 Research
 Open Access
Particle swarm based algorithms for finding locally and Bayesian Doptimal designs
 Yu Shi^{1}Email authorView ORCID ID profile,
 Zizhao Zhang^{1} and
 Weng Kee Wong^{1}
https://doi.org/10.1186/s4048801900924
© The Author(s) 2019
 Received: 1 November 2018
 Accepted: 18 March 2019
 Published: 8 April 2019
Abstract
When a modelbased approach is appropriate, an optimal design can guide how to collect data judiciously for making reliable inference at minimal cost. However, finding optimal designs for a statistical model with several possibly interacting factors can be both theoretically and computationally challenging, and this issue is rarely discussed in the literature. We propose natureinspired metaheuristic algorithms, like particle swarm optimization (PSO) and its variants, to solve such optimization problems. We demonstrate that such techniques, which are easy to implement, can find different types of optimal designs for models with several factors efficiently. To facilitate use of such algorithms, we provide computer codes to generate tailor made optimal designs and evaluate efficiencies of competing designs. As applications, we apply PSO and find Bayesian optimal designs for Exponential models useful in HIV studies and redesign a carrefuelling study for a Logistic model with ten factors and some interacting factors.
Keywords
 Bayesian design
 Design efficiency
 Generalized linear model
 Metaheuristic algorithms
Introduction
Statistical models are getting increasingly complex to capture the finer features of a problem. Models incorporate more factors as data becomes highdimensional and heterogeneous. When the model assumptions are tenable, it is important to develop and implement efficient design strategies to realize the most reliable statistical inference at minimal cost.
In the optimal design literature, we typically assume that the statistical model is fully parametrized, known and defined on a userselected design space, apart from the unknown parameters in the model. An optimal design is found under a given criterion and the optimization is usually over all designs in the design space. Frequently, the goal is to estimate one or more parameters, the response surface or a couple of meaningful functions of the model parameters. Unless the model is relatively simple, closed form formulae for the optimal designs rarely exist. Sometimes, additional assumptions are imposed to derive the optimal designs analytically and some of the assumptions may be unrealistic. Further, the bulk of the work in the optimal design literature assumes models have a couple of explanatory factors only and when there are several of them, they are usually assumed to be additive to simplify the derivation. A practical and useful way to handle design problems with many interacting explanatory factors is to develop efficient computational tools that find various types of optimal designs for a broad class of models under realistic assumptions.
We propose a stateoftheart class of algorithms called natureinspired metaheuristic algorithms for solving high dimensional design problems. We call them high dimensional design problems because there are many variables to optimize. Our experience is that traditional design algorithms tend to have problems finding optimal designs when there are several variables to optimize. They are likely to stall at a local optimum or break down because of the huge computational burden when there are many variables to optimize. Several researchers had reported similar experiences with traditional algorithms for finding optimal designs. An early one is Chaloner and Larntz (1989) who found both the (Wynn 1972) and (Fedorov 1972) algorithms very slow when they tried to find A and cBayesian optimal designs for the twoparameter logistic model. They then used the general optimization algorithm proposed by (Nelder and Mead 1965) and found it to be adequate. Similarly, (Broudiscou et al. 1996) claimed that traditional algorithms, such as FedorovWynn types of algorithms or exchange algorithms for finding optimal designs cannot be used to find nonstandard designs, such as asymmetrical Doptimal designs. They found the algorithms performed poorly and difficult to handle and so not effective; they used genetic algorithms instead. Similarly, (Royle 2002) reported that the traditional exchange algorithms are not practical for finding large spatial designs when the criterion is computationally expensive to evaluate or the discretized design space is large. These may be reasons why the bulk of the optimal experimental designs reported in the literature concern a small number of factors.
Natureinspired metaheuristic algorithms, such as particle swarm optimization (PSO) or one of its enhanced versions, such as competitive swarm optimizer (CSO), are more likely to solve optimization problems with a large number of variables to optimize. These algorithms are general purpose optimization tools and by construction, do not require any assumptions on the optimization problem. For example, these algorithms can solve optimization problems when the objective function is nondifferentiable or even when the criterion cannot be written down explicitly. This article describes PSO, its variant CSO briefly and demonstrates their capability for finding optimal designs for a broad class of models with multiple factors, including Bayesian optimal designs.
