and two directional solutions b and c (analogy to parents) selected at random. Generation is based on linear combination with some random parameters.
Separate mechanism prevents generating an offspring by simple copying of the parent. Significant role plays the mutation, which due to specific strategy, is self-adaptive and goal-oriented with respect to the direction, scale and range. If the trial solution is better, it is accepted; otherwise it is released. Iterations are repeated until the fixed a priori number of iterations has been reached, or stagnation has been detected. The method owns some specific tuned parameters: differential weight, crossover probability, … selected experimentally.
]2,0[),( RcbRay iiii
ARTIFICIAL IMMUNE SYSTEM
Antigen (invasive protein) represents new problem to solve or new (or temporary) constraints set for the solution of already solved problem. Variety of possible antigens is huge, frequently infinite. Moreover, sequence of presented antigens is not known a priori.
Antibody (protein blocking antigent, directed against intruder) corresponds to an algorithm which produces a solution to the problem. Variety of antibodies is usually small, however mechamisms exist of their aggregation and recombination in order to produce new antibodies with various properties. Patterns of antibodies are collected in the library, which constitutes memory of the system.
Matching (fitness) is the selection of antibody for the antigen. Matching is ideal, if the antibody allow us to generate solution of the problem which is globally optimal under given constraints. Otherwise, certain defined measure is used to evaluate quality of the maching. Bad maching forces the system to seek for new types of antibodies, usually by using evolution.
antigen = problem or instance
antibody = solution
LIBRARY OF ANTIBODIESfitness
recombination
ANT SEARCH. COOPERATIVE SWARMS
ANT
• seeks for food• leaves pheromone on the trail • moves at random, but prefers pheromone trails• pheromone density decreases in time
control system pheromone
generator
moving drive
Pheromone detectors
PARTICLE SWARM OPTIMIZATION
• swarm is a large set of individuals (particles) moving together• each individual performs the search trajectory in the solution space• trajectories are distributed, correlated and take into account experiences of individuals• location of the individual (solution) is described by the location vector x, changes of
location is described by velocity vector v• velocity equation containts an inertiA term and two directional terms weighted by using
some random parameters• location of the individual depends on: recent (previous) position, experience (best
location up to now), location of the leader of the swarm,• the best up to now solution form the most promising direction of the search
BEE SEARCH
Neighborhood search combined with random search and supported by cooperation (learning).
• bee swarm collects honey in hive• each bee performs the random path (solution) to the search region of nectar • selected elite bees in hive perform „waggle dance” in order to inform other bees about
promising search regions (direction, distance, quality)
flowers & nectar
hive
bee
waggle dance = distribution of knowledge
bee trajectory = solution
visited site = neighborhood
nectar amount = goal function
TABU SEARCH
• human thinking in the process of seeking a solution
• the method „best in local neighborhood”
• repeated from the best recently found• forbidding the return to solutions
already visited to prevent cyclic (wandering around); short term memory
NEIGHBOURHOOD
SUCCESSIVE NEIGHBOURHOODSEXPLORED EXHAUSTIVELY
STARTING SOLUTION
ADAPTIVE MEMORY SEARCH
• gathering data in human brain during the process of seeking a solution
• the method „best” in the current heighbourhood (a few solution relatively close to the current)
• repetition from the best recently found; intensification of the search
• operational (short term) memory: prohibition of coming back to solutions already visited to prevent wandering
• tactic memory: set direction of the search• strategic memory: selection of search regions (basins of
attraction); diversification• recency based, frequency based memory
INTELLIGENT WATER DROPS
Based on the dynamic of the river systems, action and reaction, that happen among water drops in rivers:
• a drop has some (static) parameters, namely velocity, soil;• these parameters may change during the lifetime (e.g. iterative cost)• drops flow from a source to destination• a drop starts with some initial velocity and zero soil• during the flow, drop removes some soil from the environment• speed of the drop incereases non-linearly inversely to the amount of soil; path with less soil is
