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ECDCOP SIGNED

Evolutionary Computation for Dynamic Constrained Optimization Problems

Total Cost €

EC-Contrib. €

Partnership

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Outcomes and
results

ECDCOP project word cloud

Explore the words cloud of the ECDCOP project. It provides you a very rough idea of what is the project "ECDCOP" about.

rail efficient concentrate benchmark communities constrained fundamentally industries indicators evolutionary world fill theoretical tool environmental evaluation function ec secondly stationary variables establishing constraint performance deal advancing benefit point involve made standardized dynamic dcops gap foundation attempts academia resemble constraints decision computation firstly optimization environments scenarios potentials networks time view fourthly kinds intend techniques handling few area thirdly

Project "ECDCOP" data sheet

The following table provides information about the project.

Coordinator	DE MONTFORT UNIVERSITY Organization address address: THE GATEWAY city: LEICESTER postcode: LE1 9BH website: www.dmu.ac.uk contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.
Coordinator Country	United Kingdom [UK]
Project website	http://www.tech.dmu.ac.uk/
Total cost	195˙454 €
EC max contribution	195˙454 € (100%)
Programme	1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
Code Call	H2020-MSCA-IF-2014
Funding Scheme	MSCA-IF-EF-ST
Starting year	2015
Duration (year-month-day)	from 2015-12-01 to 2017-11-30

Partnership

Take a look of project's partnership.

#	participants	country	role	EC contrib. [€]
1	DE MONTFORT UNIVERSITY DE MONTFORT UNIVERSITY Organization address address: THE GATEWAY city: LEICESTER postcode: LE1 9BH website: www.dmu.ac.uk contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	UK (LEICESTER)	coordinator	195˙454.00

Map

Project objective

Evolutionary computation (EC), as an efficient tool, has been widely applied to solve different kinds of stationary optimization problems. However, many real-world optimization problems are dynamic constrained optimization problems (DCOPs), where the objective function, constraints, decision variables, and environmental parameters may change over time. At present, very few attempts have been made to investigate this kind of optimization problems in the communities of optimization and EC. This project aims to fill this gap. In this project, we will concentrate on the design, analysis, and applications of EC for DCOPs, including the following five main aspects. Firstly, we will design a set of benchmark dynamic constrained optimization test environments which can resemble real-world scenarios. Secondly, we intend to design standardized performance indicators to evaluate EC methods for DCOPs. Thirdly, based on the standardized dynamic test and evaluation environments, we will design some novel and effective EC methods to solve DCOPs. Fourthly, we will present theoretical analysis of EC with different constraint-handling techniques for DCOPs, with the aim of establishing the theoretical foundation of this area. Finally, applying the developed EC methods to deal with DCOPs in rail networks is also one key aspect of this project. This project has great potentials to fundamentally change the way in which DCOPs are treated, both from a real-world point of view and from the point of view of advancing our theoretical understanding. The research results of this project will be of great interest to academia in many fields and of significant benefit to many industries that involve DCOPs.

Publications

List of publications.
year	authors and title	journal	last update
2018	Shouyong Jiang, Shengxiang Yang, Yong Wang, Xiaobin Liu Scalarizing Functions in Decomposition-based Multiobjective Evolutionary Algorithms published pages: 1-1, ISSN: 1089-778X, DOI: 10.1109/TEVC.2017.2707980	IEEE Transactions on Evolutionary Computation	2019-06-18
2018	Zhi-Zhong Liu, Yong Wang, Shengxiang Yang, Ke Tang An Adaptive Framework to Tune the Coordinate Systems in Nature-Inspired Optimization Algorithms published pages: 1-14, ISSN: 2168-2267, DOI: 10.1109/TCYB.2018.2802912	IEEE Transactions on Cybernetics	2019-06-18
2018	Yong Wang, Hao Liu, Huan Long, Zijun Zhang, Shengxiang Yang Differential Evolution with A New Encoding Mechanism for Optimizing Wind Farm Layout published pages: 1-1, ISSN: 1551-3203, DOI: 10.1109/TII.2017.2743761	IEEE Transactions on Industrial Informatics	2019-06-18
2017	Wenyin Gong, Yong Wang, Zhihua Cai, Shengxiang Yang A Weighted Biobjective Transformation Technique for Locating Multiple Optimal Solutions of Nonlinear Equation Systems published pages: 697-713, ISSN: 1089-778X, DOI: 10.1109/TEVC.2017.2670779	IEEE Transactions on Evolutionary Computation 21/5	2019-06-18
2018	Yong Wang, Da-Qing Yin, Shengxiang Yang, Guangyong Sun Global and Local Surrogate-Assisted Differential Evolution for Expensive Constrained Optimization Problems With Inequality Constraints published pages: 1-15, ISSN: 2168-2267, DOI: 10.1109/TCYB.2018.2809430	IEEE Transactions on Cybernetics	2019-06-18
2017	Yong Wang, Biao Xu, Guangyong Sun, Shengxiang Yang A Two-Phase Differential Evolution for Uniform Designs in Constrained Experimental Domains published pages: 665-680, ISSN: 1089-778X, DOI: 10.1109/TEVC.2017.2669098	IEEE Transactions on Evolutionary Computation 21/5	2019-06-18

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