The next section reviews the optimal design setup and “Particle swarm optimization based algorithms for generating optimal designs” section presents the particle swarm optimization algorithm. In “Websites for finding optimal designs” section, we present websites where codes for finding optimal designs are available and demonstrate with a simple example. In “Optimal designs for high dimensional models” section, we apply CSO to find highdimensional Doptimal designs for Logistic and Poisson models. In “Bayesian optimal designs for biomedical studies” section, we apply PSO to find Bayesian Doptimal designs for Exponential models useful in HIV studies. We then conclude with a discussion on future work and a cautionary remark on use of optimal designs in practice.
Background
Following convention, the worth of a design ξ is gauged by its Fisher information matrix. For nonlinear models, the information matrix depends on the unknown values of the parameters θ and we denote this matrix by M(ξ,θ). The design criterion is then expressed as a function of this matrix and as a first step, we typically replace the unknown θ in the matrix by its nominal value, θ_{0}. The resulting optimal design is called locally optimal design since it depends on θ_{0}, which may come from a pilot study or from previous similar studies (Chernoff 1972).
and as before, the smaller the Bayesian Doptimality criterion value is, the better is the design. Clearly, when the prior density is degenerate, the resulting design becomes locally Doptimal. Both criteria are appropriate for estimating model parameters.
with equality at the support points of ξ_{BayesD}.
The relative efficiency ratio compares performance of the two designs for estimating the model parameters. If the above ratio 0.5 or 50% efficiency, this means that the design ξ_{1} needs twice as many observations for it to do as well as the design ξ_{2}. When ξ_{2} is the Doptimal design, the above ratio is simply the Defficiency of the design ξ_{1}.
The next section describes a natureinspired metaheuristic algorithm and one of its variants for finding Doptimal designs for the Poisson regression models, Bayesian Doptimal designs for Exponential models and Doptimal designs for highdimensional Logistic and Poisson models.
Particle swarm optimization based algorithms for generating optimal designs
Metaheuristic algorithms are increasingly common, and a key appeal of such algorithms is that there is no or minimum assumptions required for them to work well. Metaheuristic algorithms usually involve some randomization and local searches. In particular, they go through slightly different processes and end up with frequently not too different results (Yang 2010).
We focus on particle swarm optimization (PSO), which is a member of the class of metaheuristic algorithms. It is now widely and routinely used in the engineering field. PSO was first developed in 1995 by Eberhart and Kennedy (1995). Motivated by swarm intelligence, they simulated candidate designs for the optimum using them as birds in a swarm looking for food on the ground. The swarm collectively acts and communicates to update where each bird believes where the food is (personal best) and flies toward it in the direction of the group best, which is where the flock believes the food lies after sharing information within the flock. The objective function gets updated at each iteration as the bird flies over time in search of a quality solution. Many have reported that PSO can significantly outperform genetic algorithms (GA) in terms of number of function evaluations required (Hassan et al. 2005).
To initiate the PSO algorithm, the user first selects a swarm size where each particle in the swarm represents a randomly generated candidate design. Below is the pseudocode for PSO (Kennedy 2006):
Begin
Initialize particle position and velocity
While maximum iterations or minimum error criteria is not attained
Do
For each particle
Evaluate objective function
Update particle personal best
End
Set particle with the best objective function value as the group best
For each particle
Update particle velocity: \(v^{t+1}_{id}=wv^{t}_{id}+c_{1}\psi _{1}\left (p^{t}_{id}x^{t}_{id}\right)+c_{2}\psi _{2}\left (p^{t}_{gd}x^{t}_{id}\right)\)
Update particle position: \(x^{t+1}_{id}=x^{t}_{id}+v^{t+1}_{id}\)
End
End
In the pseudocode, w is inertia weight, \(v^{t}_{id}\) and \(x^{t}_{id}\) are velocity and position of d^{th} dimension of i^{th} particle at iteration step t, c_{1}, c_{2} are weight constants, ψ_{1} and ψ_{2} are random values from uniform [0,1] distribution, p_{id} is the personal best of d^{th} dimension of particle i (the best position particle i ever visited), p_{gd} is the group best of d^{th} dimension of the swarm (the best position the group ever visited). Each particle represents a candidate design with k support points, and k (≥m) is user selected. The dimension of each particle is therefore 2k−1 because we need to optimize the locations of the k support points and their corresponding weights. The dimension is one smaller since the weights sum to unity.
We now show how PSO can find different types of optimal designs effectively for different types of generalized linear models. The examples are meant to be illustrative with some details for those new to the area; others may use our codes directly from the following websites that contain codes for finding optimal designs for more complicated situations.
