A guaranteed global convergence social cognitive optimizer (Q1718612)

Summary: From the analysis of the traditional social cognitive optimization (SCO) in theory, we see that traditional SCO is not guaranteed to converge to the global optimization solution with probability one. So an improved social cognitive optimizer is proposed, which is guaranteed to converge to the global optimization solution. The global convergence of the improved SCO algorithm is guaranteed by the strategy of periodic restart in use under the conditions of participating in comparison, which helps to avoid the premature convergence. Then we give the convergence proof for the improved SCO based on Solis and Wets' research results. Finally, simulation results on a set of benchmark problems show that the proposed algorithm has higher optimization efficiency, better global performance, and better stable optimization outcomes than the traditional SCO for nonlinear programming problems (NLPs).