Discrete binary honey bees mating optimization with capability of learning

Abstract

Honey Bees Mating Optimization (HBMO) is a novel developed method used in different engineering areas. Optimization process in this algorithm is inspired of natural mating behavior between bees. In this paper, we have attempted to create a reciprocal relation between learning and evolution which can produce an algorithm with the power of dominating local optimums and finding global optima. In the proposed model, a set of learning Automata, which can produce reinforcement signal by obtaining feedback from queens, is attributed to each drone. Simulation and comparisons based on several well-studied benchmarks demonstrate the effectiveness, efficiency and robustness of the proposed algorithms. © 2011 Springer-Verlag.