Multi-objective bio-inspired optimization algorithm based on a P system

To solve the multi-objective optimization problem more efficiently, a P system bio-inspired optimization algorithm was proposed. The algorithm used the dynamic membrane structure to enhance its adaptability. The crowding distance selecting strategy of classical NSGA-II in combination with bionic autophagy mechanism was adopted to strengthen the solutions' diversity. Moreover, dynamic mutation rule, communication rule and crossover rule in inner loop made the last solutions gained by this method as close as possible to the true Pareto optimal front. Simulation results show that the solution set obtained by this algorithm possesses better convergence and diversity in solving multi-objective optimization problems. Additionally, to control the non-minimum phase processes better, a series of optimal PID controller set are obtained by using this algorithm. Based on these optimal controllers, the PID switch strategy gains a satisfactory performance.