Bacterial foraging optimization using novel chemotaxis and conjugation strategies

Bacterial foraging optimization (BFO) has attracted much attention and been widely applied in a variety of scientific and engineering applications since its inception. However, the fixed step size and a lack of information communication between bacterial individuals during the optimization process have significant impacts on the performance of BFO. To address these issues on real-parameter single objective optimization problems, this paper proposes a new bacterial foraging optimizer using new designed chemotaxis and conjugation strategies (BFO-CC). Via the new chemotaxis mechanism, each bacterium randomly selects a standard-basis-vector direction for swimming or tumbling; this approach may obviate calculating a random unit vector and could effectively get rid of interfering with each other between different dimensions. At the same time, the step size of each bacterium is adaptively adjusted based on the evolutionary generations and the information of the globally best individual, which readily makes the algorithm keep a better balance between a local search and global search. Moreover, the new designed conjugation operator is employed to exchange information between bacterial individuals; this feature can significantly improve convergence. The performance of the BFO-CC algorithm was comprehensively evaluated by comparing it with several other competitive algorithms (based on swarm intelligence) on both benchmark functions and real-world problems. Our experimental results demonstrated excellent performance of BFO-CC in terms of solution quality and computational efficiency.