Active Multi-Population Pattern Searching Algorithm for flow optimization in computer networks – The novel coevolution schema combined with linkage learning

Przewoźniczek, Michał Witold

doi:10.1016/j.ins.2016.02.048

Cited by 20 publications

(18 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The examples of strategies with bounded and rather slow islands' number growth are Classic [16] and Active [27] strategies, also for single objective problems. Both these strategies increase the number of subpopulations by one when all subpopulations are stuck.…”

Section: Related Workmentioning

confidence: 99%

“…The remaining three algorithms introduce the dynamic islands management facility. The design rationale of ManagerClassic and ManagerActive is inspired by the single-objective managers presented in [27,16].…”

Section: Dynamic Determination Of the Number Of Islandsmentioning

confidence: 99%

“…As the number of GA's islands can be determined dynamically based on the optimisation state [16,27], it is important to have short island initialisation time. However, provisioning of a single virtual machine (VM) in a cloud was reported to take more than a minute [23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Scalable distributed evolutionary algorithm orchestration using Docker containers

Dziurzański

Zhao

Przewoźniczek

et al. 2020

Journal of Computational Science

Self Cite

View full text Add to dashboard Cite

In smart factories, integrated optimisation of manufacturing process planning and scheduling leads to better results than a traditional sequential approach but is computationally more expensive and thus difficult to be applied to real-world manufacturing scenarios. In this paper, a working approach for cloud-based distributed optimisation for process planning and scheduling is presented. Three managers dynamically governing the creation and deletion of subpopulations (islands) evolved by a multi-objective genetic algorithm are proposed, compared and contrasted. A number of test cases based on two real-world manufacturing scenarios are used to show the applicability of the proposed solution.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Dynamic Determination Of the Number Of Islandsmentioning

confidence: 99%

See 1 more Smart Citation

Scalable distributed evolutionary algorithm orchestration using Docker containers

Dziurzański

Zhao

Przewoźniczek

et al. 2020

Journal of Computational Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…The idea is to increase the number of subpopulations when the method is stuck and to decrease it when the best-found solution is frequently improved. The examples of such propositions are Classic [8] and Active [12] SDSNC, proposed for single objective optimization problems. Both strategies increase the number of subpopulations when all subpopulations are stuck.…”

Section: Related Workmentioning

confidence: 99%

“…Two managers, ManagerClassic and ManagerActive, decide dynamically on the number of islands based on the current optimisation state, whereas the third manager, ManagerStatic, uses a fixed, predefined number of islands and acts as the baseline method. The idea behind the dynamic managers has been inspired by their single-objective counterparts from [8,12].…”

Section: Determination Of the Number Of Islandsmentioning

confidence: 99%

Cloud-based dynamic distributed optimisation of integrated process planning and scheduling in smart factories

Zhao

Dziurzański

Przewoźniczek

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

Self Cite

View full text Add to dashboard Cite

In smart factories, process planning and scheduling need to be performed every time a new manufacturing order is received or a factory state change has been detected. A new plan and schedule need to be determined quickly to increase the responsiveness of the factory and enlarge its profit. Simultaneous optimisation of manufacturing process planning and scheduling leads to better results than a traditional sequential approach but is computationally more expensive and thus difficult to be applied to real-world manufacturing scenarios. In this paper, a working approach for cloud-based distributed optimisation of process planning and scheduling is presented. It executes a multi-objective genetic algorithm on multiple subpopulations (islands). The number of islands is automatically decided based on the current optimisation state. A number of test cases based on two real-world manufacturing scenarios are used to show the applicability of the proposed solution.

show abstract

The Effectiveness of the Simplicity in Evolutionary Computation

Przewoźniczek

Walkowiak

Aibin

2017

Intelligent Information and Database Systems

View full text Add to dashboard Cite

Active Multi-Population Pattern Searching Algorithm for flow optimization in computer networks – The novel coevolution schema combined with linkage learning

Cited by 20 publications

References 41 publications

Scalable distributed evolutionary algorithm orchestration using Docker containers

Scalable distributed evolutionary algorithm orchestration using Docker containers

Cloud-based dynamic distributed optimisation of integrated process planning and scheduling in smart factories

The Effectiveness of the Simplicity in Evolutionary Computation

Contact Info

Product

Resources

About