Adaptive PBIL algorithm for a class of dynamic optimization problems

In an uncertain environment, the environmental changes always occur with probabilities. In this paper the moment when a change occurs is considered as a random variable, which obeys certain distribution, and the dynamic problems possess such features are classified as a class of dynamic optimization problems. Then an adaptive population-based incremental learning (PBIL) algorithm is proposed to solve the class of dynamic optimization problems. This algorithm applies the adaptive probability of random variable to regulate the probable model of the current population. The objectives are to increase the population diversity and to rapidly adapt the environmental changes. Results of case study show that compared with traditional PBIL algorithm, the proposed adaptive PBIL algorithm can track the dynamic solution reliably and accurately.