Solving Multi-Objective Optimal Control Problems of Chemical Processes Using Hybrid Evolutionary Algorithm

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 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 explorative 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 .