META2010 META2010 Application of metaheuristics through MATLAB - PowerPoint PPT Presentation

META2010 META2010 Application of metaheuristics through MATLAB optimization toolboxes for the design of coupled resonator filters José-Ceferino Ortega Domingo Giménez University of Murcia Alejandro Álvarez-Melcón Fernando D. Quesada Polytechnic University of Cartagena

Content Introduction Synthesis of coupled resonator filters MATLAB optimization toolboxes Experimental results Conclusions and future research

Introduction  Design problems in telecommunications •Optimization of design parameters  Design of coupled resonator filters •Used in microwave-based communications •Several phases: •Phase 1: obtain couplings matrix (design technology) •Phase 2: obtain geometry (physical design)  Hybridize local and global search methods  Environment: MATLAB

Content Introduction Synthesis of coupled resonator filters MATLAB optimization toolboxes Experimental results Conclusions and future research

Synthesis of filters (I)  Analysis of the problem of synthesis of coupled resonators filters  Filters based on coupled microwave resonators  Technological design: couplings matrix  Characteristics of the filters:  Transfer function  Topology (Kite, Transversal, 2-Trisection ord. 3)  Number of design parameters (8 or 9)  Range of values (from -5 to 5)

Synthesis of filters (II) 2 Trisection ord. 3, zeros Kite, zeros -3 and 3 -5 and -3 0 0 In s 11 In s 11 -10 In s 21 In s 21 -10 -20 -20 -30 -40 -30 -50 -40 -60 -70 -50 -80 -60 -90 -100 -70 -10 -8 -6 -4 -2 0 2 4 6 8 10 -10 -8 -6 -4 -2 0 2 4 6 8 10

Synthesis of filters (III) Kite, fitness fitness 10 10 -13 Kite, fitness 10 -5 Kite, Kite, fitness 10 -13 -5 Kite, fitness fitness 10 10 -1 Kite, -1

Content Introduction Synthesis of coupled resonator filters MATLAB optimization toolboxes Experimental results Conclusions and future research

MATLAB optimization toolboxes  MATLAB Optimization Toolboxes  Optimization Toolbox  fmincon  Genetic Algorithm and Direct Search Toolbox  Direct search ( patternsearch )  Genetic algorithms ( ga )  Simulated annealing ( simulannealbnd )  and Scatter Search

Content Introduction Synthesis of coupled resonator filters MATLAB optimization toolboxes Experimental results Conclusions and future research

Experimental results: fmincon  fmincon  Part of the MATLAB Optimization Toolbox  Local search  Parameters to study:  LargeScale  Algorithm

Experimental results: fmincon

Experimental results: patternsearch  patternsearch (Direct search)  MATLAB Direct Search and Genetic Algorithm Toolbox  Local search  Parameters to study:  InitialMeshSize  MeshContraction  MeshExpansion  ScaleMesh  PollMethod  CompletePoll  PollingOrder  SearchMethod  CompleteSearch

Experimental results: patternsearch SearchMethod & CompleteSearch PollMethod CompletePoll

Experimental results: genetic algorithm  ga (Genetic algorithms)  MATLAB Direct Search and Genetic Algorithm Toolbox  Global search  Parameters to study:  PopulationSize and Generations  EliteCount and CrossoverFraction  FitnessScalingFcn and SelectionFcn  CrossoverFcn and MutationFcn  CreationFcn and HybridFcn

Experimental results: genetic algorithm standard functions SelectionFnc CrossoverFnc

Experimental results: genetic algorithm personalized functions CreationFnc CrossoverFnc MutationFnc HybridFnc

Experimental results: genetic algorithm personalized functions exec. time – CreationFnc HybridFnc MutationFnc

Experimental results: simulated annealing  simulannealbnd (Simulated annealing)  MATLAB Direct Search and Genetic Algorithm Toolbox  Local search  Parameters to study:  AnnealingFcn  InitialTemperature  ReannealInterval  TemperatureFcn  HybridFcn and HybridInterval

Experimental results: simulated annealing fitness AnnealingFnc TemperatureFnc HybridFnc & HybridInterval

Experimental results: comparison

Content Introduction Synthesis of coupled resonator filters MATLAB optimization toolboxes Experimental results Conclusions and future research

Conclusions  Evaluated the application to the design of coupled resonator filters of available tools in the toolboxes of MATLAB  Local and global search methods hybridation, with Genetic algorithms and Scatter Search  The best: ga (Genetic algorithm) personalized

Future research  Application to the physical design (2nd phase), with more computational cost. The 1st phase simplifies the physical design.  Application of other metaheuristics and implementation in MATLAB.  Study of relation between technological and physical design, to divide the physical design in smaller problems.  Application of parallelism, specially in the 2nd phase: parallel metaheuristics and parallelism in the computation of the fitness function (matricial computation).

Thanks Questions? Questions?