A Novel Hybrid Spiral-Dynamics Random-Chemotaxis Optimization Algorithm With Application To Modelling Of A Flexible Robot Manipulator

摘要

This paper presents a hybrid spiral-dynamics algorithm with random-chemotaxis; a synergy between chemotactic strategy of bacteria and spiral dynamics algorithm. Bacterial foraging algorithm has a good local search technique through random tumble and swim action. However it has slow convergence speed and high processing time. On the contrary, spiral dynamics algorithm is relatively simple and has very good global search technique based on its spiral model. This results in fast convergence speed andudshort computation time. However, the insufficient local search strategy may possibly trap the algorithm to local optima thus reduced accuracy. Incorporating bacteria chemotactic strategy into spiral dynamics algorithm can effectively solve the problems. Moreover, theudstrengths of both original algorithms can be preserved hence improving their performances. In this work, the proposed algorithm is used as an optimization tool toudestimate parameters of high order dynamic model of a flexible manipulator system. The results show that the proposed algorithm has faster convergence speed and higherudaccuracy in comparison to its predecessor algorithms. On the other hand, time-domain and frequency-domain responses show that the algorithm can predict better model for theudflexible manipulator system.