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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Publications

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Zhang X (2018) Computational Intelligence-Assisted Understanding of Nature-Inspired Superhydrophobic Behavior. in Advanced science (Weinheim, Baden-Wurttemberg, Germany)

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Zhang G (2020) A Task-Oriented Heuristic for Repairing Infeasible Solutions to Overlapping Coalition Structure Generation in IEEE Transactions on Systems, Man, and Cybernetics: Systems

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Yuan B (2016) Defect- and Variation-Tolerant Logic Mapping in Nanocrossbar Using Bipartite Matching and Memetic Algorithm in IEEE Transactions on Very Large Scale Integration (VLSI) Systems

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Yang P (2018) Turning High-Dimensional Optimization Into Computationally Expensive Optimization in IEEE Transactions on Evolutionary Computation

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Yang M (2017) Efficient Resource Allocation in Cooperative Co-Evolution for Large-Scale Global Optimization in IEEE Transactions on Evolutionary Computation

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Wang S (2018) A Systematic Study of Online Class Imbalance Learning With Concept Drift. in IEEE transactions on neural networks and learning systems

 
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