Name
Title | ||
|---|---|---|
An elitist non-dominated sorting genetic algorithm enhanced with a neural network applied to the multi-objective optimization of a polysiloxane synthesis process |
Abstract | ||
|---|---|---|
This paper presents an original software implementation of the elitist non-dominated sorting genetic algorithm (NSGA-II) applied and adapted to the multi-objective optimization of a polysiloxane synthesis process. An optimized feed-forward neural network, modeling the variation in time of the main parameters of the process, was used to calculate the vectorial objective function of NSGA-II, as an enhancement to the multi-objective optimization procedure. An original technique was utilized in order to find the most appropriate parameters for maximizing the performance of NSGA-II. The algorithm provided the optimum reaction conditions (reaction temperature, reaction time, amount of catalyst, and amount of co-catalyst), which maximize the reaction conversion and minimize the difference between the obtained viscometric molecular weight and the desired molecular weight. The algorithm has proven to be able to find the entire non-dominated Pareto front and to quickly evolve optimal solutions as an acceptable compromise between objectives competing with each other. The use of the neural network makes it also suitable to the multi-objective optimization of processes for which the amount of knowledge is limited. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.engappai.2011.02.004 | Eng. Appl. of AI |
Keywords | Field | DocType |
multi-objective optimization,neural network,optimum reaction condition,genetic algorithm,molecular weight,reaction temperature,entire non-dominated pareto front,multi-objective optimization procedure,polysiloxane,elitist non-dominated sorting genetic algorithm,polysiloxane synthesis process,reaction time,reaction conversion,feed forward neural network,objective function,pareto front,multi objective optimization | Mathematical optimization,Computer science,Sorting,Multi-objective optimization,Reaction temperature,Artificial neural network,Software implementation,Genetic algorithm | Journal |
Volume | Issue | ISSN |
24 | 5 | Engineering Applications of Artificial Intelligence |
Citations | PageRank | References |
8 | 0.51 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Renata Furtuna | 1 | 33 | 1.76 |
| Silvia Curteanu | 2 | 63 | 6.26 |
| Florin Leon | 3 | 71 | 15.03 |