Discussion of article "Population optimization algorithms: Ant Colony Optimization (ACO)"
Those articles about metaheuristic optimization techniques are awesome. You are doing a great job Andrey, it's mindblowing how much experience you are sharing 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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New article Population optimization algorithms: Ant Colony Optimization(ACO) has been published:
This time I will analyze the Ant Colony optimization algorithm. The algorithm is very interesting and complex. In the article, I make an attempt to create a new type of ACO.
The ACO algorithm is a kind of swarm intelligence algorithm. By modeling the foraging process of an ant colony, the shortest path in various environments is established using the ant colony's internal data transfer mechanism. The higher the concentration of the pheromone remaining on the path, the higher the likelihood that the ant will choose this path. At the same time, the concentration of the pheromone diminishes over time. Therefore, due to the behavior of the ant colony, the ants are constantly learning and optimizing through a feedback mechanism to determine the shortest foraging path. The ACO algorithm is widely used in path planning.
ACO on the Skin test function
ACO on the Forest test function
ACO on the Megacity test function
So, it is time for conclusions. On the one hand, the conventional Ant Colony algorithm is not applicable to optimization problems for trading financial instruments. However, in an attempt to avoid the limitations of the conventional version, we have witnessed the emergence of a completely new concept of the Ant Colony algorithm allowing for further ACO development. Such an algorithm can already be applied to a wide range of problems, including the traveling salesman problem.
Author: Andrey Dik