Solving Multi-objective Optimal Control Problems of chemical processes using Hybrid Evolutionary Algorithm
نویسندگان
1 Department of Mathematics and Computer Science, Damghan University, Damghan, Iran
2 Department of Mathematics, Payame Noor University, Tehran, Iran
3 Department of Mathematics, Payame Noor University, P.O.Box 19395-3697, Tehran, Iran
doi
10.22052/ijmc.2018.137247.1370چکیده
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.
