A novel hybrid spiral dynamics bacterial chemotaxis algorithm for global optimization with application to controller design

This paper presents a hybrid optimization algorithm, referred to as hybrid spiral dynamics bacterial chemotaxis (HSDBC) algorithm. HSDBC synergizes bacterial foraging algorithm (BFA) chemotaxis strategy and spiral dynamics algorithm (SDA). The original BFA has higher convergence speed while SDA has better accuracy and stable convergence when approaching the optimum value. This hybrid approach preserves the strengths of BFA and SDA and thus has the capability of producing better results. Moreover, it has simple structure, hence reduced computational cost. Several unimodal and multimodal benchmark functions are employed to test the algorithm in finding the global optimum point. Furthermore, the proposed algorithm is tested in the design of PD controller for a flexible manipulator system. The results show that the HSDBC outperforms SDA and BFA in all test functions and successfully optimizes the PD controller.