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FSS (F ISH S CHOOL S EARCH ) I NTELL. A LGORITHMS FOR O PTIMIZATION Prof. Carmelo Bastos Filho, PhD Prof. Fernando Buarque de Lima Neto, DIC PhD Computational.

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Presentation on theme: "FSS (F ISH S CHOOL S EARCH ) I NTELL. A LGORITHMS FOR O PTIMIZATION Prof. Carmelo Bastos Filho, PhD Prof. Fernando Buarque de Lima Neto, DIC PhD Computational."— Presentation transcript:

1 FSS (F ISH S CHOOL S EARCH ) I NTELL. A LGORITHMS FOR O PTIMIZATION Prof. Carmelo Bastos Filho, PhD Prof. Fernando Buarque de Lima Neto, DIC PhD Computational Intelligence Research Group (CIRG) Pernambuco Polytechnic School of Engineering (POLI) University of Pernambuco (UPE) – Recife, Brazil. X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence S HORT -C OURSE 5

2 CIRG@UPE Recife 2 Computational intelligence research group 5 Professors 14 M.Sc. students 17 B.Sc. students

3 Agenda Part 1: Fundamentals -Swarm Intelligence -FSS: motivation and inspirations -FSS: vanilla operators Part 2: Developments -Multimodal Optimization -FSS: versions d and p -FSS: applications and results -FSS: the way ahead X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 3 Prof. Carmelo Bastos Filho, PhD carmelofilho@poli.br Prof. Fernando Buarque de Lima Neto, DIC PhD fbln@ecomp.poli.br

4 P ART - 1: FUNDAMENTALS X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 4

5 P ART - 1: FUNDAMENTALS SWARM INTELLIGENCE X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 5

6 Swarm Intelligence Simple entities Emergence of intelligence to solve complex problems Examples – PSO – ACO – ABC – BFA… X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 6

7 Particle Swarm Optimization (PSO) Proposed in 1995 by Kennedy and Eberhart Attributes: – Current position in the search space x(t) – Current velocity v(t) – Best position found by the particle during the search (pbest) – Best position found by the particles neighbors during the search (gbest) Velocity update equation V(t+1) = w.v(t) + c 1.r 1.[p best -x(t)] + c 1.r 1.[g best -x(t)] Position update equation x(t+1) = x(t) + v(t) Social term Cognitive term X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 7

8 PSO Fast convergence, but quickly looses diversity X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 8

9 P ART - 1: FUNDAMENTALS FSS: MOTIVATION AND INSPIRATIONS X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 9

10 In real-world problems, search spaces can be huge Computationally intensive techniques may not scale-up properly or tackle a sizeable number of constraints Gregarious fish are much better equipped to survive (finding food in massive volumes and defending themselves from various threats) X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 10

11 X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 11

12 FSS was invented to be a new* family of algorithms (within Swarm intelligence approach, thus particle based) suited for optimization in high-dimensional search spaces (and now able to split the school). FSS was devised so that all fish perform local search while the fish school aggregates social information * Invented by Bastos Filho & Lima Neto in 2008 X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 12

13 Recife – 2007... X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 13

14 First publication by Bastos Filho & Lima Neto in 2008 http://www.fbln.pro.br/FSS/people.htm X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 14

15 ICSI 2011 – Chongqing, China X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 15

16 What is fss about ? TERMINOLOGY & RATIONALE X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 16

17 Fishes are entities of the swarm (i.e. school) Aquarium is the search space that can be of high dimensionality Position of each fish within the aquarium is one candidate solution (i.e. a set of values for the parameter vector) of the optimization process Weight of each fish indicates its individual success (i.e. fitness) in finding good solution Radius of the fish school indicates the collective success in finding good solution X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 17

18 (1)'Swimming' actually is a means of: performing a local search + Storing information of success (Both of Individuals and Collective nature) (2)Success of the search is given by: fish weights (large is better) + school radius (small is better) + School barycenter (closer to optima is better) (3)Non-monotonicity is achieved, e.g.: (i) By random hesitation before swim + (ii) By expansion/shrinking the school radius + (iii) By variations on swimming components X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 18