Websites for finding optimal designs
There are websites with MATLAB PSO codes that we have written for finding various optimal designs for commonly used models. They include http://wkwong.bol.ucla.edu/podpack/index.html, http://www.math.ntu.edu.tw/~optdesign/and http://www.stat.ncku.edu.tw/optdesign/. Each code is for a specific design problem for a particular model. The user inputs the required information for their design problem and the PSO code searches iteratively for the optimum.
The available codes on our website find optimal designs for different models under different criteria. Models include commonly used linear, MichaelisMenten, mixture polynomial, logistic, compartmental, Hill’s, doubleexponential, exponential, Poisson, etc. Criteria include D, D_{s}, A, G, E, minimax, etc.
The aim in many toxicity studies is to ascertain the joint toxicity effects from several toxicants on the number of organisms or cells that survive when we apply different dose combinations of the toxicants. There is limited work to address design issues for such studies and when they are available, they usually have one or two explanatory variables in the model (Russell et al. 2009; Wang et al. 2006; Qiu 2014). To fix ideas, we use a Poisson model with two toxicants to illustrate how our sites facilitate search for a locally Doptimal design for a Poisson regression model. The website has codes that are able to find designs with more interacting toxicants.
where F=(f(x_{1}),…,f(x_{k}))^{T} and W=diag(w_{1}λ_{1},…,w_{k}λ_{k}). We consider a synergism effect in this example, and set the nominal values for θ_{0}, θ_{1}, θ_{2} and the interaction term θ_{12} to be 0.1, 0.1, 0.1, and 0.01. The values are nonpositive because we expect the effect of each toxicant is such that fewer cells will survive when the dose of the toxicant is increased. We also set the nominal value of θ_{12} to be smaller than the additive effects, which is usually the case in practice (Wang et al. 2006). Another restriction on the feasible design region representing doses or concentrations of the toxicants is that their values are nonnegative.
Optimal designs for high dimensional models
In practice, models are likely to have several explanatory factors. This is because a few explanatory factors may not capture the complex structure of the full data adequately. This section shows that CSO, a variant of PSO can tackle highdimensional optimal design problems for the Logistic and Poisson models.
Locally Doptimal designs for 5factor Logistic and Poisson regression models
We expect the locally Doptimal design for each of the above models has at least k≥16 design points because there are 16 parameters in both models. This means that we have k−1 weights to determine and at least k≥16 design points to determine, implying the total number of variables we need to optimize in this problem is at least 95. If k=25, for instance, this number becomes 149 and so the problem becomes highdimensional rapidly. In the event that the Doptimal design has k=16=m support points, we have a minimallysupported design.
As always, the choice of the tuning parameters in an evolutionary algorithm deserves attention. For the hard highdimensional problems in this paper, we used 200 particles. The values for the other parameters we used were suggested by (Cheng and Jin 2015); in particular, we set ϕ=0.05 in CSO. We stop the algorithm when the change in the values of objective function from successive iterations is smaller than 10^{−5}.
We implemented PSO and Genetic Algorithm (GA) and compared their performance with CSO for searching Doptimal designs for high dimensional models. The choices for the tuning parameters in PSO were w=0.9 and c_{1}=c_{2}=2 (Shi and Eberhart 1998). The tuning parameter EliteCount in Genetic Algorithm was 0.05, which is recommended by the Matlab official implementation of the code. The swarm size of PSO and GA was also 200. Since evolutionary algorithms are stochastic and produce slightly different result for each run, we ran the algorithm five times for each model and averaged the outputs.
