Multi-objective optimization for PID parameter based on elitist-evolution guidance
基于精英进化导向的多目标PID参数优化

For multi-objective optimization problems, a decision-maker must choose one solution from many nondominated ones in Pareto front. Decision preferences are introduced into Pareto optimization in this paper, and a multiobjective genetic algorithm based on elitist-guidance mechanism is presented. Elitists are selected from Pareto optimal solutions according to decision-making preferences. The lossless-finite-precision method and the normalized incrementdistance are proposed to keep the population diversity. The effect of decision-making preferences is spread among the entire population by using the multi-population evolution mechanism. This approach is applied successfully to PID parameter optimization of automated-guided-vehicle(AGV) servo system, which can make a fast, effective and directional search for Pareto optimal solutions according to decision-making preferences, and ensures the servo control for achieving the velocity-response performance required by path tracking.