Multimodal Function Optimization Using Synchronous Bacterial Foraging Optimization Technique

AbstractThe paper presents an improved variant of bacterial foraging optimization (BFO) named as Synchronous Bacterial Foraging Optimization (SBFO). The proposed SBFO is used to optimize multimodal and high dimensional functions. As all the bacteria update their information simultaneously, it has been named synchronous. A mutation operator proposed in this paper performs the global search. In SBFO, all bacteria process information independently in the same generation; hence parallel computers can be used to evaluate fitness values. The performance of SBFO is validated on a set of seven benchmark functions i.e. Sphere, Rosenbrock, Rastrigin, Griewank, Ackley, Schaffer’s, Shekel’s Foxholes. The results are compared with other published methods such as classical BFO, hybrid BFO (BSO), swarm-based algorithms, differential evaluation and Ashaker. The simulation results on benchmark functions show that the proposed optimization is capable of producing good quality global optima as compared to the above mentione...