Scatter Search: Methodology and...
Transcript of Scatter Search: Methodology and...
Scatter Search:Scatter Search:Methodology and Methodology and
ApplicationsApplicationsManuel LagunaManuel Laguna
University of ColoradoUniversity of Colorado
Rafael Rafael MartíMartíUniversity of ValenciaUniversity of Valencia
22
Based on …Based on …
Scatter Search: Methodology and Scatter Search: Methodology and Implementations in CImplementations in CLaguna, M. and R. Laguna, M. and R. MartíMartíKluwer Academic Publishers, Boston, 2003.Kluwer Academic Publishers, Boston, 2003.
Scatter SearchScatter Search
MethodologyMethodology
44
MetaheuristicMetaheuristic
A metaheuristic refers to a master strategy A metaheuristic refers to a master strategy that guides and modifies other heuristics that guides and modifies other heuristics to produce solutions beyond those that are to produce solutions beyond those that are normally generated in a quest for local normally generated in a quest for local optimality. optimality.
A metaheuristic is a procedure that has A metaheuristic is a procedure that has the ability to escape local optimalitythe ability to escape local optimality
55
Typical Search TrajectoryTypical Search Trajectory
0
10
20
30
40
50
60
70
80
90
100
1 4 7 10 13 16 19 22 25 28 31 34 37 40
Iteration
Obj
ectiv
e Fu
nctio
n
ValueBest Value
66
Metaheuristic ClassificationMetaheuristic Classification
x/y/zx/y/z ClassificationClassificationx = A (adaptive memory) or M (x = A (adaptive memory) or M (memorylessmemoryless))y = N (systematic neighborhood search) or S (random y = N (systematic neighborhood search) or S (random sampling)sampling)z = 1 (one current solution) or P (population of z = 1 (one current solution) or P (population of solutions)solutions)
Some ClassificationsSome ClassificationsTabuTabu search (A/N/1)search (A/N/1)Genetic Algorithms (M/S/P)Genetic Algorithms (M/S/P)Scatter Search (M/N/P)Scatter Search (M/N/P)
77
Scatter SearchScatter Search
P
Diversification GenerationMethod
Repeat until |P| = PSize
Subset GenerationMethod
ImprovementMethod
Solution CombinationMethod
ImprovementMethod
Stop if no morenew solutions
Reference SetUpdate Method
RefSet
88
Repeat until |P| = PSize
Scatter Search with RebuildingScatter Search with RebuildingP
Diversification GenerationMethod
Subset GenerationMethod
ImprovementMethod
Solution CombinationMethod
ImprovementMethod
No more new solutions
Reference SetUpdate Method
RefSet
Diversification GenerationMethod
ImprovementMethod
Stop if MaxIterreached
99
TutorialTutorial
Unconstrained Nonlinear Optimization Unconstrained Nonlinear Optimization ProblemProblem
( ) ( ) ( ) ( )( ) ( )( ) ( )( )
4,,1for 1010toSubject
118.19111.10
1901100Minimize
422
42
2
23
