Particle swarm optimization for searching efficient experimental designs: A review

Chen, Ping‐Yang; Chen, Chih-Chia; Wong, Weng Kee

doi:10.1002/wics.1578

Cited by 17 publications

(7 citation statements)

References 58 publications

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“…One potentially useful approach for continuous time models may be ‘particle swarm’ optimization and other ‘nature inspired’ methods. 51 …”

Section: Discussionmentioning

confidence: 99%

Optimal study designs for cluster randomised trials: An overview of methods and results

Watson,

Girling,

Hemming

2023

Stat Methods Med Res

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There are multiple possible cluster randomised trial designs that vary in when the clusters cross between control and intervention states, when observations are made within clusters, and how many observations are made at each time point. Identifying the most efficient study design is complex though, owing to the correlation between observations within clusters and over time. In this article, we present a review of statistical and computational methods for identifying optimal cluster randomised trial designs. We also adapt methods from the experimental design literature for experimental designs with correlated observations to the cluster trial context. We identify three broad classes of methods: using exact formulae for the treatment effect estimator variance for specific models to derive algorithms or weights for cluster sequences; generalised methods for estimating weights for experimental units; and, combinatorial optimisation algorithms to select an optimal subset of experimental units. We also discuss methods for rounding experimental weights, extensions to non-Gaussian models, and robust optimality. We present results from multiple cluster trial examples that compare the different methods, including determination of the optimal allocation of clusters across a set of cluster sequences and selecting the optimal number of single observations to make in each cluster-period for both Gaussian and non-Gaussian models, and including exchangeable and exponential decay covariance structures.

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“…One potentially useful approach for continuous time models may be ‘particle swarm’ optimization and other ‘nature inspired’ methods. 51 …”

Section: Discussionmentioning

confidence: 99%

Optimal study designs for cluster randomised trials: An overview of methods and results

Watson,

Girling,

Hemming

2023

Stat Methods Med Res

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“…(2022) offers a thorough review of PSO in continuous optimal designs. 27 Walsh and Borkowski's works demonstrate PSO's effectiveness in generating optimal designs for RSM settings and discovering new 𝐺-optimal designs. 17,28,29 Walsh, Lu, and Anderson-Cook (2023) utilized PSO to analyze 𝐺and 𝐼criterion trade-offs through multi-objective optimization.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…al. (2022) offers a thorough review of PSO in continuous optimal designs 27 . Walsh and Borkowski's works demonstrate PSO's effectiveness in generating optimal designs for RSM settings and discovering new

G

‐optimal designs 17,28,29 .…”

Section: Introductionmentioning

confidence: 99%

Generating optimal designs with user‐specified pure replication structure

Walsh,

Bolton,

Borkowski

2024

Quality & Reliability Eng

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Over recent decades, optimal experimental design has surged in popularity due to affordable computing and efficient algorithms. However, some researchers highlight that this focus has often side‐lined the importance of including pure experimental replication in designs, as many optimality criteria do not inherently suggest designs with replicates. In this study, we introduce an approach that allows the designer to input a desired replication structure into the optimal design search. We list all potential replication structures, indicate the corresponding budget for pure‐error (PE) and lack‐of‐fit (LOF) degrees‐of‐freedom, and use particle swarm optimization to search for the optimal design in each scenario. We demonstrate our approach on various unexplored ‐optimal design scenarios and several scenarios recently discussed in literature. Our findings reveal the existence of highly‐efficient ‐optimal designs with unique replication structures, offering designers informed choices on allocating a limited budget for PE and LOF degrees‐of‐freedom. This method is versatile and compatible with other optimizers, such as the coordinate exchange.

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“…To generate optimal experimental designs, a particularly challenging task is to redefine the particle designs' movement toward its personal local-best design and global-best design. A review of some recent applications of PSO and its variants to tackle various types of efficient experimental design is [16]. Since finding optimal PWO designs for the OofA experiment is to solve a discrete optimization problem, we utilize an update procedure for the particle designs that is similar to the modified PSO algorithms in [17,18].…”

Section: The Performance Of the Exchange Algorithmmentioning

confidence: 99%

Constructing Optimal Designs for Order-of-Addition Experiments Using a Hybrid Algorithm

Wang

2023

Mathematics

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For order-of-addition experiments, the response is affected by the addition order of the experimental materials. Consequently, the main interest focuses on creating a predictive model and an optimal design for optimizing the response. Van Nostrand proposed the pairwise-order (PWO) model for detecting PWO effects. Under the PWO model, the full PWO design is optimal under various criteria but is often unaffordable because of the large run size. In this paper, we consider the D-, A- and M.S.-optimal fractional PWO designs. We first present some results on information matrices. Then, a flexible and efficient algorithm is given for generating these optimal PWO designs. Numerical simulation shows that the generated design has an appealing efficiency in comparison with the full PWO design, though with only a small fraction of runs. Several comparisons with existing designs illustrate that the generated designs achieve better efficiencies, and the best PWO designs and some selected 100% efficient PWO designs generated by the new algorithm are reported.

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Particle swarm optimization for searching efficient experimental designs: A review

Cited by 17 publications

References 58 publications

Optimal study designs for cluster randomised trials: An overview of methods and results

Optimal study designs for cluster randomised trials: An overview of methods and results

Generating optimal designs with user‐specified pure replication structure

Constructing Optimal Designs for Order-of-Addition Experiments Using a Hybrid Algorithm

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