Solving Multi-objective Optimal Control Problems of chemical processes using Hybrid Evolutionary Algorithm

Evolutionary algorithms have been recognized to be suitable 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. ‎This paper applies an evolutionary optimization scheme‎, ‎inspired by Multi-objective Invasive Weed Optimization (MOIWO) and Non-dominated Sorting (NS) strategies‎, ‎to find approximate solutions for multi-objective optimal control problems (MOCPs)‎. ‎The desired control function may be subjected to severe changes over a period of time‎. ‎In response to deficiency‎, ‎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. ‎ ‎The performance of the proposed algorithm ‎is compared with conventional Non-dominated Sorting Genetic Algorithm (NSGA-II) and Non-dominated Sorting Invasive Weed Optimization (NSIWO) algorithm‎.The results show that the proposed algorithm has better performance than others in terms of computing time‎, ‎convergence rate and diversity of solutions on the Pareto ‎frontier.