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

Heuristic optimization provides a robust and efficient approach forextracting approximate solutions of multiobjective problems because of theircapability to evolve a set of nondominated solutions distributed along thePareto frontier. The convergence rate and suitable diversity of solutions areof great importance for multiobjective evolutionary algorithms. The focus ofthis 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 multiobjective optimal control problems (MOCPs). In the proposed method, the process of dispersal has beenmodified in the MOIWO. This modification will increase the exploration powerof the weeds and reduces the search space gradually during the iteration process. Thus, the convergence rate and diversity of solutions along the Paretofrontier have been promote. Finally, the ability of the proposed algorithm isevaluated and compared with conventional NSGA-II and NSIWO algorithmsusing three practical MOCPs. The results show that the proposed algorithmhas better performance than others in terms of computing time, convergenceand diversity.