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Derivative-Free Optimization: Biogeography-Based Optimization Dan Simon Cleveland State University 1.

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Presentation on theme: "Derivative-Free Optimization: Biogeography-Based Optimization Dan Simon Cleveland State University 1."— Presentation transcript:

1 Derivative-Free Optimization: Biogeography-Based Optimization Dan Simon Cleveland State University 1

2 2 Outline 1.Biogeography 2.Biogeography-Based Optimization 3.Benchmark Functions and Results 4.Sensor Selection: A Real-World Problem 5.BBO Code Walk-Through

3 3 Biogeography The study of the geographic distribution of biological organisms Mauritius 1600s

4 4 Biogeography Species migrate between “islands” via flotsam, wind, flying, swimming, …

5 5 Biogeography Habitat Suitability Index (HSI): Some islands are more suitable for habitation than others Suitability Index Variables (SIVs): Habitability is related to features such as rainfall, topography, diversity of vegetation, temperature, etc.

6 6 Biogeography As habitat suitability improves: – The species count increases – Emigration increases (more species leave the habitat) – Immigration decreases (fewer species enter the habitat)

7 7 Biogeography-Based Optimization 1.Initialize a set of solutions to a problem 2.Compute “fitness” (HSI) for each solution 3.Compute S,, and  for each solution 4.Modify habitats (migration) based on,  5.Mutatation 6.Typically we implement elitism 7.Go to step 2 for the next iteration if needed

8 Biogeography-Based Optimization = the probability that the immigrating individual’s solution feature is replaced  = the probability that an emigrating individual’s solution feature migrates to the immigrating individual ---- immigrating island (individual) emigrating islands (individuals) 8

9 9 Benchmark Functions 14 standard benchmark functions were used to evaluate BBO relative to other optimizers. Ackley Fletcher-Powell Griewank Penalty Function #1 Penalty Function #2 Quartic Rastrigin Rosenbrock Schwefel 1.2 Schwefel 2.21 Schwefel 2.22 Schwefel 2.26 Sphere Step

10 10 Benchmark Functions Functions can be categorized as Separable or nonseparable – for example, (x+y) vs. xy Regular or irregular – for example, sin x vs. abs(x) Unimodal or multimodal – for example, x 2 vs. cos x

11 11 Benchmark Functions Penalty function #1: nonseparable, regular, unimodal

12 12 Benchmark Functions Step function: separable, irregular, unimodal

13 13 Benchmark Functions Rastrigin: nonseparable, regular, multimodal

14 14 Benchmark Functions Rosenbrock: nonseparable, regular, unimodal

15 15 Benchmark Functions Schwefel 2.22: nonseparable, irregular, unimodal

16 16 Benchmark Functions Schwefel 2.26: separable, irregular, multimodal

17 17 Optimization Algorithms Ant colony optimization (ACO) Biogeography-based optimization (BBO) Differential evolution (DE) Evolutionary strategy (ES) Genetic algorithm (GA) Population-based incremental learning (PBIL) Particle swarm optimization (PSO) Stud genetic algorithm (SGA)

18 18 Average performance of 100 simulations (n = 50) ACOBBODEESGAPBILPSOSGA Ackley Fletcher Griewank Penalty 12.2E71.2E49.7E41.3E62.5E52.8E72.1E6100 Penalty 25.0E E41.1E45.4E56.4E4100 Quartic E Rastrigin Rosenbrock Schwefel Schwefel Schwefel Schwefel Sphere Step

19 19 Aircraft Engine Sensor Selection Health estimation Better maintenance Better control performance

20 20 Aircraft Engine Sensor Selection What sensors should we use? Measure pressures, temperatures, speeds 11 sensors; some can be duplicated Estimate efficiencies and airflow capacities Optimize estimation accuracy and cost Use a Kalman filter for health estimation

21 21 Aircraft Engine Sensor Selection Suppose we want to pick N objects out of K classes while choosing from each class no more than M times. Example: We have red balls, blue balls, and green balls (K=3). We want to pick 4 balls (N=4) with each color chosen no more than twice (M=2). 6 Possibilities: {B, B, G, G}, {R, B, G, G}, {R, B, B, G}, {R, R, G, G}, {R, R, B, G}, {R, R, B, B}

22 22 Aircraft Engine Sensor Selection Pick N objects out of K classes while choosing from each class no more than M times. q(x)= (1 + x + x 2 + … + x M ) K = 1 + q 1 x + q 2 x 2 + … + q N x N + … + x MK Multinomial theorem: The number of unique combinations (order independent) is q N

23 23 Aircraft Engine Sensor Selection Example: Pick 20 objects out of 11 classes while choosing from each class no more than 4 times. q(x) = (1 + x + x 2 + x 3 + x 4 ) 11 = 1 + … + 3,755,070 x 20 + …+ x hours of CPU time for an exhaustive search. We need a quick suboptimal search strategy.

24 24 Aircraft Engine Sensor Selection Average and best performance of 100 Monte Carlo simulations. Computational savings = 99.99% (21 hours  8 seconds). ACOBBODEESGAPBILPSOSGA Mean Best BBO.m


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