Towards a parameterless out-of-the-box population size control for evolutionary and swarm-based algorithms for single objective bound constrained real-parameter numerical optimization

We present an innovative step towards a parameterless out-of-the-box population size control for evolutionary and swarm-based algorithms for single objective bound constrained real-parameter numerical optimization. To the best of our knowledge, our approach is the first parameterless out-of-the-box parameter control for such a kind of technique. It is easy to implement and to use, since it does not require the adjustment of any parameter. The general idea is to increment the velocity of the population change if the best fitness stagnates, and decrement it otherwise. Then, in order to effectively change the population size, a mechanism of removal/addition of individuals inspired by the selection methods of evolutionary algorithms is executed. Our experimental results provide evidence that our controller is not only compatible with any evolutionary or swarm-based algorithm for single objective bound constrained real-parameter numerical optimization, but that it also performs well in many scenarios. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This article presents a parameterless out-of-the-box population size control method for evolutionary and swarm-based algorithms in single objective bound constrained real-parameter numerical optimization. It incrementally changes the velocity of population change based on the stagnation of fitness and utilizes a mechanism inspired by evolutionary algorithms for individual removal/addition to effectively change the population size. Experimental results demonstrate that the controller is compatible and performs well in various scenarios.