Abstract
Backtracking search optimization algorithm (BSA) is new evolutionary algorithm (EA) that to solve global optimization problems. BSA is similar to evolutionary algorithm, including selection, crossover, mutation and operations. The core idea of BSA is to get guidance from the previous population and search solutions with better fitness. While BSA only evaluates the individuals based on the fitness, which could make the individuals with better exploration prospects be discarded when updating population. Thus, in this paper, we proposed a novel way to improve exploration abilities that utilize negative correlation learning enhanced search behavior in BSA to further improve its search ability. We used benchmark function suit CEC'17 to verify the proposed new algorithm and the experiment result indicates the feasibility of this hybridization.
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