A population-based algorithm with the selection of evaluation precision and size of the population

In this paper, a new nature-inspired hybrid population-based algorithm is proposed. Firstly, during its operation, it changes the size of the population to reduce the number of processed individuals. For this purpose, dedicated functions that determine the size of population for each algorithm step are used. Secondly, for each individual of the population, the algorithm selects and changes an operator for its modification. This provides a balance between searching for new solutions and fine-tuning of those already found. Thirdly, the algorithm can control the sampling period of the optimized (dynamic) systems, reducing the complexity of the fitness function for individuals. This makes it easier to use the algorithm to optimize even complex systems, which is of great practical importance. Finally, the algorithm allows to solve problems consisting in choosing the structure of the solution and the parameters of this structure. The control problems considered in the simulations, where both the parameters and the structure of the PID-based controller have to be selected, are exactly this type of problem. The results obtained for the proposed algorithm are significantly better than the results obtained with the use of other methods. (C) 2021 Elsevier B.V. All rights reserved.