A Multioperator Search Strategy Based on Cheap Surrogate Models for Evolutionary Optimization

It is well known that in evolutionary algorithms (EAs), different reproduction operators may be suitable for different problems or in different running stages. To improve the algorithm performance, the ensemble of multiple operators has become popular. Most ensemble techniques achieve this goal by choosing an operator according to a probability learned from the previous experience. In contrast to these ensemble techniques, in this paper we propose a cheap surrogate model-based multioperator search strategy for evolutionary optimization. In our approach, a set of candidate offspring solutions are generated by using the multiple offspring reproduction operators, and the best one according to the surrogate model is chosen as the offspring solution. Two major advantages of this approach are: 1) each operator can generate a solution for competition compared to the probability-based approaches and 2) the surrogate model building is relatively cheap compared to that in the surrogate-assisted EAs. The model is used to implement multioperator ensemble in two popular EAs, that is, differential evolution and particle swarm optimization. Thirty benchmark functions and the functions presented in the CEC 2013 are chosen as the test suite to evaluate our approach. Experimental results indicate that the new approach can improve the performance of single operator-based methods in the majority of the functions.