2234
21
2212
K=≤≤−
−−+−+−
+−+−+−+−
ix
xxxx
xxxxxx
i
1010
Diversification Generation Diversification Generation MethodMethod
Subrange 1 Subrange 2 Subrange 3 Subrange 4
-10 -5 0 +5 +10
Probability of selecting a subrange is proportional to a frequency count
1111
Diverse SolutionsDiverse Solutions
x1
1.11-9.587.428.83-6.231.640.2-3.09-6.08-1.97
x2
0.85-6.57-1.71-8.457.483.34-3.646.620.678.13
x3
9.48-8.819.284.526-8.32-5.3-2.33-6.48-5.63
x2
-6.35-2.275.923.187.8-8.66-7.03-3.121.488.02
f(x)835546.21542078.9901878.0775470.7171450.5546349.8114023.87469.1279099.954537.2
Solution123456789
10
1212
Improvement MethodImprovement MethodSolution x1 x2 x3 x2 f(x)
1 2.5 5.4 2.59 5.67 1002.72 -0.52 -0.5 0.35 -0.14 138.53 -2.6 5.9 4.23 10 7653.74 0.49 0.53 2.47 5.89 213.75 -3.04 9.45 1.14 0.41 720.16 -1.4 2.46 0.37 -3.94 1646.77 -0.36 -0.31 0.8 1.14 57.18 -1.63 2.51 0.73 0.56 21.59 -0.8 0.69 -1.16 1.5 11.2
10 -2.47 5.32 -2.92 8.15 1416.7
Nelder and Mead (1965)
1313
Reference Set Update MethodReference Set Update Method(Initial (Initial RefSetRefSet))
Objective functionvalue to measurequality
b1 high-qualitysolutions
Max-min criterion andEuclidean distances tomeasure diversity
b2 diversesolutions
RefSet of size b
1414
Initial Initial RefSetRefSetSolution
number in P x1 x2 x3 x4 f(x) 35 -0.0444 0.0424 1.3577 1.8047 2.1 46 1.133 1.2739 -0.6999 0.5087 3.5 34 -0.0075 0.0438 1.4783 2.2693 3.5 49 1.1803 1.4606 -0.344 0.2669 5.2 38 1.0323 0.9719 -0.8251 0.695 5.3
Solution x1 x2 x3 x4 f(x) 37 -3.4331 10 1.0756 0.3657 1104.1 30 3.8599 10 -4.0468 10 9332.4 45 -4.4942 10 3.0653 10 13706.1 83 -0.2414 -6.5307 -0.9449 -9.4168 17134.8 16 6.1626 10 0.1003 0.1103 78973.2
High-Quality Solutions
Diverse Solutions
1515
Subset Generation MethodSubset Generation Method
All pairs of reference solutions that include All pairs of reference solutions that include at least one new solutionat least one new solution
The method generates (b2The method generates (b2--b)/2 pairs from b)/2 pairs from the initial the initial RefSetRefSet
1616
Combination MethodCombination Method
1 2 3 4 5 6 7 8 9 10 11 12 13
1
2
3
4
5
6
7
8
9
10
x2 = (8,4)
x1 = (5,7)
x3 = x1 - r(x2 - x1)x4 = x1 + r(x2 - x1)x5 = x2 + r(x2 - x1)
x4 = (6.5,5.5)r = 1/2
x5 = (11,1)r = 1
x3 = (9,7)r = 2/3
y
x
1717
Alternative Combination MethodAlternative Combination Method
x3
1 2 3 4 5 6 7 8 9 10 11 12 13
1
2
3
4
5
6
7
8
9
10
x2 = (8,4)
x1 = (5,7)
x3 = x1 - r(x2 - x1)x4 = x1 + r(x2 - x1)x5 = x2 + r(x2 - x1)
y
x
x4
x5
1818
Reference Set Update MethodReference Set Update Method
Updated RefSet
Worst
Best1
b
2...
Quality 1
b
2...
Worst
Best
New trialsolution
RefSet of size b
1919
Static UpdateStatic UpdatePool of new trial solutions
RefSet of size b
Worst
BestQuality 1
b
2...
