Pareto-optimal Solutions for Multi-objective Optimal Control Problems using Hybrid IWO/PSO Algorithm

Heuristic optimization provides a robust and efficient approach for extracting approximate solutions of multi-objective problems because of their capability to evolve a set of non-dominated solutions distributed along the Pareto frontier. The convergence rate and suitable diversity of solutions are of great importance for multi-objective evolutionary algorithms. The focus of this paper is on a hybrid method combining two heuristic optimization techniques, Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO), to find approximate solutions for multi-objective optimal control problems (MOCPs). In the proposed method, the process of dispersal has been modified in the MOIWO. This modification will increase the exploration power of the weeds and reduces the search space gradually during the iteration process. Thus, the convergence rate and diversity of solutions along the Pareto frontier have been promote. Finally, the ability of the proposed algorithm is evaluated and compared with conventional NSGA-II and NSIWO algorithms using three practical MOCPs. The results show that the proposed algorithm has better performance than others in terms of computing time, convergence and diversity.