Vibhu Trivedi (Advisor: Prof. Manoj Ramteke)
Modeling, Simulation and Optimization of Chemical Processes
Multiobjective optimization (MOO) of industrial systems often involves
solving complicated model equations which require inordinately large
computational time for each function calculation (i.e. solving the model
once) and can be attempted for a limited number of function
calculations. For such cases, the algorithm which converges to the
acceptable Pareto optimal solutions in a small number of function
calculations (= population size à maximum number of generations) or in a
small number of generations (for fixed population size) is always
recommended. Since last decade, the concept of jumping gene from biology
has become quite popular for increasing the convergence speed of
binary-coded elitist non-dominated sorting genetic algorithm (NSGA-II).
This inspired several researchers to implement this concept in
real-coded elitist non-dominated sorting genetic algorithm which is free
from limitations of binary coding. However, these implementations have
achieved only a limited success. This is primarily due to their focus on
mimicking the procedure rather than simulating its effect. In order to
address this shortcoming, we have developed a new jumping gene operator,
namely, simulated binary jumping gene for real-coded NSGA-II (RNSGA-II).
The algorithm is first tested on 37 test MOO problems and then
successfully applied to the MOO problems of phthalic anhydride reactor
and dynamic steam reformer.