Bacterial Foraging Optimization (BFO)

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Presentation transcript:

Bacterial Foraging Optimization (BFO) Momčilo Vasilijević Vm153390m@student.etf.rs

Optimization problems Selection of a best element (with regard to some criteria) from some set of available alternatives Maximizing or minimizing a real function Many methods for solving Gradient descent RMSPROP Hessian-free optimization Evolutionary algorithms

Biological inspiration Foraging behavior of E. coli Swimming up a nutrient gradient Swimming in groups Concentric patterns of swarms with high bacterial density

Bacterial foraging optimization algorithm Chemotaxis Simulates the movement of an E.coli cell Swimming and tumbling via flagella Swarming Group of E.coli cells arrange themselves in a traveling ring Reproduction Elimination and dispersal

Chemotaxis Motion patterns in the presence of chemical attractants and repellents Modeling movement of single bacteria Choose random direction Δ t Move from current position 𝜃 𝑡 to next position 𝜃 𝑡+1 if next position is better, w.r.t cost function 𝐽 𝑐𝑜𝑠𝑡 (Θ,𝑡) 𝜃 𝑡+1 =𝜃 𝑡 +𝐶Δ(𝑡)

Swarming Simulating spatio-temporal patterns (swarms) Arranging bacteria in rings Cell-to-cell cost 𝐽 𝑐𝑐 = 𝑖=1 𝑆 − 𝑑 𝑎𝑡𝑡 𝑒 − 𝑤 𝑎𝑡𝑡 𝜃− 𝜃 𝑗 2 + 𝑖=1 𝑆 − 𝑑 𝑟𝑒𝑝 𝑒 − 𝑤 𝑟𝑒𝑝 𝜃− 𝜃 𝑗 2 𝐽 𝑐𝑜𝑠𝑡 =𝐽+ 𝐽 𝑐𝑐

Pseudo code For each elimination-dispersal For each reproduction For each chemotaxis For each bacteria Calculate cost function 𝐉 𝐥𝐚𝐬𝐭 = 𝐉 𝐜𝐨𝐬𝐭 𝜽 𝒕 Tumble: generate 𝚫 For I = 1 to M Swim: 𝜽 𝒕+𝟏 =𝜽 𝒕 +𝑪∙𝚫 If 𝐉 𝐜𝐨𝐬𝐭 𝜽 𝒕+𝟏 < 𝑱 𝒍𝒂𝒔𝒕 Update 𝑱 𝒍𝒂𝒔𝒕 Update 𝜽 Else break Reproduce Eliminate and disperse

Demo

Applications Training neural network for short term electric load forecast Image enhancement Tuning adaptive median filter Improve peak signal to noise ratio of a highly corrupted image Tuning of PID (proportional derivative integral) controller parameters

Pros and cons Pros Cons Simple implementation Easy to distribute No local minima problem No need for gradient Fitness function can change over time Cons Complexity 𝑂 𝐶𝑜𝑙𝑜𝑛𝑦𝑆𝑖𝑧𝑒 2 Too many parameters to tune Fixed search step

Q/A Thank you for your attention! Momčilo Vasilijević, 15/3390 vm153390m@student.etf.rs