19 What is FSS about ? FUNCTIONING & OPERATORS X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 19

20 Local computations (i.e. swimming) is composed of distinct components (this results in smart swimming) Fish think they are alone in the aquarium, but whenever they need there is an intuition available (this results in very low communication cost) Barycenter of the school moves smoothly towards optimal solution (this results in always better solutions along processing) School radius is based on barycenter gradient (this results in the interesting ability to self control exploration and exploitation modes of operation) X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 20

21 P ART - 1: FUNDAMENTALS FSS: VANILLA OPERATORS X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 21

22 OPERATORS: 1. Feeding 2. Swimming: Individual movement Collective-instinctive Collective-volitive STOP-CONDITIONS: 1. limit of cycles; 2. time limit; 3. maximum school weight 4. minimum school radius (*) Learning Local search Social glue Global search Problem dependent Problem independent (#2) (#1) (#3) (#4) X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 22

23 FSS Operators: – Individual movement – Feeding – Collective-instinctive movement – Collective-volitive movement X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 23

24 Fish Swimming - Individual X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 24

25 FSS Operators: – Individual movement – Feeding – Collective-instinctive movement – Collective-volitive movement X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 25

26 FSS Operators: – Individual movement – Feeding – Collective-instinctive movement – Collective-volitive movement X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 26

27 Fish Swimming – Collective instinctive X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 27

28 FSS Operators: – Individual movement – Feeding – Collective-instinctive movement – Collective-volitive movement This operator performs contraction or expansion depending on the school of fish success or failure - Barycenter calculation: - Volitive movement Expansion (+) Contraction (-) X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 28

29 Fish Swimming – Collective vollitive X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 29

30 Single-objective optimization (SOO) Static Environment Examples of FSS at work Sphere Function (3D view) X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 30

31 FSS Is capable to auto-regulate the granularity of search, but has slow convergence X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 31

32 Single-objective optimization (SOO) – Static Environment Examples of FSS at work X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 32

33 P ART - 2: DEVELOPMENTS X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 33

34 P ART - 2: DEVELOPMENTS M ULTIMODAL O PTIMIZATION X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 34

35 The world is not flat... Pico da Neblina Brazilian highest summit @ 3,040m A-S-L In NW Brazilian Amazon Forest, close to the border with Venezuela X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 35

36 Sometimes it seems to be flat... Monte Roraima Impressive 30 Km 2 summit @ 2,772m A-S-L In Guyana, close to the triple border of Brazil and Venezuela X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 36

37 BUT it is not flat at all! Mont Serrat Impressive set of peaks @ 1,236m A-S-L In Catalonia, 50Km far from Barcelona in NE of Spain X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 37

38 How FSS could be improve from tackling only one global solution to: (i) many/all global solutions or even (ii) many/local solutions of a given MMOP? (i) Mount-Roraima MMOPs (ii) Mountserrat MMOPs (Infinite number of solutions) (Finite number of solutions) Evokes impossibility Evoke unfeasibility X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 38

39 P ART - 2: DEVELOPMENTS FSS: VERSION D X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 39

40 Fish schools do split in Nature! X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 40

41 Large fish schools are good (for protection and for finding food) but are also bad (because of increased competition) The splitting of the school could be governed by the density of fish within a sub-regions of the school Densely populated regions would provide food to closer fish much more efficiently than to fish that are apart (a sort of collaborative behavior) X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 41

42 The new flavor of Fish School Search, dFSS, was devised to be a metaheuristic that is non-exhaustive, fast, scalable, inexpensive and is able to elegantly tackle multimodal optimization problems. X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 42

43 Fishes are entities of the swarm (i.e. school) Aquarium is the search space that can be of high dimensionality Position of each fish within the aquarium is one candidate solution (i.e. a set of values for the parameter vector) of the optimization process Weight of each fish indicates its individual success (i.e. fitness) in finding good solution Radius of the fish school indicates the collective success in finding good solution Same as FSS X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 43

