Scalarizing Functions in Bayesian Multiobjective Optimization

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordScalarizing functions have been widely used to convert a multiobjective optimization problem into a single objective optimization problem. However, their use in solving (computationally) expensive multi- and many-objective optimization problems in Bayesian multiobjective optimization is scarce. Scalarizing functions can play a crucial role on the quality and number of evaluations required when doing the optimization. In this article, we study and review 15 different scalarizing functions in the framework of Bayesian multiobjective optimization and build Gaussian process models (as surrogates, metamodels or emulators) on them. We use expected improvement as infill criterion (or acquisition function) to update the models. In particular, we compare different scalarizing functions and analyze their performance on several benchmark problems with different number of objectives to be optimized. The review and experiments on different functions provide useful insights when using and selecting a scalarizing function when using a Bayesian multiobjective optimization method.Natural Environment Research Council (NERC)Youth and Sports of the Czech Republi

What is the real impact of acute kidney injury?

Background: Acute kidney injury (AKI) is a common clinical problem. Studies have documented the incidence of AKI in a variety of populations but to date we do not believe the real incidence of AKI has been accurately documented in a district general hospital setting. The aim here was to describe the detected incidence of AKI in a typical general hospital setting in an unselected population, and describe associated short and long-term outcomes. Methods: A retrospective observational database study from secondary care in East Kent (adult catchment population of 582,300). All adult patients (18 years or over) admitted between 1st February 2009 and 31st July 2009, were included. Patients receiving chronic renal replacement therapy (RRT), maternity and day case admissions were excluded. AKI was defined by the acute kidney injury network (AKIN) criteria. A time dependent risk analysis with logistic regression and Cox regression was used for the analysis of in-hospital mortality and survival. Results: The incidence of AKI in the 6 month period was 15,325 pmp/yr (adults) (69% AKIN1, 18% AKIN2 and 13% AKIN3). In-hospital mortality, length of stay and ITU utilisation all increased with severity of AKI. Patients with AKI had an increase in care on discharge and an increase in hospital readmission within 30 days. Conclusions: This data comes closer to the real incidence and outcomes of AKI managed in-hospital than any study published in the literature to date. Fifteen percent of all admissions sustained an episode of AKI with increased subsequent short and long term morbidity and mortality, even in those with AKIN1. This confers an increased burden and cost to the healthcare economy, which can now be quantified. These results will furnish a baseline for quality improvement projects aimed at early identification, improved management, and where possible prevention, of AKI

Energy efficient global optimisation of reactive dividing wall distillation column

This is the author accepted manuscript. The final version is avialable from Taylor & Francis via the DOI in this recordAn optimisation problem to minimise energy requirements in the synthesis of bio-additive ethyl tertiary butyl ether (ETBE) via reactive dividing wall distillation column (RDWC) is considered. The contribution of the article is to solve a real-world optimisation problem by addressing two challenges: (i) finding optimal process conditions in few numbers of simulations and (ii) handling mixed-integer variables. An efficient global optimisation algorithm is used to find optimal process conditions and adapted to handle both integer and continuous variables. ETBE is produced by the reaction of ethanol and isobutene in RDWC and has proven its niche in reducing the energy requirements for reaction–separation processes. However, the overall economics of the process is governed by the energy requirements. Therefore, it is crucial to find the optimal process conditions for achieving a cost-effective process. Reboiler duty of RDWC, considered as a measure of the energy requirements to be minimised by using the algorithm. Seven variables (four integers and three continuous) are used in the optimisation process to minimise the reboiler duty. A very low value of reboiler duty is obtained after doing the optimisation, which not only provides insight when using RDWC but also shows the potential of the algorithm used.Natural Environment Research Council (NERC

Convergence and stability theorems for the Picard-Mann hybrid iterative scheme for a general class of contractive-like operators

In this paper we use the general class of contractive-like operators introduced by Bosede and Rhoades (J. Adv. Math. Stud. 3(2):1-3, 2010) to prove strong convergence and stability results for Picard-Mann hybrid iterative schemes considered in a real normed linear space. We establish the strong convergence and stability of the Picard iterative scheme as a corollary. Our results generalize and improve a multitude of results in the literature, including the recent results of Chidume (Fixed Point Theory Appl. 2014:233, 2014)

