To solve the problems that the basic salp swarm algorithm (SSA) converges slowly and is easy to fall into the local optimum

a new modified SSA based on refracted opposition-based learning (ROBL) and adaptive control factor (RCSSA) was proposed. First

the ROBL mechanism was used to calculate the refracted opposite solution of individual solution

which greatly improved the convergence accuracy and speed of the algorithm. Then

the adaptive control factor of the leader in SSA was introduced into the position update of the follower

which could effectively control the entire search process and increase the local exploitation ability. To verify the optimization performance of the proposed algorithm

seven unimodal

16 multimodal benchmark functions

and one engineering design problem were utilized to investigate the algorithm. In the experiment

two SSAs improved by single strategy were introduced to verify the proposed algorithm

and five state-of-the-art intelligent optimization algorithms such as whale optimization algorithm were added to further verify the superiority of the algorithm. Research results show that the addition of ROBL mechanism and adaptive control factor could effectively enhance the exploitation and exploration abilities of the basic SSA for both low-dimensional and high-dimensional benchmark optimization problems

and the optimization performance of RCSSA was better than most other intelligent algorithms.