44 (1)'Swimming' actually is a means of: performing a local search + Storing information of success (Both of Individuals and Collective nature) (2)Success of the search is given by: fish weights (large is better) + school radius (small is better) + School barycenter (closer to optima is better) (3)Non-monotonicity is achieved, e.g.: (i) By random hesitation before swim + (ii) By expansion/shrinking the school radius + (iii) By variations on swimming components Almost the same as FSS X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 44

45 Every fish share their food with all others Sharing of food depends on (i) distance and (ii) density Distance and density act as segregators (among sub- populations of the school) Fish now has a memory of collaborations with their pals Swimming in dFSS is weighted also by this memory X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 45

46 OPERATORS: 1. Feeding 2. Swimming: Individual movement Collective-instinctive Collective-volitive STOP-CONDITIONS: 1. limit of cycles; 2. time limit; 3. maximum school weight 4. minimum school radius (*) Learning Local search Social glue Global search Problem dependent Problem independent (#2) (#1) (#3) (#4) Almost the same as FSS (two more: memory and partition) X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 46

47 Individual movement FSS dFSS X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 47

48 Feeding FSS dFSS X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 48

49 MEMORY (*NEW*) X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 49

50 Collective-instinctive FSS dFSS X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 50

51 Collective-VOLITIVE FSS dFSS X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 51

52 PARTITION (*NEW*) while There is fish in the main school do Choose a fish i randomly in the main school Create a new subgroup Si Put fish i in subgroup Si Remove fish i from the main school Find other fish j in the main school that satisfies (#) while there exists fish j in the main school do Put fish j in subgroup Si Remove fish j from the main school Set i = j Find other fish j in the main school that satisfies (#) end while X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 52

53 Multi-modal optimization (MMOP) Equal-Peaks A Function Examples of dFSS at work X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 53

54 Multi-modal optimization (MMOP) F.O.M. Examples of dFSS at work X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 54

55 Comparing FSS and dFSS (Uni- and Multimodal abilities) Examples of dFSS at work X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 55

56 Multi-modal optimization (MMOP) 3D-Function: Plateus Examples of dFSS at work X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 56

57 Multi-modal optimization (MMOP) 3D-Function: Staircase Examples of dFSS at work X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 57

58 Multi-modal optimization (MMOP) 3D-Functions: Circles Examples of dFSS at work X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 58

59 Benchmark functions used for comparison Examples of dFSS at work X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 59

60 Comparing dFSS to GSO and NichePSO (Check ICSI2011 Paper – MADEIRO et al.) Metric: average on 30 trial only when algorithms were able to capture above 95% of existing optimal solutions of the MOOP Examples of dFSS at work X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 60

61 P ART - 2: DEVELOPMENTS FSS: VERSIONS P X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 61

62 Fish (in schools) do process stand alone (sometimes)! X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 62

63 Why not using graphic processors to boost FSS computation? CUDA® Platform (NVIDIA) is easy to be used for that! X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 63 -GeForce GTX 280 (GPU compatible with NVIDIA CUDA ®) -1296MHz (240 Cores) -1Gb Memory -CUDA: 3.2 -OpenGL: 2.1 -Operating System: Ubuntu 10.04

64 The new flavor of Fish School Search, pFSS, was devised to be a metaheuristic that is non-exhaustive, fast, scalable, inexpensive and in a parallel manner. X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 64

65 Fishes are entities of the swarm (i.e. school) Aquarium is the search space that can be of high dimensionality Position of each fish within the aquarium is one candidate solution (i.e. a set of values for the parameter vector) of the optimization process Weight of each fish indicates its individual success (i.e. fitness) in finding good solution Radius of the fish school indicates the collective success in finding good solution Same as FSS X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 65

66 X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 66 Synchronous Asynchronous

67 X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 67 Simulation details: -30 fish -30 dimensions -50 executions (average) -10.000 iteracion Rosenbrock Rastrigin Griewank

68 X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 68

69 X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 69 CIRGs newest PSC (with 1792 cores)

70 P ART - 2: DEVELOPMENTS FSS: THE WAY AHEAD X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 70

71 FSS: The way ahead Hybridization with other Swarm Techniques* Parallelization of current implementations** New operators** Use in Dynamic Problems* Speciation** New real-world complex applications*** moFSS (Multi-Objetive)*** … Legend: * Results already published ** Research already initiated *** Some possibilities already assessed X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 71

