Hybrid butterfly based particle swarm optimization for optimization problems

One of the superior optimization algorithms amongst all earlier introduced algorithms is the particle swarm optimization algorithms. This paper introduces Butterfly Particle swarm optimization (BF-PSO) with some novel control parameters such as sensitivity of butterfly towards nectar by different means of communication and probability of the nectar presence. This new hybrid algorithm is based on the intelligent characteristics and behavior of butterfly during the process of food (nectar) search and mimics their intelligent network structures. Sensitivity and the probability of nectar, according to the degree of nodes is calculated using this new algorithm. By adding the effect of these modifications in the standard Particle Swarm Optimization (PSO), the algorithm performance and the ability to search optimum value of the Particle Swarm Optimization is improved. Finally, the results of applying the BF-PSO on benchmark functions are shown. The overall improvement in performances of the BF-PSO on the basis of the sensitivity of butterfly and probability of nectar source.