Abstract: To solve some function optimization problems,the optimization algorithm based on the impulsive toxicant input model of ecotoxicology dynamics was constructed．In the algorithm,an environment system corresponds to the search space of an optimization problem,and there is pollution in the environment system,and some pollution sources pour toxicant pollutants into the environment system impulsively and periodically．Many different classes of population live in the system,and there are competition and predatory-prey relation among different classes of population,and each population in a class of population is just an alternative solution of an optimization problem．The ecotoxicology dynamics model is mapped into describing the change of some features of a population.The interaction between environment and populations as well as among populations is used to construct evolution operators of populations,and these operators realize sufficient information exchange between environment and populations as well as among populations．The research results show that environment pollution gives influence on a very small part of features of a population,which means that only a very small part of features take part in computation.Then convergence speed of the algorithm can be substantially improved,and impulsively discharged toxicant pollutants result in fierce change of state value of a feature of the population,which enables a search to jump out from local optima easily.Strong populations who can endure pollution keep growing,while week populations who can not endure pollution will stop growing,which ensures the algorithm to converge．The case study shows that for some function optimization problems the algorithm has higher speed of convergence and higher accuracy of global optima than the existed population-based intelligent optimization algorithms.

Key words: Function optimization,Intelligent optimization computation,Ecotoxicology dynamics,Environment pollution