faster than path with more soil• soil is gathered in the drop and removed from the environment• drop statistically prefers path with lower soil
annealing = slow cooling of ferromagnetic or antyferromagnetic solid in order to eliminate internal stretches
Boltzman (harmonic)
Logarithmic (Hajek lemay)
Geometric
SIMULATED ANNEALING. COOLING SCHEMES
)( kk aT
....,1,0)2(ln
k
kTk
k
Tk1 0
01 111 Tk
T
T
TT
k
kk
• Random starting solution• Sequence of k trial moves in the space• K steps in each fixed temperature• Starting temperature adjusted automatically• Adaptive speed of cooling
SIMULATED ANNEALING. AUTOTUNING
SIMULATED ANNEALING. AUTOTUNING
pT
lnmax
0
)()'(maxmax )('max xKxKxNxYx
kk
kk T
TT
11
kk
3
)1ln(
p 0.9
SIMULATED JUMPING SIMULATED JUMPING
annealing by successive heating and cooling, in order to eliminate internal stretches of the spin-glass solid (mixed ferromagnetic and antyferromagnetic material); the aim is to penetrate high barriers that exist between domains
]...,2,1[],0[)()1( NRR
tTtT iepodgrzewan
],0()()1( iapodgrzewanstudzenia tTtT
DISTANCE MEASURES IN THE SOLUTION SPACE
4
)1( nn
72
)52)(1( nnn
)( 2nO )(nO )log( nnO
)2(nn HH
nHn
)( 3
1
n
nn 2
complexity
variance
mean
receiptnumber of inversionin -1 o
n minus the numberof cycles in -1 o
n minus the lenght of the maximal increasing subsequence in -1 o
measure DA (, ) DS (, ) DI (, )
Move type A S I
DISCRETE OPTIMIZATION. SOLUTION SPACE PROPERTIES
0
20
40
60
80
100
120
140
160
180
0 5 10 15 20 25 30
DIST [%]
RE [%]
-14
-12
-10
-8
-6
-4
-2
0
2
4
-18 -14 -10 -6 -2 2 6 10
x
y
There exists strong correlation between quality of the function value (RE) and distance to the best solution (DIST); this correlation is preserved after transformation of the solution to x/y coordinates
BIG VALLEYstartstart
bestbest
SELECTED INSTANCES. BIG VALLEY
RAN
0
5
10
15
20
25
30
35
40
0 2 4 6 8 10 12
DIST [%]
RE [%]RAN
-14
-12
-10
-8
-6
-4
-2
0
-18 -16 -14 -12 -10 -8 -6 -4 -2
x
y
startstart
bestbest
SELECTED METHODS. RANDOM SEARCH
RANDOM SEARCH TRAJECTORY
Random search offers slow convergence to the good solution because it doesn’t use any information about structure of the solution space
SA
0
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20
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30
35
40
0 2 4 6 8 10 12
DIST [%]
RE [%]SA
-14
-12
-10
-8
-6
-4
-2
0
-18 -16 -14 -12 -10 -8 -6 -4 -2
x
y
startstart
bestbest
Simulated annealing offers moderate speed of convergence to the good solution; it is much more similar to the random search than to goal-oriented search
SELECTED METHODS. SIMULATED ANNEALING
SIMULATED ANNEALING TRAJECTORY
TS
-14
-12
-10
-8
-6
-4
-2
0
-18 -16 -14 -12 -10 -8 -6 -4 -2
x
yTS
0
5
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30
35
40
0 2 4 6 8 10 12
DIST [%]
RE [%]
startstart
bestbestTABU SEARCH TRAJECTORY
Tabu search offers quick convergence to the good solution; this is the fast descent method supported by adaptive memory
SELECTED METHODS. TABU SEARCH
PARALLEL OPTIMIZATION: NEW CLASS OF ALGORITHMS
• Theoretical models of parallel calculation: SISD, SIMD, MISD, MIMD • Theoretical models of memory access: EREW, CREW, CRCW• Parallel calculation environments: hardware, software, GPGPU• Shared memory programming: Pthreads (C), Java threads, Open MP (FORTRAN, C, C++)• Distributed memory programing, message-passing, object-based, Internet computing: PVM, MPI,
Sockets, Java RMI, CORBA, Globus, Condor• Measures of quality of parallel algorithms: runtime, speedup, effciency, cost• Single/multiple searching threads; granularity• Independent/cooperative search threads• Distributed (reliable) calculations in the net
PARALLEL OPTIMIZATION: FESTIVAL OF APPROACHES
• SIMULATED ANNEALING: – Single thread, conventional SA, parallel calculation of the goal function value; fine grain;
theory of convergence– Single thread, pSA, parallel moves, subset of random trial solutions selected in the
neighborhood, parallel evaluation of trial solutions; theory of convergence– Exploration of equilibrium state at fixed temperature in parallel– Multiple independent threads; coarse grain– Multiple cooperative threads; coarse grain
• GENETIC SEARCH:– Single thread, conventional GA, parallel calculation of the goal function value; small grain;
theory of convergence– Single thread, parallel evaluation of population;– Multiple independent threads; coarse grain – Multiple cooperative threads, distributed subpopulations: migration, diffusion, island models– …