Updated RefSet = Best b from RefSet ∪ Pool
2020
RefSetRefSet after Updateafter Updatexx11 xx22 xx33 xx44
ff((xx))
1.13831.1383 1.29651.2965 0.83060.8306 0.7150.715 0.140.14
0.70160.7016 0.52970.5297 1.20781.2078 1.46331.4633 0.360.36
0.52690.5269 0.2870.287 1.26451.2645 1.60771.6077 0.590.59
1.19631.1963 1.39681.3968 0.68010.6801 0.4460.446 0.620.62
0.33260.3326 0.10310.1031 1.36321.3632 1.83111.8311 0.990.99
0.33680.3368 0.10990.1099 1.38181.3818 1.93891.9389 1.021.02
0.31270.3127 0.09490.0949 1.35121.3512 1.85891.8589 1.031.03
0.75920.7592 0.5230.523 1.31391.3139 1.71951.7195 1.181.18
0.20040.2004 0.03440.0344 1.40371.4037 1.94381.9438 1.241.24
1.38921.3892 1.93051.9305 0.12520.1252 --0.01520.0152 1.451.45
2121
TutorialTutorial
00--1 Knapsack Problem1 Knapsack Problem
Maximize 10x1 + 14x2 + 9x3 + 8x4 + 7x5 + 5x6 + 9x7 + 3x8
S.t. 7x1 + 12x2 + 8x3 + 9x4 + 8x5 + 6x6 + 11x7 + 5x8 < 100
xi = { 0, 1} for i = 1, …, 8
2222
Additional StrategiesAdditional Strategies
Reference SetReference SetRebuildingRebuildingMultiMulti--tiertier
Subset GenerationSubset GenerationSubsets of size > 2Subsets of size > 2
Combination MethodCombination MethodVariable number of solutionsVariable number of solutions
2323
RebuildingRebuildingRefSet Rebuilt RefSet
b1
b2
DiversificationGeneration Method
Reference SetUpdate Method
2424
22--Tier Tier RefSetRefSet
RefSet
b1
b2
Solution CombinationMethod
ImprovementMethod
Try here first
If it fails, thentry here
2525
33--Tier Tier RefSetRefSet
RefSet
b1
b2
Solution CombinationMethod
ImprovementMethod
Try here first
If it fails, thentry here
b3
Try departingsolution here
2626
Subset GenerationSubset GenerationSubset Type 1:Subset Type 1: all 2all 2--element subsets.element subsets.Subset Type 2:Subset Type 2: 33--element subsets derived element subsets derived from the 2from the 2--element subsets by augmenting element subsets by augmenting each 2each 2--element subset to include the best element subset to include the best solution not in this subset.solution not in this subset.Subset Type 3:Subset Type 3: 44--element subsets derived element subsets derived from the 3from the 3--element subsets by augmenting element subsets by augmenting each 3each 3--element subset to include the best element subset to include the best solutions not in this subset.solutions not in this subset.Subset Type 4:Subset Type 4: the subsets consisting of the the subsets consisting of the best best ii elements, for elements, for ii = 5 to b. = 5 to b.
2727
Subsets of Size > 2Subsets of Size > 2
Type 1 Type 2Type 3 Type 4
LOLIB
Random0%10%20%30%40%50%60%70%80%90%
2828
Variable Number of SolutionsVariable Number of SolutionsQuality
Worst
Best1
b
2... Generate 5 solutions
Generate 3 solutions
Generate 1 solution
RefSet of size b
2929
Hybrid ApproachesHybrid Approaches
Use of MemoryUse of MemoryTabuTabu Search mechanisms for intensification Search mechanisms for intensification and diversificationand diversification
GRASP ConstructionsGRASP ConstructionsCombination MethodsCombination Methods
GA OperatorsGA OperatorsPath Path RelinkingRelinking
3030
MultiobjectiveMultiobjective Scatter SearchScatter Search
This is a fruitful research areaThis is a fruitful research area
Many Many multiobjectivemultiobjective evolutionary evolutionary approaches exist (approaches exist (CoelloCoello, et al. 2002), et al. 2002)
