A Multi-Strategy Integrated Optimized Salp Swarm Algorithm and Its Application for Engineering Problems.

Abstract

Salp Swarm Algorithm has the advantages of few adjustment parameters and easy implementation, which has been applied in many fields, such as data mining, image processing, and engineering calculation. However, this algorithm is easy to fall into local optimization and sometimes the accuracy of optimization is not high. To solve the above problems in SSA, this paper proposes an orthogonal opposition-based adaptive slap swarm algorithm OOASSA. First, an orthogonal opposition learning strategy was introduced when updating the leader position to enhance the adequacy of global search and improve the algorithm's ability to get out of the local extreme value. Then, it was introduced adaptive inertia weight when updating the follower position and introduced an adaptive adjustment strategy in the ratio of a leader-follower number to ensure that the algorithm had a good global development ability in the early stage of iteration. Good local exploration ability in late iteration. In this paper, 10 test functions and 3 engineering optimization problems in CEC2017 are used for simulation experiments, and the proposed algorithm's global exploration, local mining, and local optimization capabilities are significantly better than many of the most advanced SSA variants.