Discussion of article "Population optimization algorithms: Cuckoo Optimization Algorithm (COA)"

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New article Population optimization algorithms: Cuckoo Optimization Algorithm (COA) has been published:

The next algorithm I will consider is cuckoo search optimization using Levy flights. This is one of the latest optimization algorithms and a new leader in the leaderboard.

Let us dwell in more detail on the process of laying eggs by the cuckoo. From all the nests, a nest where the egg is supposed to be laid will be randomly selected. Since the egg is a solution, it can be represented by the quality of the egg. If the cuckoo egg is of higher quality than the parent egg, then it will be replaced. Otherwise, the parent egg will remain in the nest. In fact, subsequent evolution will continue from the surviving chick. This means that if the chick of the parent egg survived, then evolution will continue from the same place. Further development is possible only if the cuckoo egg turns out to be more viable and the search for solving the problem continues from a new place. The decision tree is schematically shown in Figure 1.


decision tree

Fig. 1. Decision tree. The red dot is the beginning, the green dot is the final decision

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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