Population Dynamics Optimization Algorithm Under Microbial Control in Contaminated Environment

To solve some function optimization problems, the PDO-MCCE algorithm is proposed by using the population dynamics model with microbial control in contaminated environment. In this algorithm, individuals are automatically divided into two categories, normal population and mutation population. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, which solves the problem of determining the number of individuals artificially. The algorithm has 7 operators, among which the competition and mutation operators realize the information exchange within and between populations respectively; the influence and poison operators realize the information diffusion of strong individuals and the transfer of environmental information to individuals respectively; the new and death operators increase and reduce the number of individuals respectively; the growth operator ensures the algorithm has global convergence; the number of individuals in the mutation population increases periodically and the probability of jumping out from the trap of local optimal solutions can be greatly increased; in the iterative progress, the algorithm only deals with 3/500~1/10 of the number of individual features at a time, thus greatly reducing the time complexity. Test cases show that the PDO-MCCE algorithm has good performance and is suitable for solving some optimization problems with high dimensions