Matlab optimization toolbox ubc9/15/2023 ![]() ![]() In each candidate search, unlike most prior related studies, RECAS employs a surrogate model in an aggregated manner to directly approximate the acquisition function that maps each point to its quality assessment indicator rather than a certain objective. ![]() The proposed algorithm is a non-evolutionary-based method and iteratively determines new points for expensive evaluation via a series of independent reference vector-guided candidate searches. In this study, we present a novel surrogate-assisted many-objective optimization algorithm named RECAS. (Arts Building room 386)Ĭhristine Shoemaker: Surrogate-assisted many-objective optimization with reference vector guided candidate search and aggregated surrogate model ↓ĭesigning many-objective optimization algorithms that cope with more than three objectives is challenging, especially when objective functions are multimodal and expensive to evaluate. Further, weĪlso present preliminary numerical results which show viability of the proposed method. We propose an algorithm model and study its theoretical convergence properties. In this talk, we approach the problem with hard constraints by means of an interior log-barrier penaltyįunction. However, such an approach frequently causes numerical difficulties whichĪre basically due to the discontinuity of the objective function on the boundary of the feasible region. In this way, if a feasible starting point is known, the algorithm remains trapped In such situations, it is customary to use an extreme or dead penaltyĪpproach in which an extremely high value is assigned to the objective function out side of theįeasible region. Frequently in such applications,Ĭonstraints are hard in the sense that functions cannot be computed (or they could not even be defined) Problems mainly arise in those contexts where the objective and constraint functions are computedīy means of relatively complex and time-consuming simulators. Typically, first order derivatives cannot be used or are, at the very least, untrustworthy. no analytical expression of the functions is available. ![]() Problems is characterized by objective and/or constraint functions that are only known through Nowadays, black-box optimization problems are ubiquitous in many applications. Giampaolo Liuzzi: A new derivative-free interior point method for constrained black-box optimization ↓ Finally, we will introduce a new measureįor quantifying the quality of a PkSS, inspired by the cosine measure for positive spanning sets. We will then explain how to construct PkSS of minimum cardinality, using arguments from polytope theory. After formally defining the positive k-spanning property, we will provide examples of PkSSs. A positive k-spanning set (PkSS) remains positively spanning even when some of its elements are removed. In this talk, we will focus on a particular subclass of PSSs, called positive k-spanning sets. In particular, it does not reflect the spanning capabilities of a given subset of the PSS vectors. A good cosine measure implies that the directions in the PSS can be useful for optimization purposes, however this metric does not fully account for the structure of the PSS. Derivative-free optimization methods based on PSSs typically favor those with the best cosine measure. Gabriel Jarry-Bolduc: A derivative-free trust region algorithm using calculus rules to build the model function ↓Ī positive spanning set (PSS) is a set of vectors that spans the whole space using non-negative linear combinations. Different strategies are tested on a some analytical test problems, and on a real hydroelectric dam optimization problem. We introduce a trend matrix and a trend direction to guide the Mesh Adaptive Direct Search (Mads) algorithm when optimizing a monotonic grey box optimization problem. With this goal in mind, we have built a theoretical foundation through a thorough study of monotonicity on cones of multivariate functions. ![]() Our objective is to develop an algorithmic mechanism that exploits this monotonic information to find a feasible solution as quickly as possible. We refer to this type of problems as ``monotonic grey box'' optimization problems. That is, when increasing a variable, the user is able to predict if a function increases or decreases, but is unable to quantify the amount by which it varies. We are interested in blackbox optimization for which the user is aware of monotonic behaviour of some constraints defining the problem. ![]()
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