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In this paper, a new multi-objective differential evolution with a diversity preserving mechanism called the ε-elimination algorithms is used for the Pareto optimum design of gripper mechanisms. The ε-elimination diversity is used to improve the population diversity among the obtained Pareto front. In the ε-elimination diversity approach based on a threshold value all the clones or ε-similar individuals are recognized and simply removed from the current population. It should be noted that such e-similarity must exist both in the space of objectives and in the space of the associated design variables. The proposed algorithm has been used for two different configuration of robot’s gripper. The dimensions of mechanisms are considered as design variables and optimally selected by proposed algorithm to improve the efficiency of griper mechanism. Two conflicting objectives which are the difference between maximum and minimum gripping forces and the transmission ratio of actuated and experienced gripper forces, are considered for Pareto optimization. The best configuration of gripper mechanism is suggested by comparing of trade-off design points. The comparisons of the obtained Pareto front using the method of this paper with those obtained in other references shows a significant improvement.

Keywords: Differential evolution, multi-objective optimization, Robot Gripper, mechanism

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