Parameter values for two Logistic and two Poisson simulated models
Model  θ_{0},θ_{1},⋯,θ_{15} 

81 (Logistic)  [0.72, 0.25, 0.11, 0.91, 0.47, 0.63, 0.80, 0.86 
0.22, 0.19, 0.82, 0.31, 0.33, 0.12, 0.10, 0.41]  
82 (Logistic)  [0.50, 0.10, 0.18, 0.48, 0.74, 0.63, 0.96, 0.90 
0.36, 0.03, 0.93, 0.21, 0.84, 0.30, 0.67, 0.97]  
91 (Poisson)  [0.54, 2.70, 0.37, 1.60, 2.47, 2.44, 2.42, 0.23 
0.29, 3.00, 2.03, 1.26, 2.04, 1.86, 2.79, 0.21]  
92 (Poisson)  [0.17, 1.01, 0.88, 2.53, 0.34, 2.01, 1.23, 2.04 
0.82, 0.96, 1.26, 2.81, 0.17, 1.39, 1.64, 1.55] 
Average criterion values of the locally Doptimal designs found by genetic algorithm (GA), particle swarm optimization (PSO) and competitive swarm optimizer (CSO) for the 5factor models, along with their standard deviations in parentheses
A CSO generated 21 point design for model 92 with at least 99% Defficiency
x _{1}  x _{2}  x _{3}  x _{4}  x _{5}  w 

1.000  1.000  1.000  1.000  1.000  0.049 
1.000  1.000  1.000  1.000  1.000  0.049 
1.000  0.259  1.000  1.000  1.000  0.051 
1.000  0.608  1.000  1.000  1.000  0.046 
1.000  1.000  1.000  1.000  1.000  0.053 
1.000  1.000  1.000  1.000  0.296  0.046 
1.000  1.000  1.000  1.000  1.000  0.043 
1.000  1.000  1.000  1.000  1.000  0.049 
0.827  1.000  1.000  1.000  1.000  0.046 
0.239  1.000  1.000  1.000  1.000  0.052 
0.673  1.000  1.000  1.000  1.000  0.036 
0.746  1.000  1.000  1.000  1.000  0.047 
1.000  1.000  1.000  1.000  1.000  0.054 
1.000  1.000  1.000  1.000  1.000  0.043 
1.000  1.000  1.000  1.000  1.000  0.053 
1.000  1.000  1.000  1.000  1.000  0.037 
1.000  1.000  1.000  1.000  1.000  0.054 
1.000  1.000  1.000  1.000  1.000  0.050 
1.000  1.000  1.000  1.000  1.000  0.042 
1.000  1.000  1.000  1.000  1.000  0.052 
1.000  1.000  1.000  1.000  1.000  0.048 
We verify the optimality of this and other designs by checking the values of sensitivity function over a userselected discretized grid set in the design space. Clearly for models with several explanatory factors, the finer the grid set, the longer time it takes to check optimality. It is helpful to start with some initial testings with a rough grid to determine whether there are violations of the equivalence theorem; if there are, this suggests the design is not optimal and another should be generated. For this particular example, after initial testings, we discretized the search space with a total of (2/0.2+1)^{5}=161051 grid points for this 5factor model, i.e. a step size of 0.2 for each of the factor spaces. We then plot the multidimensional sensitivity function over the grid set, which is now much harder to visualize and appreciate its properties than the case when there is only one explanatory factor. One option is to stretch the highdimensional grid into a onedimensional vector on the xaxis and plot the sensitivity function values along the xaxis.
A realworld application on car refueling experiment
Variables in the car refueling experiment
Variable  Notation  Type  Range 

Ring type  x _{1}  Binary  1 or 1 
Lightning  x _{2}  Binary  1 or 1 
Sharpening  x _{3}  Binary  1 or 1 
Smoothing  x _{4}  Binary  1 or 1 
Lightning Angle  x _{5}  Continuous  [50, 90] 
Gascap Angle 1  x _{6}  Continuous  [30, 55] 
Gascap Angle 2  x _{7}  Continuous  [0, 10] 
Can Distance  x _{8}  Continuous  [18, 48] 
Reflective Ring Thickness  x _{9}  Continuous  [0.125, 0.425] 
Threshold Step Value  x _{10}  Continuous  [5, 15] 
PInteraction 1  x _{1} x _{9}     
PInteraction 2  x _{2} x _{5}     
PInteraction 3  x _{4} x _{8}     
TInteraction 1  x _{6} x _{7} x _{8}     
TInteraction 2  x _{3} x _{4} x _{10}     
The 12point locally Doptimal design for the additive 10factor car refueling experiment
x _{1}  x _{2}  x _{3}  x _{4}  x _{5}  x _{6}  x _{7}  x _{8}  x _{9}  x _{10}  w 

1  1  1  1  50.000  30.000  4.200  48.000  0.125  5.000  0.091 
1  1  1  1  50.000  30.000  10.000  48.000  0.125  8.570  0.091 
1  1  1  1  50.000  30.000  10.000  45.680  0.125  5.000  0.091 
1  1  1  1  54.640  30.000  10.000  48.000  0.125  5.000  0.091 
1  1  1  1  50.000  32.900  10.000  48.000  0.125  5.000  0.091 
1  1  1  1  50.000  30.000  10.000  48.000  0.125  5.000  0.081 
1  1  1  1  50.000  30.000  10.000  48.000  0.425  5.000  0.077 
