3 research outputs found
Parallel strategies for Direct Multisearch
Publisher Copyright:
漏 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Direct multisearch (DMS) is a derivative-free optimization class of algorithms, suited for computing approximations to the complete Pareto front of a given multiobjective optimization problem. In DMS class, constraints are addressed with an extreme barrier approach, only evaluating feasible points. It has a well-supported convergence analysis and simple implementations present a good numerical performance, both in academic test sets and in real applications. Recently, this numerical performance was improved with the definition of a search step based on the minimization of quadratic polynomial models, corresponding to the algorithm BoostDMS. In this work, we propose and numerically evaluate strategies to improve the performance of BoostDMS, mainly through parallelization applied to the search and to the poll steps. The final parallelized version not only considerably decreases the computational time required for solving a multiobjective optimization problem, but also increases the quality of the computed approximation to the Pareto front. Extensive numerical results will be reported in an academic test set and in a chemical engineering application.preprintpublishe
Aeroelastic Tailoring of Transport Wings Including Transonic Flutter Constraints
Several minimum-mass optimization problems are solved to evaluate the effectiveness of a variety of novel tailoring schemes for subsonic transport wings. Aeroelastic stress and panel buckling constraints are imposed across several trimmed static maneuver loads, in addition to a transonic flutter margin constraint, captured with aerodynamic influence coefficient-based tools. Tailoring with metallic thickness variations, functionally graded materials, balanced or unbalanced composite laminates, curvilinear tow steering, and distributed trailing edge control effectors are all found to provide reductions in structural wing mass with varying degrees of success. The question as to whether this wing mass reduction will offset the increased manufacturing cost is left unresolved for each case
Multicriteria Optimization Techniques for Understanding the Case Mix Landscape of a Hospital
Various medical and surgical units operate in a typical hospital and to treat
their patients these units compete for infrastructure like operating rooms (OR)
and ward beds. How that competition is regulated affects the capacity and
output of a hospital. This article considers the impact of treating different
patient case mix (PCM) in a hospital. As each case mix has an economic
consequence and a unique profile of hospital resource usage, this consideration
is important. To better understand the case mix landscape and to identify those
which are optimal from a capacity utilisation perspective, an improved
multicriteria optimization (MCO) approach is proposed. As there are many
patient types in a typical hospital, the task of generating an archive of
non-dominated (i.e., Pareto optimal) case mix is computationally challenging.
To generate a better archive, an improved parallelised epsilon constraint
method (ECM) is introduced. Our parallel random corrective approach is
significantly faster than prior methods and is not restricted to evaluating
points on a structured uniform mesh. As such we can generate more solutions.
The application of KD-Trees is another new contribution. We use them to perform
proximity testing and to store the high dimensional Pareto frontier (PF). For
generating, viewing, navigating, and querying an archive, the development of a
suitable decision support tool (DST) is proposed and demonstrated.Comment: 38 pages, 17 figures, 11 table