There are problems that are difficult to solve through mathematical programming or by classical methods. These problems are called hard problems due to their high complexity or high dimension. On the other hand, mataheuristics intends to seek a better solution to a problem. The Improvement Harmony Search algorithm is proposed under modification of the bandwidth parameter increasing the quality of the exploitation of the solutions. That is why within the state of the art, are mentioned several versions of harmonic search. The state of the art is supports the fact that the algorithm belongs to the category of those who make modifications to its parameters. This research demonstrates the ability of ImHS to solve a problem of high complexity focused on solving four-bar mechanism designs, whose solutions imply high dimension and which are also classified as hard problems. The two problems that are solved in this investigation, are problems very attacked within the state of the art by various metaheuristics. A comparison is then made against previous solutions with traditional metaheuristics and other versions of harmony search algorithm. Finally, the effectiveness of performance is demonstrated, where proposed algorithm it exceeded five metaheuristic algorithms and five harmony search versions. An optimum is provided in an easy and useful way, the parametric statistics are improved and the number of feasible solutions is exceeded in NPhard problems as in the case of problems with four-bar mechanisms.INDEX TERMS algorithms performance, four-bar mechanism, improved harmony search, optimization problems, mechatronic.
This article presents the development of a dynamic optimization method for controller tuning. This is proposed because most traditional tuning methods for complex coupled dynamic models are based on experience, and thus lacking accuracy. The case study is a Mobile Manipulator that consists of an anthropomorphic manipulator and a differential mobile platform. The system model has a trajectory generator that includes the coupled kinematic model, the Jacobian model, the coupled dynamic model, and a Proportional-Derivative plus controller. The tuning of the model is obtained by solving an optimization problem, using the Differential Evolution algorithm. This optimization approach allows to minimize simultaneously the energy consumption and the error on the trajectory tracking by the end effector. A novel strategy is applied to formulate the objective function, including constant weights for balancing the minimization effect. The objective is to avoid an energy consumption equal to zero that represents an error condition of no motion. The results of the case study and its statistical analysis are presented. The best result was modeled in Solidworks R and simulated in Matlab R. This model was exported to Simscape Multibody TM of Matlab, and its simulation produced satisfactory results, suggesting that the proposed optimization method can be a useful tool to solve real engineering problems.
Abstract-In this paper the variation of the velocity error of a four-bar mechanism with spring and damping forces is reduced by solving a dynamic optimization problem using a differential evolution algorithm with a constraint handling mechanism. The optimal design of the velocity control for the mechanism is formulated as a dynamic optimization problem. Moreover, in order to compare the results of the differential evolution algorithm, a simulation experiment of the proposed control strategy was carried out. The simulation results and discussion are presented in order to evaluate the performance of both approaches in the control of the mechanism.
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