On the exploitation of search history and accumulative sampling in robust optimisation

Alyahya, Khulood; Doherty, Kevin; Fieldsend, Jonathan E.; Akman, Ozgur E.

doi:10.1145/3067695.3076060

Cited by 2 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As part of our work in this area, we have begun developing a suite of evolutionary optimisers specifically adapted to BDE systems that can be used together with a basic Python implementation of the parallel solver (Fieldsend et al, 2021). This suite currently contains a particle swarm optimiser that can identify multiple local optima within a given cost function landscape (Fieldsend, 2014) and an algorithm that utilises elite accumulative sampling to locate optima that are robust to disturbances in the parameter space (Alyahya et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

BDEtools: A MATLAB Package for Boolean Delay Equation Modeling

Akman

Doherty

Wareham

2023

Journal of Computational Biology

View full text Add to dashboard Cite

Boolean Delay Equations (BDEs) can simulate surprisingly complex behaviour, despite their relative simplicity. In addition to steady state dynamics, BDEs can also generate periodic and quasiperiodic oscillations, m:n frequency locking and even chaos. Furthermore, the enumerability of Boolean update functions and their compact parametrisation means that BDEs can be leveraged to generate low-level descriptions of biological networks, from which more detailed formulations (e.g. differential equation models) can be constructed. However, although several studies have demonstrated the utility of BDE modelling in computational biology, a current barrier to the wider adoption of the BDE approach is the absence of freely-available simulation software.In this work, we present BDEtools -an open-source MATLAB package for numerically solving BDE models. After giving a brief introduction to BDE modelling, we describe the package's solver algorithms, together with several utility functions that can be used to provide solver inputs and to process solver outputs. We also demonstrate the functionality of BDEtools by illustrating its application to an established model of a gene regulatory network that controls circadian rhythms.BDEtools makes it straightforward for researchers to quickly build reliable BDE models of biological networks. We hope that its ease of use and free availability will encourage more researchers to explore BDE formulations of their systems of interest. Through the continued use of BDEs by the computational biology community, we will no doubt discover their potential applicability to a broader class of biological networks.

show abstract

Section: Discussionmentioning

confidence: 99%

BDEtools: A MATLAB Package for Boolean Delay Equation Modeling

Akman

Doherty

Wareham

2023

Journal of Computational Biology

View full text Add to dashboard Cite

show abstract

“…It was found that implicit averaging method can be promising, but it should be accompanied with extra fitness evaluation to refine the final solutions obtained. To handle the same type of uncertainty, Fieldsend et al proposed an Elite Accumulating Sampling (EAS) in 2015 and 2017 [30,31,32]. This technique regularly re-evaluates the elites and accumulate the evaluations, which results in the need for additional function evaluations and higher computational cost.…”

Section: Related Work and Problem Backgroundmentioning

confidence: 99%

Confidence-based robust optimisation using multi-objective meta-heuristics

Mirjalili

Lewis

Dong

2018

Swarm and Evolutionary Computation

View full text Add to dashboard Cite

Robust optimisation refers to the process of finding optimal solutions that have the lowest sensitivity to possible perturbations. In a multi-objective search space the robust optimal solutions should have the least dispersion on all of the objectives, making it a more challenging problem than in a single-objective search space. This paper establishes a novel and cheap technique for finding robust optimal solutions called confidence-based robust multi-objective optimisation. This approach uses a novel, modified Pareto dominance operator to differentiate search agents of meta-heuristics based on both level of robustness and confidence. The proposed confidence-based Pareto dominance allows us to design different confidence-based robust optimisation variants of meta-heuristics based on different methods. As a case study, robust Multi-Objective Particle Swarm Optimisation is equipped with the proposed operator to produce Confidence-based Robust Multi-Objective Particle Swarm Optimisation. A set of specific test functions and performance indicators is employed for benchmarking the Confidence-based Robust Multi-Objective Particle Swarm Optimisation.The results show that the proposed method is able to confidently and reliably find robust optimal solutions without significant extra computational burden. The paper also considers finding robust Pareto optimal front for a marine propeller design problem to demonstrate the applicability of the approach proposed in solving computationally expensive real-world problems with unknown true robust Pareto optimal fronts.

show abstract

On the exploitation of search history and accumulative sampling in robust optimisation

Cited by 2 publications

References 8 publications

BDEtools: A MATLAB Package for Boolean Delay Equation Modeling

BDEtools: A MATLAB Package for Boolean Delay Equation Modeling

Confidence-based robust optimisation using multi-objective meta-heuristics

Contact Info

Product

Resources

About