Adaptive PBIL algorithm and its application to solve scheduling problems

An adaptive population-based incremental learning algorithm (APBIL) is presented basing on analyzing the characteristics of traditional PBIL algorithm in this paper. Overcoming disadvantages of traditional PBIL algorithm, the proposed APBIL algorithm can adjust learning rate and mutation probability automatically according to the evolution degree of the algorithm's searching performed. Extensive computational tests with Flow shop and Job shop scheduling problems are carried out. The results compared with standard PBIL algorithm's and genetic algorithm's show that the proposed algorithm exceed the traditional PBIL algorithm and GA in calculation efficiency and search capability. The proposed algorithm can acquire stable high quality solution.