Discussion of article "Population optimization algorithms: Grey Wolf Optimizer (GWO)"

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New article Population optimization algorithms: Grey Wolf Optimizer (GWO) has been published:

Let's consider one of the newest modern optimization algorithms - Grey Wolf Optimization. The original behavior on test functions makes this algorithm one of the most interesting among the ones considered earlier. This is one of the top algorithms for use in training neural networks, smooth functions with many variables.

surrounding

Fig. 3. Diagram of the omega movement in relation to alpha, beta and delta


The pseudocode of the GWO algorithm is as follows:

1) Randomly initialize the gray wolf population.
2) Calculate the fitness of each member of the population.
3) Pack leaders:
-α = member with the best fitness value
-β = second best member (in terms of fitness)
-δ = third best member (in terms of fitness value)
Update the position of all omega wolves according to the equations depending on α, β, δ
4) Calculate the fitness of each member of the population.
5) repeat step 3.

Author: Andrey Dik

 

Those articles about metaheuristic optimization techniques are awesome! You are doing a great job Andrey, it's mind blowing how much experience you have to share with us, thank you!

@METAQUOTES please consider implement those metaheuristic optimization targets to the optimizer! It would be great for the software.

Something easy that user can set inside OnTester() as:

OptimizerSetEngine("ACO"); // Ant Colony Optimization
OptimizerSetEngine("COA"); // cuckoo optimization algorithm
OptimizerSetEngine("ABC"); // artificial bee colony
OptimizerSetEngine("GWO"); // grey wolf optimizer
OptimizerSetEngine("PSO"); // particle swarm optimisation


Cheers from Brazil

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