Chaos quantum bee colony algorithm for constrained complicate optimization problems and application of robot gripper.

Global constrained optimization problems are very complex for engineering applications. To solve complicated and constrained optimization problems with fast convergence and accurate computations, a new quantum artificial bee colony algorithm using Sin chaos and Cauchy factor (SCQABC) is proposed. The algorithm introduces a quantum bit to initialize the population, which is then updated by the quantum rotation gate to enhance the convergence of the artificial bee colony algorithm (ABC). Sin chaos is introduced to process the individual positions, which improves the randomness and ergodicity of initializing individuals and results in a more diverse initial population. To overcome the upper limits of visiting target individual positions, Cauchy factor is used to mutate individuals for solving falling into local optimum problems. To evaluate the performance of the SCQABC, 20 classical benchmark functions and CEC-2017 are used. The practical engineering problems are also used to verify the practicability of SCQABC algorithm. Moreover, the experimental results will be compared with other well-known and progressive algorithms. According to the results, the SCQABC improves by 64.93% compared with the ABC and also has corresponding improvement compared with other algorithms. Its successful application to the robot gripper problem highlights its effectiveness in solving constrained optimization problems. [ABSTRACT FROM AUTHOR]