Abstract
Backtracking search optimization algorithm (BSA) is a new evolutionary algorithm. It is a population-based evolutionary algorithm designed to solve global optimization problems. It has a similar structure to differential evolution, including selection, mutation and crossover processes. This structure guarantees that BSA can utilize the information of the whole population and maintain the population diversity in a high level. While all these operations are randomly executed and have no directionality, which makes BSA can't direct the individuals to search the region that has been detected to be promising with better solutions. Thus, we combine BSA with grey wolf optimization to provide search motivation towards the better individuals. The experiment results on CEC'17 benchmark suit indicate the feasibility of this combination.
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