SS can use similar techniques developed SS can use similar techniques developed for MOEA (for MOEA (multiobjectivemultiobjective evolutionary evolutionary approchesapproches))
3131
MultiobjectiveMultiobjective EA TechniquesEA Techniques
Independent SamplingIndependent SamplingSearch on Search on ff((xx)) = = ∑∑wwii ffii((xx))Change weights and rerunChange weights and rerun
Criterion SelectionCriterion SelectionDivide reference set into Divide reference set into kk subsetssubsetsAdmission to Admission to iithth subset is according to subset is according to ffii((xx))
Scatter SearchScatter SearchApplications and ImplementationsApplications and Implementations
3333
ScatterScatter Search Search ElementsElements
Diversification Generator MethodDiversification Generator MethodImprovement MethodImprovement MethodReference Set Reference Set
InitializationInitializationUpdateUpdateRebuildRebuild
Subset Generation Method Subset Generation Method Solution Combination MethodSolution Combination Method
Problem Independent
3434
C C CodeCode ObjectiveObjective
Illustrate the methods and strategiesIllustrate the methods and strategiesUseUse itit in different applicationsin different applicationsExtend the previous version:Extend the previous version:
http://http://www.uv.es/~rmarti/sscode.htmlwww.uv.es/~rmarti/sscode.htmlModularityModularityVersatilityVersatility
Non expertsNon experts (use as (use as itit isis withwith smallsmall changeschanges))Experts (can Experts (can easilyeasily introduce introduce importantimportant modificationsmodifications))
AnsiAnsi C (different platforms and systems)C (different platforms and systems)
3535
Basic Basic DesignDesign
P
Diversification GenerationMethod
Repeat until |P| = PSize
Subset GenerationMethod
ImprovementMethod
Solution CombinationMethod
ImprovementMethod
Stop if no morenew solutions
Reference SetUpdate Method
RefSet
3636
RefSet Data RefSet Data StructureStructuretypedeftypedef structstruct REFSETREFSET
{{ intint b;b; /* Size/* Size */*/
doubledouble **sol;**sol; /* Solutions/* Solutions */*/
doubledouble **ObjValObjVal;; //* Objective value of solutions* Objective value of solutions */*/
intint *order;*order; /* Order of solutions *//* Order of solutions */
intint **iteriter;; /* Entering iteration *//* Entering iteration */
intint NewSolutionsNewSolutions;; /* =1 if new element has been added *//* =1 if new element has been added */
} } REFSETREFSET;;
bsol
ObjValorderiter
NewSolutions
RefSet nvar
3737
MainMain FunctionFunctionintint main(void)main(void){{
SS *SS *pbpb;; /* Pointer to problem data/* Pointer to problem data **//intint nvarnvar = 10;= 10; /* Number of variables/* Number of variables */*/intint b = 10;b = 10; /* Size of reference set/* Size of reference set **//intint PSizePSize = 100;= 100; /* Size of P/* Size of P */*/
pbpb = = SSProblem_DefinitionSSProblem_Definition(nvar,b,PSize(nvar,b,PSize););
/* Insert here the Scatter Search code *//* Insert here the Scatter Search code */
SSFree_DataStructuresSSFree_DataStructures(pb(pb););return 0;return 0;
}}
3838
Basic Basic DesignDesign
pbpb = = SSProblem_Definition(nvar,b,PSizeSSProblem_Definition(nvar,b,PSize););
SSCreate_PSSCreate_P(pb(pb););
SSCreate_RefSetSSCreate_RefSet(pb(pb););
WhileWhile((pbpb-->>rsrs-->>NewSolutionsNewSolutions))
