Ensemble of Population-Based Metaheuristic Algorithms

No optimization algorithm can obtain satisfactory results in all optimization tasks. Thus, it is an effective way to deal with the problem by an ensemble of multiple algorithms. This paper proposes an ensemble of population-based metaheuristics (EPM) to solve single-objective optimization problems. The design of the EPM framework includes three stages: the initial stage, the update stage, and the final stage. The framework applies the transformation of the real and virtual population to balance the problem of exploration and exploitation at the population level and uses an elite strategy to communicate among virtual populations. The experiment tested two benchmark function sets with five metaheuristic algorithms and four ensemble algorithms. The ensemble algorithms are generally superior to the original algorithms by Friedman's average ranking and Wilcoxon signed ranking test results, demonstrating the ensemble framework's effect. By solving the iterative curves of different test functions, we can see that the ensemble algorithms have faster iterative optimization speed and better optimization results. The ensemble algorithms cannot fall into local optimum by virtual populations distribution map of several stages. The ensemble framework performs well from the effects of solving two practical engineering problems. Some results of ensemble algorithms are superior to those of metaheuristic algorithms not included in the ensemble framework, further demonstrating the ensemble method's potential and superiority.