A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approximation-based algorithms. We also compare these algorithms based on different criteria such as metamodeling technique and evolutionary algorithm used, type and dimensions of the problem solved, handling constraints, training time and the type of evolution control. Furthermore, we identify and discuss some promising elements and major issues among algorithms in the literature related to using an approximation and numerical settings used. In addition, we discuss selecting an algorithm to solve a given computationally expensive multiobjective optimization problem based on the dimensions in both objective and decision spaces and the computation budget available.The research of Tinkle Chugh was funded by the COMAS Doctoral Program (at the University of Jyväskylä) and FiDiPro Project DeCoMo (funded by Tekes, the Finnish Funding Agency for Innovation), and the research of Dr. Karthik Sindhya was funded by SIMPRO project funded by Tekes as well as DeCoMo

Rapidity distribution as a probe for elliptical flow at intermediate energies

Interplay between the spectator and participant matter in heavy-ion collisions is investigated within isospin dependent quantum molecular dynamics (IQMD) model in term of rapidity distribution of light charged particles. The effect of different types and size rapidity distributions is studied in elliptical flow. The elliptical flow patterns show important role of the nearby spectator matter on the participant zone. This role is further explained on the basis of passing time of the spectator and expansion time of the participant zone. The transition from the in-plane to out-of-plane is observed only when the mid-rapidity region is included in the rapidity bin, otherwise no transition occurs. The transition energy is found to be highly sensitive towards the size of the rapidity bin, while weakly on the type of the rapidity distribution. The theoretical results are also compared with the experimental findings and are found in good agreement.Comment: 8 figure

In vitro induction and proliferation of protocorm-like bodies (PLBs) from leaf segments of Phalaenopsis bellina (Rchb.f.) Christenson

An in vitro culture procedure was established to induce protocorm-like bodies (PLBs) from leaf segments of the Phalaenopsis bellina (Rchb.f.) Christenson directly from epidermal cells without intervening callus on ½ strength modified Murashige and Skoog (MS) (in Physiol Plant 15:473–497, 1962) medium supplemented with 1-Naphthaleneacetic acid (NAA; 0, 0.1, 1 mg/l) and Thidiazuron (TDZ; 0, 0.1, 1, 3 mg/l). The best response was established at 3 mg/l TDZ which induced 78% of leaf segments to form a mean number of 14 PLBs per explant after 16 weeks of culture. No PLBs were found when leaf segments were cultured on ½ strength modified MS media supplemented with 0.1 and 1 mg/l NAA. The best induction percentage for auxin: cytokinin combination was at the combination of NAA and TDZ at 1.0 and 3.0 mg/l which gave 72% induction with 9 PLBs per explant. Semi-solid ½ strength MS and liquid Vacin and Went (VW) (in Bot Gaz 110:605–613, 1949) medium were used in order to find the highest survival and number of PLBs proliferation after 3 months in culture. Half strength MS showed an average of 9 PLBs in comparison with VW with an average of 5.3 PLBs per explants. Histological observations revealed that the regenerated PLBs were generally formed from the epidermal layers of the posterior regions of the leaf segments. Scanning electron micrograph of PLBs showed the origin of newly formed PLB from the peripheral region of leaf segments

Maximising hypervolume and minimising ϵ-indicators using Bayesian optimisation over sets

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordBayesian optimisation methods have been widely used to solve problems with computationally expensive objective functions. In the multi-objective case, these methods have been successfully applied to maximise the expected hypervolume improvement of individual solutions. However, the hypervolume, and other unary quality indicators such as multiplicative -indicator, measure the quality of an approximation set and the overall goal is to find the set with the best indicator value. Unfortunately, the literature on Bayesian optimisation over sets is scarce. This work uses a recent set-based kernel in Gaussian processes and applies it to maximise hypervolume and minimise -indicators in Bayesian optimisation over sets. The results on benchmark problems show that maximising hypervolume using Bayesian optimisation over sets gives a similar performance than non-set based methods. The performance of using indicator in Bayesian optimisation over sets needs to be investigated further. The set-based method is computationally more expensive than the non-set-based ones, but the overall time may be still negligible in practice compared to the expensive objective functions.Ministry of Science and Innovation of the Spanish Governmen