A Memetic Approach to the Solution of Constrained Min-Max Problems

Filippi, Gianluca; Vasile, Massimiliano

doi:10.1109/cec.2019.8790124

Cited by 9 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For more details about the method please refer to Refs. 12,13 For the evaluated optimal design solution d * A , the decomposition approach presented in Refs. 6,[9][10][11] has been applied to the ML-ENM in order to propagate uncertainty through the spacecraft model and reconstruct a good approximation of the belief curve with a fraction of the computational cost required for the exact one (Fig.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Multi Layer Evidence Network Model for the Design Process of Space Systems Under Epistemic Uncertainty

Filippi¹,

Vasile

2020

Computational Methods in Applied Sciences

Self Cite

View full text Add to dashboard Cite

The purpose of this paper is to introduce a new method for the design process of complex systems affected by epistemic uncertainty. In particular, a multi-layer network is proposed to model the whole design process and describe the transition between adjacent phases. Each layer represents a design phase with a particular detail definition, each node a subsystem and each link a sharing of information. The network is used to quantify and propagate uncertainty through the different layers (design phases) where, proceeding from phase A to phase F, the detail of the mathematical model is increased. Thus, it can be considered as a multi-fidelity approach for the design of a complex system affected by epistemic uncertainty. The framework of Dempster-Shafer Theory of Evidence (DST) is used to model epistemic uncertainty and the model is then called Multi-Layer Evidence Network Model (ML-ENM).

show abstract

Section: Methodsmentioning

confidence: 99%

“…Then the system is optimised for robustness with the min-max algorithm. 12,13 Evidence Theory is applied to quantify uncertainty on the optimal solution. 6,[9][10][11] It is finally shown that the optimal solution at phase A is robust against the uncertainty in the next design phases.…”

Section: Introductionmentioning

confidence: 99%

A Multi Layer Evidence Network Model for the Design Process of Space Systems Under Epistemic Uncertainty

Filippi¹,

Vasile

2020

Computational Methods in Applied Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…Then the system is optimised for robustness with a min-max algorithm. 12,13 Evidence Theory is applied to quantify uncertainty on the optimal solution. 6,[9][10][11] It is finally shown that the optimal solution at phase A is robust against the uncertainty in the next design phases.…”

Section: Introductionmentioning

confidence: 99%

Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences

2021

Computational Methods in Applied Sciences

View full text Add to dashboard Cite

The purpose of this paper is to introduce a new method for the design process of complex systems affected by epistemic uncertainty. In particular, a multi-layer network is proposed to model the whole design process and describe the transition between adjacent phases. Each layer represents a design phase where each node is a subsystem and each link a sharing of information. The network is used to quantify and propagate uncertainty through the different layers (design phases). At each phase, from phase A to phase F, the fidelity of the mathematical model describing each subsystem and component is increased. Thus, it can be considered as a multi-fidelity approach for the design of a complex system affected by epistemic uncertainty. The framework of Dempster-Shafer Theory of Evidence (DST) is used to model epistemic uncertainty. The model is then called Multi-Layer Evidence Network Model (ML-ENM).

show abstract

“…We use a graph representation to model the CEdS and the interaction of the subsystems and components under uncertainty [3]. This approach allows to have a holistic and coherent view of the entire system and design process as well as simplifying the communications between different actors that are involved.…”

Section: Introductionmentioning

confidence: 99%

Network Resilience Optimisation of Complex Systems

Filippi

Vasile

2021

Advances in Uncertainty Quantification and Optimization Under Uncertainty With Aerospace Applications

Self Cite

View full text Add to dashboard Cite

This paper presents a method for the design for resilience of complex systems under uncertainty. In this approach, the complexity of the system is captured by a network formulation. Each node i is completely determined by two indicators: a performance fi and a functionality ci. Both these measures depend on time, decision and uncertain variables. In particular, we suggest the functional measure of resilience. The node resilience depends on the evolution over time of its state xi which is solution of a differential equation. Bifurcation theory is used to continuously model transitions between fully functioning and degraded states, disruptions and shocks that could affect the node. This work shows how to exploit the network properties and to optimise the global connected resilience emerging from the coupled dynamics of the single nodes.

show abstract

A Memetic Approach to the Solution of Constrained Min-Max Problems

Cited by 9 publications

References 22 publications

A Multi Layer Evidence Network Model for the Design Process of Space Systems Under Epistemic Uncertainty

A Multi Layer Evidence Network Model for the Design Process of Space Systems Under Epistemic Uncertainty

Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences

Network Resilience Optimisation of Complex Systems

Contact Info

Product

Resources

About