One of the main challenges in the design of passive suspension systems is the optimum selection of suspension system parameters. In this paper, a-four-degree-of-freedom quarter car model is implemented in order to design an optimal suspension system for better ride comfort and road holding characteristics. The mathematical model was generated in MATLAB Simulink environment for simulation. The Multi-objective particle swarm optimisation algorithm is used to optimise the suspension parameters such as suspension spring stiffness, damping coefficient of dampers, driver seat stiffness and driver seat damping coefficient. In addition, an artificial neural network model is trained to predict the root mean square values of ride comfort and road holding characteristics for a given set of input parameters by using the neural network toolbox in MATLAB. The results show that the acceleration of sprung mass and head decayed to a minimum under 2 seconds and the magnitude of the acceleration of the head was lower than that of the sprung mass. The unsprung mass was not displaced from the ground for more than 0.014m and road holding characteristics were also similar.
The paper proposes a new optimization algorithm that is extremely robust in solving mathematical and engineering problems. The algorithm combines the deterministic nature of classical methods of optimization and global converging characteristics of meta-heuristic algorithms. Common traits of nature-inspired algorithms like randomness and tuning parameters (other than population size) are eliminated. The proposed algorithm is tested with mathematical benchmark functions and compared to other popular optimization algorithms. The results show that the proposed algorithm is superior in terms of robustness and problem solving capabilities to other algorithms. The paradigm is also applied to an engineering problem to prove its practicality. It is applied to find the optimal location of multi-type FACTS devices in a power system and tested in the IEEE 39 bus system and UPSEB 75 bus system. Results show better performance over other standard algorithms in terms of voltage stability, real power loss and sizing and cost of FACTS devices.
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