solving ‎‎‎multiobjective optimal control problems of chemical ‎processes ‎using ‎hybrid ‎evolutionary ‎algorithm

Evolutionary algorithms have been recognized to be suitable for extracting approximate solutions of multiobjective problems because of their capability to evolve a set of nondominated solutions distributed along the pareto frontier‎. ‎this paper applies an evolutionary optimization scheme‎, ‎inspired by multiobjective invasive weed optimization (moiwo) and nondominated sorting (ns) strategies‎, ‎to find approximate solutions for multiobjective 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 nondominated sorting genetic algorithm (nsgaii) and nondominated 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.