72 FSS: The way ahead X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 72 FSS is fast (and outperforms many other Swarm Intelligent algorithms) FSS is computationally inexpensive (almost no communication costs) FSS controls its own exploration/exploitation mode along search FSS is flexible for new extensions and modifications dFSS deals with multimodal problems elegantly New investigations indicate that FSS is easy to parallelize and can tackle dynamic problems

73 X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 73 ICSI 2012 – Shenzhen, China *NEW

74 O FFICIAL WEB - SITE X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 74

75 M AIN P AGE (W EB ) X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 75 HTTP :// WWW. FBLN. PRO. BR /FSS/ INDEX. HTM

76 L INKS P AGE (W EB ) X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 76 HTTP :// WWW. FBLN. PRO. BR /FSS/ LINKS. HTM

77 V ERSIONS P AGE (W EB ) X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 77 HTTP :// WWW. FBLN. PRO. BR /FSS/ VERSIONS. HTM

78 REFERENCES X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 78

79 References MADEIRO, Salomão S.; BASTOS-FILHO, Carmelo J. A.; LIMA NETO, Fernando B. de. "Multimodal Optimization based on Fish School Behavior" to appear in Swarm Intelligence Journal, 2010, Springer. [TO APPEAR] CAVALCANTI JÚNIOR, G. M. ; Bastos-Filho, C. J. A. ; LIMA NETO, F. B. ; CASTRO, R.. A Hybrid Algorithm based on Fish School Search and Particle Swarm Optimization for Dynamic Problems. In ICSI2011: Second International Conference on Swarm Intelligence. Springer - Lecture Notes in Computer Science, v. 6729, p. 543-552, 2011. MADEIRO, S. S. ; LIMA NETO, F. B. ; Bastos-Filho, C. J. A. ; FIGUEIREDO, E. M. N.. Density as the Segregation Mechanism in Fish School Search for Multimodal Optimization Problems. In ICSI2011: Second International Conference on Swarm Intelligence. Springer - Lecture Notes in Computer Science, v. 6729, p. 563-572, 2011. BASTOS-FILHO, Carmelo J. A.; LIMA NETO, Fernando B. de; SOUSA, Maria F. C.; PONTES, Murilo R.; MADEIRO, Salomão S. "On the Influence of the Swimming Operators in the Fish School Search Algorithm". In: IEEE International Conference on Systems, Man, and Cybernetics - SMC2009, 2009, San Antonio, USA. BASTOS FILHO, Carmelo J. A. ; LIMA NETO, Fernando B. de; LINS, Anthony J. C. C.; NASCIMENTO, Antônio I. S.; LIMA, Marília P. "A Novel Search Algorithm based on Fish School Behavior". In: 2008 IEEE International Conference on Systems, Man, and Cybernetics - SMC 2008, 2008, Cingapura. BASTOS-FILHO, Carmelo J. A.; LIMA NETO, Fernando B. de; LINS, Anthony J. C. C.; NASCIMENTO, Antônio I. S.; LIMA, Marília P. "Fish School Search: an overview". In: CHIONG, Raymond (Ed.). Nature-Inspired Algorithms for Optimisation. Series: Studies in Computational Intelligence, Vol. 193.. pp. 261-277. Berlin: Springer- Verlag, 2009. {ISBN: 978-3-642-00266-3} X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence FSS – Intelligent Algorithms for Optimization Profs. Carmelo Bastos Fo. e Fernando Buarque (CIRG/UPE) Fortaleza-CE, 08/11/2011 Brazil Slide 79

80 Prof. Carmelo Bastos Filho, PhD carmelofilho@poli.br Prof. Fernando Buarque de Lima Neto, DIC PhD fbln@ecomp.poli.br Computational Intelligence Research Group (CIRG) Pernambuco Polytechnic School of Engineering (POLI) University of Pernambuco (UPE) – Recife, Brazil. X Brazilian Congress on Computational Intelligence XI School on Computational Intelligence V ALEU MACHO ! (T HAT IS, THANK YOU IN THE LOCAL DIALECT ) Follow the fish...


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