SSUpdate_RefSetSSUpdate_RefSet(pb(pb););
SSBestSol(pb,sol,&valueSSBestSol(pb,sol,&value););
SSFree_DataStructures(pbSSFree_DataStructures(pb););
3939
AdvancedAdvanced DesignDesignpbpb = = SSProblem_Definition(nvar,b,PSizeSSProblem_Definition(nvar,b,PSize););SSCreate_PSSCreate_P(pb(pb););SSCreate_RefSetSSCreate_RefSet(pb(pb););
for(Iterfor(Iter=1; =1; IterIter<<100100; ; IterIter++)++){{
if(pbif(pb-->>rsrs-->>NewSolutionsNewSolutions))SSUpdate_RefSetSSUpdate_RefSet(pb(pb););
elseelseSSRebuild_RefSetSSRebuild_RefSet(pb(pb););
}}SSBestSol(pb,sol,&valueSSBestSol(pb,sol,&value););SSFree_DataStructures(pbSSFree_DataStructures(pb););
4040
SSCreate_PSSCreate_Pwhile(currentPSizewhile(currentPSize <=<= pbpb-->p>p-->>PSizePSize)){{SSGenerate_Sol(pb,solSSGenerate_Sol(pb,sol););obj_valobj_val = sol_value(sol);= sol_value(sol);SSImprove_solutionSSImprove_solution(pb,sol,&obj_val(pb,sol,&obj_val););
/* Check whether sol is a new solution *//* Check whether sol is a new solution */j=1;equal=0;j=1;equal=0;while(j<while(j<currentPSizecurrentPSize && !equal)&& !equal)
equal=equal=SSEqualSolSSEqualSol(sol,pb(sol,pb-->p>p-->>sol[j++],pbsol[j++],pb-->>nvarnvar););
/* Add improved solution to P *//* Add improved solution to P */if(!equal){if(!equal){
for(j=1;j<=for(j=1;j<=pbpb-->>nvar;jnvar;j++)++)pbpb-->p>p-->>sol[currentPSize][jsol[currentPSize][j]=sol[j];]=sol[j];
pbpb-->p>p-->>ObjVal[currentPSizeObjVal[currentPSize++] = ++] = obj_valobj_val;;}}
}}
4141
SSCreate_RefSetSSCreate_RefSet/* Order solutions in P *//* Order solutions in P */p_order = p_order = SSOrderSSOrder(pb(pb-->p>p-->>ObjValObjVal, , pbpb-->p>p-->>PSizePSize, , pbpb-->Opt);>Opt);b1 = b1 = pbpb-->>rsrs-->b / 2;>b / 2;
/* Add the /* Add the firstfirst ((bestbest) b1) b1 solutions in P to RefSet */solutions in P to RefSet */
/* Compute minimum distances from P to RefSet *//* Compute minimum distances from P to RefSet */for(i=1;i<=for(i=1;i<=pbpb-->p>p-->>PSize;iPSize;i++)++)
min_dist[i]= min_dist[i]= SSDist_RefSetSSDist_RefSet(pb,b1,pb(pb,b1,pb-->p>p-->sol[i]);>sol[i]);
/* Add b/* Add b--b1 diverse solutions to RefSet */b1 diverse solutions to RefSet */for(i=b1+1;i<=for(i=b1+1;i<=pbpb-->>rsrs-->b;i++)>b;i++) {{
a=a=SSMax_dist_index(pb,min_distSSMax_dist_index(pb,min_dist););/* Copy sol a from P to /* Copy sol a from P to RefsetRefset */*/SSUpdate_distancesSSUpdate_distances(pb,min_dist,i(pb,min_dist,i););
}}/* Compute the order in RefSet: /* Compute the order in RefSet: orderorder */*/pbpb-->>rsrs-->>NewSolutionsNewSolutions == 1;1; pbpb-->>CurrentIterCurrentIter = 1;= 1;
4242
SSUpdate_RefSetSSUpdate_RefSet
pbpb-->>rsrs-->>NewSolutionsNewSolutions=0;=0;
SSCombine_RefSet(pbSSCombine_RefSet(pb););
pbpb-->>CurrentIterCurrentIter++;++;
for(a=1;a<=for(a=1;a<=pbpb-->pool_size;a++)>pool_size;a++)
{{
value=value=sol_value(pbsol_value(pb-->pool[a]);>pool[a]);
SSImprove_solution(pb,pbSSImprove_solution(pb,pb-->pool[a],&value);>pool[a],&value);
SSTryAdd_RefSet(pb,pbSSTryAdd_RefSet(pb,pb-->pool[a],value);>pool[a],value);
}}
pbpb-->>pool_sizepool_size=0;=0;
4343
AdvancedAdvanced DesignsDesigns
ReferenceReference SetSet UpdateUpdateDynamicDynamic / / StaticStatic2 2 TierTier / 3 / 3 TierTier
Subset Subset GenerationGenerationUse of Use of MemoryMemory
ExplicitExplicit MemoryMemoryAttributiveAttributive MemoryMemory
PathPath RelinkingRelinking
