Recent Meta-Heuristics Improved by Self-Adaptation Applied to Nonlinear Model-Based Predictive Control

Abstract

Nonlinear Model-based Predictive Control (NMPC) is a relevant research area having applications in the industrial sector. Traditionally, in this technique, gradient descent algorithms have been used to solve the related optimization problem. More recently, bio-inspired meta-heuristics have also been applied to this problem. However, only a few works have been devoted to testing solvers that use parameter control with self-adaptive traits, which allows mitigating the problem of offline parameter tuning in bioinspired approaches. In this paper, we propose the novel Adaptive Modified Grey Wolf Optimization (AMGWO) and the Adaptive Moth-Flame Optimization (AMFO), for solving Nonlinear Model-based Predictive Control (NMPC) problems. To achieve this, a mechanism for individual leaders weighting and a crossover operator are introduced in AMGWO, and a simple self-adaptive parameter technique is applied in both meta-heuristics. The improved solvers are tested to perform the swing-up of a single inverted pendulum and attitude control of a satellite, which are nonlinear problems relevant for assessing control performance. Nonparametric statistical tests are applied to compare the improved meta-heuristics optimization outcomes with other five meta-heuristics, which shows that the self-adaptive parameter technique can significantly improve the performance when applied as an NMPC solver, as the AMFO and AMGWO statistically outperform or performs as well as all algorithms compared in both the pendulum and satellite control, respectively. This is important as improving the optimizer efficiency will lead to more accurate control and enable rapid hardware implementation.

Optimal Stochastic Process Optimizer: A New Metaheuristic Algorithm With Adaptive Exploration-Exploitation Property

“…However, this algorithm: 1) takes the number of function evaluations as the criterion to distinguish exploration and exploitation, which is too simple; 2) uses search history to update new parameters in the next iteration, which may lead to local optimum. In [34], authors proposed self-adaptation versions of Grey Wolf Optimization (AMGWO) and Moth Flame Optimization (AMFO). The self-adaptive parameters of these two algorithms are controlled by the previous obtained solutions.…”