<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The nature of optimization for intermittent sugar cane crystallization process is to obtain ideal crystals. One typical difficulty in crystallization optimization refers to the simultaneous effects of both seeding characters and process variables on the final crystal size distribution (CSD) parameters, including mean size (MA) and coefficient of variation (CV). And the application of traditional multiobjective evolutionary algorithm in crystallization process could not optimize all of them. Therefore, this paper puts forward a different multi-objective framework, and correspondingly, an improved optimization algorithm is applied to intermittent sugar cane crystallization. This method combines the elitist non-dominated sorting genetic algorithm (NSGA-II) with technique for order preference which is similar to an ideal solution (TOPSIS), and it provides a quantitative way to analyze the effect of both seed characteristics and process variables on the trade-off between MA and CV. Furthermore, the proposed algorithm has been adapted here to be compared with the NSGA-II, and the comparing results demonstrate better Pareto-optimal solutions of the novel approach</p>
</abstract>

The nature of optimization for intermittent sugar cane crystallization process is to obtain ideal crystals. One typical difficulty in crystallization optimization refers to the simultaneous effects of both seeding characters and process variables on the final crystal size distribution (CSD) parameters, including mean size (MA) and coefficient of variation (CV). And the application of traditional multiobjective evolutionary algorithm in crystallization process could not optimize all of them. Therefore, this paper puts forward a different multi-objective framework, and correspondingly, an improved optimization algorithm is applied to intermittent sugar cane crystallization. This method combines the elitist non-dominated sorting genetic algorithm (NSGA-II) with technique for order preference which is similar to an ideal solution (TOPSIS), and it provides a quantitative way to analyze the effect of both seed characteristics and process variables on the trade-off between MA and CV. Furthermore, the proposed algorithm has been adapted here to be compared with the NSGA-II, and the comparing results demonstrate better Pareto-optimal solutions of the novel approach

An Improved Multi-Objective Evolutionary Optimization Algorithm For Sugar Cane Crystallization

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The nature of optimization for intermittent sugar cane crystallization process is to obtain ideal crystals. One typical difficulty in crystallization...

An Improved Multi-Objective Evolutionary Optimization Algorithm For Sugar Cane Crystallization

Published Online: Jun 01, 2016

Page range: 953 - 978

Received: Dec 17, 2015

Accepted: Dec 28, 2016

DOI: https://doi.org/10.21307/ijssis-2017-903

Keywords
Multi-objective optimization, Intermittent sugar cane crystallization, NSGA-II, TOPSIS

© 2016 Yanmei Meng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

An Improved Multi-Objective Evolutionary Optimization Algorithm For Sugar Cane Crystallization

Published Online: Jun 01, 2016

Page range: 953 - 978

Received: Dec 17, 2015

Accepted: Dec 28, 2016

DOI: https://doi.org/10.21307/ijssis-2017-903

KeywordsMulti-objective optimization, Intermittent sugar cane crystallization, NSGA-II, TOPSIS

© 2016 Yanmei Meng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
Multi-objective optimization, Intermittent sugar cane crystallization, NSGA-II, TOPSIS