Flying Fox
Optimizer

The Flying Fox Optimizer (FFO) was developed by Dr. Konstantinos Zervoudakis and Prof. Stelios Tsafarakis to solve optimization problems and it was published in the journal of Engineering with Computers in 2023.

Zervoudakis, K., & Tsafarakis, S. (2023). A global optimizer inspired from the survival strategies of flying foxes. Engineering with Computers, 39, 1583–1616. https://doi.org/10.1007/s00366-021-01554-w

The Flying Foxes Optimization (FFO) draws inspiration from the survival strategies of flying foxes during a heatwave. The proposed method exploits a Fuzzy Logic (FL) technique to determine the parameters individually for each solution, thus resulting in a parameters-free optimization algorithm. The comparison results reveal that the proposed FFO optimizer constitutes a powerful attractive alternative for global optimization.

Single-objective FFO

Objective function f(x), x=(x1,x2,...xn)

Calculate population size (N) and Survival List (SL) size (NL)

Initialize the flying fox population

Evaluate solutions

Find coolest and hottest positions (Best and worst solutions found so far)

Do While stopping criteria are not met

For i=1:N

Calculate the parameters for flying fox i

Update position of flying fox i

Evaluate new solution

if new solution is better

Accept the new solution

else

if new solution is in a far region

Replace flying fox i in far region through SL

Evaluate new solution

end

Update coolest and hottest positions and SL

end

Update pD

Replace flying foxes according to pD

Evaluate new solutions

Update coolest and hottest positions and SL

end while

Postprocess results and visualization.

Click on the links below to download simplified Matlab or Python demo codes, which show how to use the Flying Fox Optimizer to solve global optimization problems.

Matlab: Click here
Python optiseek library provided by A. Dundore: Click here

Researchers are allowed to use the demo codes mentioned above in their research projects, as long as they cite as:

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Flying FoxOptimizer

Single-objective FFO

Flying Fox
Optimizer