AnAn ExampleExampleTheThe Linear Ordering ProblemLinear Ordering Problem
Given a matrix of weights E = {eij}mxm, the LOP consists of finding a permutation p of the columns (and rows) in order to maximize the sum of the weights in the upper triangle
ApplicationsTriangulation for Input-Output Economic Tables.Aggregation of individual preferencesClassifications in Sports
C p eE p pj i
m
i
m
i j( ) =
= +=
−
∑∑11
1
Maximize
An An InstanceInstance
1234
1 2 3 4
3412
3 4 1 20 9 8 32 0 11 45 3 0 122 3 4 0
⎡
⎣
⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥
0 12 5 34 0 2 68 3 0 911 4 2 0
⎡
⎣
⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥
p=(1,2,3,4)
cE(p)=12+5+3+2+6+9=37
p*=(3,4,1,2)
cE(p*)=9+8+3+11+4+12=47
4646
SS SS forfor thethe LOPLOP
Complete the “generic” codeComplete the “generic” code
Design specific methods for the “problemDesign specific methods for the “problem--dependent” elementsdependent” elements
Diversification Generator MethodDiversification Generator MethodImprovement MethodImprovement MethodCombination MethodCombination Method
4747
Diversification GeneratorDiversification GeneratorUse of Use of problemproblem structurestructure toto createcreate methodsmethods in in orderorder totoachieveachieve a a goodgood balance balance betweenbetween qualityquality andand diversitydiversity..QualityQuality
DeterministicDeterministic constructiveconstructive methodmethodDiversityDiversity
RandomRandom GeneratorGeneratorSystematicSystematic GeneratorsGenerators (Glover, 1998)(Glover, 1998)
GRASP constructions. GRASP constructions. The method randomly selects from a short list of the most The method randomly selects from a short list of the most attractive sectors. attractive sectors.
Use of Use of MemoryMemoryMModifyingodifying a measure of attractiveness proposed by Becker with a measure of attractiveness proposed by Becker with a frequencya frequency--based memory measure that discourages sectors based memory measure that discourages sectors from occupying positions that they have frequently occupiedfrom occupying positions that they have frequently occupied
4848
Diversity vs. QualityDiversity vs. Quality
Compare several diversification generatorsCreate a set of 100 solutions with each one
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
DG05 DG04 DG02 DG01 DG09 DG08 DG06 DG03 DG07 DG10
Procedure
∆C
∆d
∆C+∆d
∆
∆d = Standardized Diversity∆C = Standardized Quality
4949
ImprovementImprovement MethodMethod
INSERT_MOVE (INSERT_MOVE (pjpj, i) consist of deleting , i) consist of deleting pjpj from from its current position j to be inserted in position i its current position j to be inserted in position i Apply a first strategyApply a first strategy
scans the list of sectors in search for the first sector scans the list of sectors in search for the first sector whose movement results in an improvement whose movement results in an improvement
E p( ) = − −−
−
⎛
⎝
⎜⎜⎜⎜⎜⎜⎜⎜⎜
⎞
⎠
⎟⎟⎟⎟⎟⎟⎟⎟⎟
0 12 5 3 1 8 36 0 3 6 4 4 28 5 0 5 7 0 32 7 2 0 3 6 08 0 3 1 0 4 19 1 6 2 13 0 42 9 4 5 8 1 0
MoveValue = CE(p’) - CE(p)
CE (p’) = 78 + (1 - 4) + (6 - 0) + (2 - 6) + (13 - 4) = 78 + 8 = 86
5050
Solution Combination MethodSolution Combination MethodThe method scans (from left to right) each reference The method scans (from left to right) each reference permutation.permutation.
