A novel hybrid spiral-dynamics bacterial-foraging algorithm for global optimization with application to control design

This paper presents a hybrid optimization algorithm referred to as Hybrid spiral dynamics bacterial foraging (HSDBF). The algorithm synergizes spiral adaptive simplified bacterial foraging algorithm (BFA) and spiral dynamics inspired optimization algorithm (SDA). The standard BFA has better exploitation strategy while SDA has superior exploration approach and stable convergence when approaching the optimum value. The hybrid algorithm preserves the strengths of BFA and SDA, thus producing better results. Moreover, it has simple structure and involves less computational burden. Several unimodal and multimodal benchmark functions are employed to test the algorithm in determining the global optimum point. Furthermore, the proposed method is applied to a proportional-derivative (PD) controller optimization for a flexible manipulator system (FMS). The results show that HSDBF outperforms BFA in all test functions and successfully optimizes the PD controller.

I. Introduction

Metaheuristic optimization algorithms have gained noticeable of interest among researchers worldwide. These algorithms are inspired by biological phenomena or natural phenomena. Some of the newly introduced algorithms include Bacterial foraging algorithm (BFA) [1], Biogeography-based optimization (BBO) [2], Cuckoo search optimization (CSO) [3], Galaxy-based search algorithm (GSO) [4], and Spiral dynamics inspired optimization (SDA) [5]. Nowadays BFA, BBO and CSO which have been inspired from biological phenomena are gaining popularity due to their capability and achievements in dealing with real world applications while GSO and SDA which have been inspired from natural phenomena seem to have similar potential applications as other optimization algorithms.