Algorithm of marriage in honey bees optimization based on the local particle swarm optimization

Both Marriage in Honey Bees Optimization (MBO) and Particle Swarm Optimization (PSO) are swarm-intelligence methods. Combining Particle Swarm Optimization algorithm and an improved Marriage in Honey Bees Optimization algorithm we gave before, the paper proposes a new Marriage in Honey Bees Optimization algorithm, named Particle Swarm-Marriage in Honey Bees Optimization (PS-MBO). The local characteristic is obtained by reforming the original Particle Swarm Optimization, and it is used in the process of MBO algorithm to increase the performance of MBO. The global convergence characteristic of PS-MBO with the probability being 1 is proved by the Markov Chain theory. Some simulations are done based on some popular complex Evaluation Functions and Traveling Salesman Problem (TSP). By comparing PS-MBO with MBO and Genetic Algorithm (GA), the results show that PS-MBO has better convergence performance.