An Improved Chaos Bird Swarm Optimization Algorithm

Abstract

Aiming at the defect that bird-swarm algorithm (BSA) is easily trapped in the local optimum and appears premature convergence for high-dimensional functions, an improved chaos bird-swarm optimization algorithm (LCDE-BSA) is proposed in the paper. Logistic chaotic mapping is used to initialize the population and make the initial distribution to the entire population of space. Population catastrophe is adopted to escape from local optimum. Differential evolution algorithm is used to enhance the population diversity and improve the optimizing efficiency by mutation operation, crossover operation and selection operation on individuals except the current best individual. Experiments on six criteria test functions indicate that LCDE-BSA has better global search ability and convergence properties than BSA.