Abstract: To solve large-scale optimization problems(OP),a population dynamics-based optimization algorithm with global convergence was constructed based on the population dynamics theory．In the algorithm,each population is just an alternative solution of OP,and a feature attribute of a population corresponds to a variable of an alternative solution.The principle of orthogonal Latin squares is used to produce initial values of populations so as to cover search space with balance dispersion and neat comparability.The competition,mutual-benefit,predator-prey,mergence,mutation and selection behavior between any two populations are used to construct evolution policies of populations so as to ensure population suitability index(PSI)of each population to keep either to stay unchanged or to transfer toward better states,therefore the global convergence is ensured.During evolution process of populations,each population’s transferring from one state to another realizes the search for the global optimum solution．The stability condition of a reducible stochastic matrix was applied to prove the global convergence of the algorithm．The case study shows that the algorithm is efficient.

Key words: Evolutionary computation,Function optimization,Biogeography-based optimization,Population dynamics