Swarm-based algorithm can successfully avoid the local optimal constraints, thus achieving a smooth balance between exploration and exploitation. Salp swarm algorithm (SSA), as a swarm-based algorithm on account of the predation behavior of the salp, can solve complex daily life optimization problems in nature. SSA also has the problems of local stagnation and slow convergence rate. This paper introduces an improved salp swarm algorithm, which improve the SSA by using the chaotic sequence initialization strategy and symmetric adaptive population division. Moreover, a simulated annealing mechanism based on symmetric perturbation is introduced to enhance the local jumping ability of the algorithm. The improved algorithm is referred to SASSA. The CEC standard benchmark functions are used to evaluate the efficiency of the SASSA and the results demonstrate that the SASSA has better global search capability. SASSA is also applied to solve engineering optimization problems. The experimental results demonstrate that the exploratory and exploitative proclivities of the proposed algorithm and its convergence patterns are vividly improved.

Background: Gene expression and disease control are regulated by the interaction between distal enhancers and proximal promoters, and the study of enhancer promoter interactions (EPIs) provides insight into the genetic basis of diseases. Objective: Although the recent emergence of high-throughput sequencing methods have a deepened understanding of EPIs, accurate prediction of EPIs still limitations. Methods: We have implemented a XGBoost-based approach and introduced two sets of features (epigenomic and sequence) to predict the interactions between enhancers and promoters in different cell lines. Results: Extensive experimental results show that XGBoost effectively predicts EPIs across three cell lines, especially when using epigenomic and sequence features. Conclusion: XGBoost outperforms other methods, such as random forest, Adadboost, GBDT, and TargetFinder.