1  1  1  1  50.000  30.000  10.000  48.000  0.125  5.000  0.091 
1  1  1  1  50.000  30.000  10.000  48.000  0.125  5.000  0.091 
1  1  1  1  50.000  30.000  10.000  48.000  0.125  5.000  0.091 
1  1  1  1  50.000  30.000  10.000  48.000  0.125  5.000  0.075 
1  1  1  1  50.000  30.000  10.000  48.000  0.425  5.000  0.004 
The 17point locally Doptimal design for the 10factor car refueling experiment with two and three factor interactions
x _{1}  x _{2}  x _{3}  x _{4}  x _{5}  x _{6}  x _{7}  x _{8}  x _{9}  x _{10}  w 

1  1  1  1  50.000  30.000  0.026  31.494  0.125  5.000  0.062 
1  1  1  1  90.000  30.000  0.285  18.000  0.425  5.000  0.063 
1  1.  1  1  90.000  37.342  0.000  47.999  0.425  15.000  0.061 
1  1  1  1  68.511  55.000  0.209  29.239  0.425  15.000  0.062 
1  1  1  1  90.000  30.000  0.085  28.026  0.125  15.000  0.062 
1  1  1  1  90.000  31.591  0.000  34.269  0.425  5.000  0.062 
1  1  1  1  50.000  55.000  0.000  33.014  0.125  5.000  0.062 
1  1  1  1  50.000  36.649  0.000  48.000  0.425  15.000  0.061 
1  1  1  1  90.000  55.000  0.025  48.000  0.425  5.000  0.062 
1.  1  1  1  90.000  55.000  0.091  36.073  0.125  15.000  0.061 
1  1  1  1  75.860  30.000  0.363  18.000  0.125  15.000  0.063 
1.  1  1  1  50.000  55.000  0.007  36.516  0.125  15.000  0.062 
1  1  1  1  90.000  30.000  0.029  38.137  0.425  15.000  0.020 
1  1  1  1  90.000  30.000  0.000  45.986  0.125  5.000  0.060 
1  1  1  1  50.000  30.000  0.000  34.471  0.125  15.000  0.057 
1  1  1  1  67.477  30.000  0.070  48.000  0.125  15.000  0.063 
1  1  1  1  50.000  30.000  0.011  18.361  0.425  15.000  0.056 
Bayesian optimal designs for biomedical studies
Bayesian optimal designs incorporate prior knowledge of the model parameters at the design stage. The prior knowledge usually comes in the form of a probability density function for the parameters and is averaged out by numerical integration before an optimization scheme is applied to find the optimal design. Because the integration and optimization spaces can be very different objects, each with varying magnitude, finding a Bayesian optimal design in a high dimensional problem can be very challenging. Here, we show that PSO is a promising tool for finding Bayesian Doptimal designs for Exponential models which are commonly used in pharmacokinetic/pharmacodynamic studies.
In HIV studies, Exponential models are frequently used to characterize viral load changes with time after administration of a potent inhibitor of HIV1 protease in vivo (Perelson et al. 1996). Derived from a series of ordinary differential equations that describes the virus change in different compartments, the model is a good representation of longitudinal HIV dynamics. The important parameters in such a model include virus clearance rate and infected cell life span (Wu and Ding 1999). Some analytic locally optimal designs and Bayesian optimal designs are available for the Exponential models (Han and Chaloner 2003; Dette and Neugebauer 1997).
Here Y_{j} is the viral load at time t_{j} and the sampling times are t_{j}∈[t_{min},t_{max}]⊆ [0,60]. Both t_{min} and t_{max} are preselected and refer, respectively, to the minimum and maximum time where observations can be taken. We have ε_{j}∼iidN(0,σ^{2}) and the model parameters we wish to estimate are P_{0}, P_{1} and δ (all > 0). Following (Han and Chaloner 2003), the prior densities for P_{0}, P_{1} are both uniform [0.5, 1.5], and for δ is uniform [0.9, 1.1]. Additionally we use a different specification for model 12 with the usual P_{0} and P_{1} but with δ ∼ uniform [0, 0.2]. We call the former specification model 121 and the latter model 122 and try to compare the properties of Bayesian optimal designs under different specifications. The priors can be quite flexible and they do not have to be independent. The above three models are simplified Exponential regression models with two to three parameters, and one can easily extend to the models with more parameters based on the disease stages. Specifically, model 12 describes the trajectory of plasma HIV RNA level under antiviral treatment (Wu and Ding 1999); model 10 and 11 are special cases when P_{0} is treated as a nuisance parameter.
