SystemPerformanceOptimizer Class

public sealed class SystemPerformanceOptimizer : CrossEntropyProgram

Public NotInheritable Class SystemPerformanceOptimizer
	Inherits CrossEntropyProgram

public ref class SystemPerformanceOptimizer sealed : public CrossEntropyProgram

[<SealedAttribute>]
type SystemPerformanceOptimizer = 
    class
        inherit CrossEntropyProgram
    end

Inheritance

Object    CrossEntropyProgram    SystemPerformanceOptimizer

The Cross-Entropy method for combinatorial and multi-extremal optimization

The current implementation of the SystemPerformanceOptimizer class is based on the main Cross-Entropy program for optimization proposed by Rubinstein and Kroese (Algorithm 4.2.1)^[1].

The Cross-Entropy method raises as a solution to the problem of estimating the probability of rare events regarding the performance of complex systems. However, soon the method was recognized as closely related to the optimization of continuous or discrete functions. In fact, optimizing a function is a task that can be accomplished by exploiting the Cross-Entropy machinery aimed to estimate the probability of rare events, since the event represented by a function assuming a value over a given extreme threshold can be typically interpreted as rare.

The Cross-Entropy algorithm for optimization is an adapted version of that for rare event simulation. As the latter, it is an iterative multi step procedure, having two main steps. In the sampling step, the state-space of the system (the domain of the performance function , ) is traversed looking for good candidate points at which global performance extrema can be attained. In the subsequent updating step, the distribution from which the states are sampled is modified in order to increase, in the next iterations, the probability of sampling near the true optimizer, say .

Sampling step

At each iteration , a sampling step is executed to generate states of the system which are interpreted as candidate arguments of the performance function. It is assumed that such sample is drawn from a specific density or probability function, member of a parametric family



where is a vector representing a possible state of the system, and is the set of allowable values for parameter . The parameter exploited at iteration is referred to as the reference parameter at and indicated as .

The parametric family must be carefully chosen. In particular, the first reference parameter, , must be such that no relevant subsets of the state-space will receive a too low probability: when starting the search for an optimizer, all possible states must have a real chance of being selected. If there is no a-priori knowledge about the position of the optimizer inside the state-space, it would be very useful if the parametric family could include a parameter such that the corresponding distribution were - at least approximately - uniform on the system's state-space. That parameter would be the perfect candidate for .

Furthermore, it is expected that, given a reference parameter , a corresponding reasonable value could be guessed for the optimal argument, say . In this way, by modifying appropriately the reference parameter, one can suppose to better foresee the optimal argument. Finally, given the optimal reference parameter, say (the parameter corresponding to the last iteration executed by the algorithm before stopping), it will be possible to select via the best argument at which the searched extremum can be considered as reached according to the Cross-Entropy method.

Updating step

Given the sample drawn at iteration from distribution , the main idea behind the Cross-Entropy updating step is that it must be designed to improve the concentration of the probability mass near . To obtain such result, the updating procedure is based on the sampled points whose performances are more relevant given the optimization goal of the algorithm: if one is maximizing the performance function, then the updating step relies on the states having the highest observed performances; otherwise, the update of the reference parameter is supported by those sampled states corresponding to the lowest performances.

Let be the sampled state such that , , and consider a constant, referred to as the rarity of the program, say , set to a value in the interval .

If the optimization goal is a maximization, then the points occupying a position greater than or equal to are the elite sampled states, with being the ceiling function. In this case, the current iteration is said to reach a performance level equal to .

Otherwise, if the optimization goal is a minimization, then the elite sampled states can be defined as those points occupying a position less than or equal to , where is the floor function. Such points are thus , and the current iteration is said to reach a performance level equal to .

The joint distribution from which the states will be sampled in the next iteration is updated exploiting the elite states only: in this way, given that the performances of the elite points are likely to be similar to that at , it can be expected that such choice will push towards .

In addition, to achieve the convergence of the algorithm, the mass also needs to accumulate near . Such result can be obtained by estimating via the Cross-Entropy method the probability of a rare event: in case of maximization, the event of interest being



or, if must be minimized,

The target probability can thus be represented as



where is or , and is the indicator function of .

The Cross-Entropy method selects another density in by identifying a parameter that guarantees the most accurate estimate of the target probability for a specified simulation effort, or sample size , by taking into account the Likelihood estimator based on the ratio



where



is the density of conditional on .

Such estimator is unusable, since it depends on the target probability. However, it is ideally optimal because its variance is equal to zero. For this reason, it is proposed to select the parameter such that the corresponding density is the closest to the optimal one, the difference between two densities, say and , being measured as a Kullback–Leibler divergence:



The selection is thus obtained by minimizing the divergence between the generic density in , say , and the ideal density :

Such minimization corresponds to the maximization



where is the expectation operator under . This implies that, given a sample from , a solution to such optimization can be estimated through the solution to the program:



where



and



The interest in such an approach is due to the possibility of choosing the new parameter under which the event becomes less rare under density than under density : the mass concentration becomes higher. In this way, by decreasing the rareness of , a smaller simulation effort would be required in order to obtain a good estimation, since a higher mass concentration is attained. The iteration parameter is thus chosen to be the optimal one in for estimating :



and can be interpreted as the Maximum Likelihood estimate of based only on the elite sample points.

By default, the algorithm stops iterating when the iteration level does not change for a predefined number of iterations, or the number of executed iterations reaches a specified maximum.

Executing a Cross-Entropy optimizer

Class SystemPerformanceOptimizer follows the Template Method pattern^[2] by defining in an operation the structure of a Cross-Entropy algorithm aimed to optimize the performance of a system. Optimize is the template method, in which the invariant parts of a Cross-Entropy optimizer are implemented once, deferring the implementation of behaviors that can vary to a SystemPerformanceOptimizationContext instance, that method Optimize receives as a parameter.

SystemPerformanceOptimizationContext inherits or defines abstract or virtual methods which represent some primitive operations of the template method, i.e. those varying steps that its subclasses will override to define the concrete behavior of the algorithm. See the documentation of class SystemPerformanceOptimizationContext for examples of how to implement a context for system's performance optimization.

Reference