Heterogeneous ensemble algorithms for function optimization

Ensemble optimization algorithms have been widely used in solving complex optimization problems because they can make full use of the advantages of different algorithms or strategies. However, it is very difficult to integrate multiple optimization methods with different structures in an algorithm framework because evolutionary mechanisms or coding methods are different in different evolutionary computation (EC) algorithms. To give full play to the complementary advantages of different algorithms without obviously increasing the computational complexity of the algorithm, a framework of heterogeneous ensemble algorithms (EHA) with multiple populations is developed. First, to maintain the diversities of the subpopulations, a new grouping strategy considering the convergence accuracy and acceleration is designed to divide the population into three subpopulations. This can decrease the probability of local convergence of the algorithm. Second, three algorithms considering the characteristics of the individuals are distributed for the subpopulations to balance the exploitation and exploration abilities of the framework. Third, a method for chromosome modification is designed to realize the smooth evolution of individuals in different subpopulations. Finally, the individuals are regrouped to realize the information exchange among three subpopulations. The performance of EHA is evaluated on two data suites (IEEE CEC2005 and IEEE CEC2014), and the results are compared with those of some other algorithms. The results indicate that EHA has excellent optimization performance.

Automated summary

The paper proposes a framework of heterogeneous ensemble algorithms (EHA) that integrates multiple optimization methods with different structures. The EHA framework effectively utilizes the advantages of different algorithms without significantly increasing computational complexity. Evaluation results indicate that EHA has excellent optimization performance.