Evolutionary Computation for Dynamic Optimisation in Network Environments
Lead Research Organisation:
University of Birmingham
Department Name: School of Computer Science
Abstract
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Organisations
People |
ORCID iD |
| Xin Yao (Principal Investigator) |
Publications
Mei Y
(2016)
A Competitive Divide-and-Conquer Algorithm for Unconstrained Large-Scale Black-Box Optimization
in ACM Transactions on Mathematical Software
Zhang X
(2018)
Computational Intelligence-Assisted Understanding of Nature-Inspired Superhydrophobic Behavior.
in Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Qian C
(2018)
On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments.
in Evolutionary computation
Li M
(2017)
How to Read Many-Objective Solution Sets in Parallel Coordinates [Educational Forum]
in IEEE Computational Intelligence Magazine
Fahy C
(2022)
Finding and Tracking Multi-Density Clusters in Online Dynamic Data Streams
in IEEE Transactions on Big Data
Tang K
(2017)
A Scalable Approach to Capacitated Arc Routing Problems Based on Hierarchical Decomposition.
in IEEE transactions on cybernetics
Li M
(2018)
Multiline Distance Minimization: A Visualized Many-Objective Test Problem Suite
in IEEE Transactions on Evolutionary Computation
Fu H
(2015)
Robust Optimization Over Time: Problem Difficulties and Benchmark Problems
in IEEE Transactions on Evolutionary Computation
Wang M
(2018)
Population Evolvability: Dynamic Fitness Landscape Analysis for Population-Based Metaheuristic Algorithms
in IEEE Transactions on Evolutionary Computation
Omidvar M
(2017)
DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization
in IEEE Transactions on Evolutionary Computation
| Description | The key discoveries include new algorithms for capacitated arc routing problems in uncertain environments; a new formulation of robust optimisation over time to better capture and define characteristics of real world problems; a new dynamic scheduling algorithms for dynamic scheduling and its application in software project scheduling; a multi-population approach to dynamic travelling salesman problems; and a comprehensive review of railway rescheduling. |
| Exploitation Route | Our findings can be used by the academic communities for further studies both in terms of theories and new algorithms. They can also be investigated by non-academic sectors. We are actively organising workshops where we invite industrialists to participate and discuss our findings. |
| Sectors | Digital/Communication/Information Technologies (including Software),Transport |
| URL | http://www.cercia.ac.uk/projects/ecdone/ |
| Description | This research was focused on computational studies of evolutionary dynamic optimisation algorithms. Its impact has been on the academic community. One of the highlights of this project was its study of dynamic multi-objective optimisation problems with a changing number of objectives. That was the first time a comprehensive experimental study was carried out and, as a result, a new evolutionary algorithm was designed. This work, published in IEEE Transactions on Evolutionary Computation in 2017, has attracted more than 100 citations according to Google Scholar. With knowledge gained during the investigation of dynamic evolutionary algorithms, we were able to developed new dynamic algorithms for software project scheduling and flexible job shop scheduling problems. Unlike most dynamic evolutionary optimisation at that time, we formulated a new and potentially more practical dynamic optimisation problem: robust optimisation over time. We explored potential links between dynamic optimisation and reinforcement learning. New algorithms for tackling large scale optimisation were proposed. |
| First Year Of Impact | 2016 |
| Sector | Education |
| Impact Types | Societal |