Abstract:

An improved knowledge-based cultural algorithm（IKBCA） was proposed to solve dynamic optimization problem of chemical and biochemical processes. The first step of IKBCA was to discrete time region and control region, and to encode chromosome by taking advantage of soft constraint strategy. The second step was to use the process of iteration: “initialize population”-“evolve control region”-“optimize population” to achieve the optimal control profile. IKBCA inserted genetic algorithm (GA) into the population space and differential evolution algorithm (DE) into the brief space. By analyzing, extracting, and managing three kinds of improved knowledge developed from the individual information computed, the evolutionary process could be well guided. When applied to dynamic optimization problem of three typical chemical processes with distinguishing control features, IKBCA demonstrated a competitive optimal searching ability, and its feasibility and effectiveness were verified for the dynamic nonlinear constrained optimization problem.