Convergence analysis of butterfly optimization algorithm

Convergence analysis of any random search algorithm helps to verify whether and how quickly the guarantees convergence point interest. Butterfly optimization (BOA) is a popular population-based stochastic optimizer introduced by mimicking foraging behaviors butterflies in nature. In this paper, we have developed Markov chain model BOA analyzed behavior algorithm. The constituted where population sequence generated found be finite homogenous defined state set reducible. performed mathematically using with help global theorem which based on satisfying two subtle conditions. butterfly has been satisfy conditions for enact, whereof it BOA. We also tried show experimentally that does not always considerable impact rate as influenced various other factors. Moreover, compared several state-of-the-art algorithms experimentally. Further, effects parameters, namely sensory modality power exponent performance BOA, studied.