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This paper puts forward an adaptive predator–prey optimization algorithm to solve the weight selection problem of linear quadratic control applied for vibration control of vehicle suspension system. The proposed technique addresses the two key issues of PSO, namely (a) the premature convergence of the particles, and (b) the imbalance between exploration and exploitation of the particles in finding the global optimum. The main principle behind this optimization algorithm is that the inertia weight is adaptively updated based on the success rate of the particles to increase the convergence, and the predator–prey strategy is reinforced to avoid the particles getting trapped in a local minimum thereby, guaranteeing convergence of the particles towards the global optimal solution. The convergence of the particles towards the global minimum is guaranteed on the basis of a passivity argument. Moreover, the strength of this new adaptive optimization technique to tune the gains of linear quadratic regulator is validated experimentally on a laboratory scale active vehicle suspension system for improved ride comfort and passenger safety.

Forecasting multivariate time series is an important problem considered in many real-world scenarios. To deal with that problem, several forecasting models have already been proposed, where Fuzzy Cognitive Maps (FCMs) are proved to be a suitable alternative. The key limitation of the existing FCM-based forecasting models is the lack of time-efficient learning algorithms. In this paper, we plug that gap by proposing a new FCM learning algorithm which is based on Moore–Penrose inverse. Moreover, we propose an innovative approach that equips FCM with long-term, multistep prediction capabilities. A huge advantage of our method is the lack of parameters which in the case of competitive approaches require laborious adjustment or tuning. The other added value of our method is the reduction of the processing time required to train FCM. The performed experiments revealed that FCM trained using our method outperforms the best FCM-based forecasting model reported in the literature.