Each reference permutation votes for its first element that is sEach reference permutation votes for its first element that is still till not included in the combined permutation (“incipient element”). not included in the combined permutation (“incipient element”). The voting determines the next element to enter the first still The voting determines the next element to enter the first still unassigned position of the combined permutation.unassigned position of the combined permutation.The vote of a given reference solution is weighted according to The vote of a given reference solution is weighted according to the incipient element’s position.the incipient element’s position.
Incipient element(3,1,4,2,5) votes for 4 Solution under construction:(1,4,3,5,2) votes for 4 (3,1,2,4,_ )(2,1,3,5,4) votes for 5
5151
ExperimentsExperiments withwith LOLIB LOLIB
49 Input-Output Economic Tables
GDGD CKCK CK10CK10 TSTS SSSS
OOptimaptimadevdeviationiation
0.15%0.15% 0.15%0.15% 0.02%0.02% 0.04%0.04% 0.01%0.01%
NNumber of umber of optimaoptima
1111 1111 2727 3333 4242
RRun time un time (sec(secondsonds))
0.010.01 0.100.10 1.061.06 0.490.49 2.352.35
5252
Another ExampleAnother ExampleA commercial SS implementationA commercial SS implementation
OptQuest Callable Library (by OptTek)As other context-independent methodsseparates the method and the evaluation.
OptimizationProcedure
Input
Output
SystemEvaluator
5353
OptQuestOptQuest basedbased ApplicationsApplications
Solution Generator
Solution EvaluatorUser-writtenApplication
OptQuestCallable Library
SystemEvaluator
5454
FeasibilityFeasibility andand EvaluationEvaluation
User Implementation
ConstraintMapping
Complex SystemEvaluator
PenaltyFunction
x x*F(x*)
G(x*)P(x*)
Returns toOptQuest
The OptQuest enginegenerates a new solution
5555
ComparisonComparison withwith GenocopGenocopTestTest onon 28 28 hardhard nonlinearnonlinear instancesinstances
1.0E+03
1.0E+04
1.0E+05
1.0E+06
1.0E+07
1.0E+08
1.0E+09
1.0E+10
1.0E+11
1.0E+12
1.0E+13
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
Evaluations
Ave
rage
obj
ectiv
e fu
nctio
n va
lue
(Log
arith
mic
scal
e)
GenocopOCL
1.0E+03
1.0E+04
1.0E+05
1.0E+06
1.0E+07
1.0E+08
1.0E+09
1.0E+10
1.0E+11
1.0E+12
1.0E+13
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
Evaluations
Ave
rage
obj
ectiv
e fu
nctio
n va
lue
(Log
arith
mic
scal
e)
GenocopOCL
5656
ConclusionsConclusions
The development of metaheuristics usually The development of metaheuristics usually entails a fair amount of experimentation entails a fair amount of experimentation (“skill comes from practice”).(“skill comes from practice”).Code objectives:Code objectives:
Quick StartQuick StartBenchmark Benchmark Advanced DesignsAdvanced Designs
Scatter Search provides a flexible Scatter Search provides a flexible “framework” to develop solving methods“framework” to develop solving methods..
5757
CallCall forfor PapersPapers
European Journal of Operational ResearchEuropean Journal of Operational Research
Feature Issue on Feature Issue on SCATTER SEARCH METHODSSCATTER SEARCH METHODS
FOR OPTIMIZATIONFOR OPTIMIZATION
Deadline for submissions: June 30, 2003Deadline for submissions: June 30, 2003http://http://www.uv.es/~rmarti/ejor.htmlwww.uv.es/~rmarti/ejor.html
5858
QuestionsQuestions