Discussion
Constructing efficient designs is critical to best use the data to reach reliable estimations. One potential issue in constructing highdimensional or Bayesian designs is that the computational time will increase when the model becomes large and the number of the design points increases. Even for a simple linear additive model with 20 factors and 21 design points, the dimension of the optimal design problem is 20×21+20=440. To solve this constrained optimization problem, PSO has to optimize 440 variables. Consequently there is high computational costs. Fortunately, parallel computing techniques can be applied to accelerate the computations. Hung and Wang (2012) proposed a GPUaccelerated PSO (GPSO) algorithm by using a thread pool model and implemented GPSO on a Graphic Processing Unit (GPU). The authors demonstrated that the proposed GPSO can significantly reduce the computational burden with satisfactory parallel efficiency. Likewise, (Chen et al. 2013) proposed a discrete PSO approach, named LaPSO, to search for an optimal Latin hypercube design. The authors accelerated LaPSO by using GPU and showed that the GPU implementation can save computational time significantly for large optimization problems. We expect that the programs in this article can also be accelerated on parallel computers such as GPU. The parallelization will likely produce computing tools with faster response time and better user experience.

we have experiences with metaheuristic algorithms that work well for some nominal values for a model but not for other nominal values; similarly, the same algorithm may not work well when the design space is changed, and especially when it is enlarged. These are likely scaling problems that our current work is trying to address and understand.

confirming optimality of a generated design remains a challenge because it is difficult to appreciate interesting features in a high dimensional plot. An alternative is to find its efficiency lower bound, which amounts to solving another highdimensional optimization problem to find the maximal value of the sensitivity function. This means that the metaheuristic algorithm has to be applied twice or find another more appropriate metaheuristic algorithm to solve this second optimization problem.

as the number of explanatory factors in the model increases, so is the number of variables we need to optimize. For example, in the 10factor carrefueling example, CSO fails to find a locally Doptimal design when the model includes all twofactor interaction terms and some threefactor interaction terms. Currently, the best design found by CSO seems to have a Defficiency of about 82%. Clearly, some further enhancements of the metaheuristic algorithms may be needed. Hybridization to combine one or two more algorithms with CSO may also improve performance.
Summary
Our main contributions in this paper are 1) use PSO and its variants to find optimal designs for highdimensional and Bayesian models; and 2) the creation of online tools for practitioners to generate different types of tailor made optimal designs for their problems. Web based tools can be very valuable to help practitioners make informed decisions on the study design. For instance, a successful website is the one housed in Houston at https://biostatistics.mdanderson.org/SoftwareDownload/, where an array of software is available for download to find many types of adaptive Bayesian designs for Phase I and II trials. It has more than 17,000 downloads to date indicating a high demand of such tools in practice. Some of our PSO codes for stand alone programs are in MATLAB, where the user can download to make changes, when necessary, for their problems. This website allows practitioners to compare designs and arrive at an informed decision on the choice of the design to implement.
We have applied PSO and its variants to search for Bayesian optimal designs and highdimensional models. These are challenging tasks as they involve scaling problems and multiple integration over different types of parameter spaces. While such algorithms do not perform integration per se, they can be cleverly hybridized with effective tools for integration purposes to find these hard to find Bayesian optimal designs. Our current work includes hybridizing PSO or its variants with sparse grid algorithm and results are promising.
We close with a cautionary note that an optimal design should not be used religiously but should serve as a guide or benchmark. This is because the optimal design is found under a fixed set of assumptions that may not adequately reflect reality and so may not satisfy the needs of the practitioners. Different optimal designs under various settings should be compared carefully before the design is implemented. The guiding principle is that the implemented design should not stray too far from the optimum as measured by its efficiency relative to the optimum. PSO facilitates search for an efficient design, calculates an efficiency lower bound and compares competing designs. Our hope is that the practitioners are more informed of such algorithms and the availability of them on websites will help them implement more efficient designs.
Declarations
Acknowledgement
The authors would like to thank Dr. RayBing Chen and Dr. Weichung Wang for their support in maintaining the website.
Funding
The research in this publication were partially supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number R01GM107639. The funding did not influence the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Availability of data and materials
Not applicable.
Authors’ contributions
YS wrote the manuscript and provided PSOgenerated designs, ZZ applied CSO and generated optimal designs for highdimensional models and WKW supervised and edited the whole manuscript. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
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Authors’ Affiliations
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