Abstract: The Salp Swarm Algorithm (SSA) may have trouble in dropping into stagnation as a kind of swarm intelligence method. This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning to compensate for the above weakness called QBSSA. In the proposed QBSSA, an adaptive barebones strategy can help to reach both accurate convergence speed and high solution quality; quasi-oppositional-based learning can make the population away from traping into local optimal and expand the search space. To estimate the performance of the presented method, a series of tests are performed. Firstly, CEC 2017 benchmark test suit is used to test the ability to solve the high dimensional and multimodal problems; then, based on QBSSA, an improved Kernel Extreme Learning Machine (KELM) model, named QBSSA–KELM, is built to handle medical disease diagnosis problems. All the test results and discussions state clearly that the QBSSA is superior to and very competitive to all the compared algorithms on both convergence speed and solutions accuracy.

Key words: Salp swarm algorithm, Bare bones, Quasi-oppositional based learning, Function optimizations, Kernel extreme learning machine