Abstract
Multiobjective optimization (MOO) refers to solving more than one-objective optimization problems simultaneously to attain a set of solutions, i.e., the Pareto front. In this study, an elitist strategy of multiobjective differential evolution (Elitist-MODE) algorithm is used to find the Pareto optimal solutions for a set of multiobjective test problems [POL (unconstrained), CONSTR-EX (constrained), and TNK (constrained)] and two-industrial engineering process problems, namely, styrene reactor and polyethylene terephthalate reactor (PET). A detailed analysis is reported for the test problems and real-world industrial problems, and the obtained Pareto fronts are compared with the Pareto front obtained using MODE and MODE III algorithms. Simultaneous maximization of selectivity and yield (for the styrene reactor) and simultaneous minimization of acid- and vinyl-end group concentrations (for the PET reactor) are considered. A detailed analysis of obtained Pareto fronts with respect to the set of decision variables for the individual industrial study is reported. It is observed that the improved strategies of MODE algorithms in general converge to the true Pareto front both for the test problems (constrained/unconstrained) and complex industrial problems. It is also suitable for solving the problems having disconnected Pareto front.
| Original language | English |
|---|---|
| Pages (from-to) | 455-463 |
| Number of pages | 9 |
| Journal | Materials and Manufacturing Processes |
| Volume | 26 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Apr 11 2011 |
| Externally published | Yes |
Keywords
- Differential evolution
- Elitist-MODE
- Genetic algorithms
- Industrial processes
- MODE
- MOOP
ASJC Scopus subject areas
- General Materials Science
- Mechanics of Materials
- Mechanical Engineering
- Industrial and Manufacturing Engineering