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The connection weights parameters play important roles in adjusting the performance of PID neural network (PIDNN) for complex control systems. However, how to obtain an optimal set of initial values of these connection weight parameters in a multivariable PIDNN called MPIDNN is still an open issue for system designers and engineers. This paper formulates this issue as a typical constrained optimization problem firstly by minimizing the cumulative sum of the product of exponential time and the system errors, and a real-time penalty function for overshoots of the system outputs, and then proposes an adaptive population extremal optimization-based MPIDNN method called PEO-MPIDNN for the optimal control issue of multivariable nonlinear control systems. The simulation results for two typical multivariable nonlinear control systems have demonstrated the superiority of the proposed PEO-MPIDNN to real-coded …