University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Multi-tasking Multi-objective Evolutionary Operational Indices Optimization of Beneficiation Processes

Yang, Cuie, Ding, Jinliang, Jin, Yaochu, Wang, Chengzhi and Chai, Tianyou (2018) Multi-tasking Multi-objective Evolutionary Operational Indices Optimization of Beneficiation Processes IEEE Transactions on Automation Science and Engineering.

[img]
Preview
Text
Multi-tasking Multi-objective Evolutionary Operational Indices Optimization of Beneficiation Processes.pdf - Accepted version Manuscript

Download (1MB) | Preview

Abstract

Operational indices optimization is crucial for the global optimization in beneficiation processes. This paper presents a multi-tasking multi-objective evolutionary method to solve operational indices optimization, which involves a formulated multi-objective multifactorial operational indices optimization problem (MO-MFO) and a proposed multi-objective multifactorial optimization algorithm for solving the established MO-MFO problem. The MO-MFO problem includes multiple level of accurate models of operational indices optimization, which are generated on the basis of a dataset collected from production. Among the formulated models, the most accurate one is considered to be the original functions of the solved problem, while the remained models are the helper tasks to accelerate the optimization of the most accurate model. For the multifactorial optimization algorithm, the assistant models are alternatively in multi-tasking environment with the accurate model to transfer their knowledge to the accurate model during optimization in order to enhance the convergence of the accurate model. Meanwhile, the recently proposed two-stage assortative mating strategy for a multi-objective multifactorial optimization algorithm is applied to transfer knowledge among multi-tasking tasks. The proposed multi-tasking framework for operational indices optimization has conducted on 10 different production Conditions of beneficiation. Simulation results demonstrate its effectiveness in addressing the operational indices optimization of beneficiation problem.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Yang, Cuie
Ding, Jinliang
Jin, YaochuYaochu.Jin@surrey.ac.uk
Wang, Chengzhi
Chai, Tianyou
Date : 10 September 2018
DOI : 10.1109/TASE.2018.2865593
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Operational Indices Optimization; Beneficiation Process; Multi-tasking Optimization; Evolutionary Algorithms (EAs)
Related URLs :
Additional Information : Note to Practitioners
Depositing User : Clive Harris
Date Deposited : 13 Aug 2018 07:58
Last Modified : 15 Nov 2018 14:41
URI: http://epubs.surrey.ac.uk/id/eprint/848901

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800