Salp Swarm Algorithm (SSA) draws inspiration from the swarming nature of salps. In order to alleviate the deficit of initial population quality and diversity, improper balancing of exploration and exploitation, and slow convergence speed, we have proposed a novel variant of SSA (LOLSSA). The effective evolutionary strategies of the basic SSA are combined with lens opposition-based learning to improve the initial population diversity. During exploration, the information about the local best position of each individual is effectively shared with the follower salp position update process to avoid stucking at the local optimum. At the end of each iteration, the local search algorithm is utilized to increase exploitation. Further, to accelerate the convergence speed, inertia weight is incorporated into the leader’s salp position.