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

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

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

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

The anaerobic bacteriology of intrapulmonary infections in Kuwait

Objective: The primary objective of this study was to ascertain the association of anaerobic bacteria in intrapulmonary infections and their susceptibility pattern to commonly prescribed antibiotics. Methods: One hundred clinical samples (85 broncho-alveolar lavage and 15 lung abscess aspirates) from suspected intrapulmonary infection cases were investigated in order to determine the role of anaerobic bacteria in these infections. The anaerobic bacterial isolates were identified by using the Vitek Anaerobic Card System and conventional methods. Susceptibility of these isolates was determined by Etest method against eight commonly prescribed antibiotics. Results: A total of 42 anaerobes were isolated, of which Prevotella spp. were the commonest isolates, made up of 42.9% (18/42), followed by Peptostreptococcus spp. 33.3% (14/42). Only two Bacteroides fragilis strains were isolated. All the isolates were sensitive to metronidazole, clindamycin, imipenem and meropenem; however, one Prevotella was resistant to piperacillin-tazobactam. The two B. fragilis isolates were susceptible to metronidazole, imipenem, meropenem and piperacillin-tazobactam, and one was found to be resistant to clindamycin. Conclusion: Overall, Prevotella spp. were found to be the predominant anaerobic bacteria associated with intrapulmonary infections in Kuwait. All the commonly prescribed antibiotics had excellent in vitro activities against nearly all the isolates

Probabilistic Selection Approaches in Decomposition-based Evolutionary Algorithms for Offline Data-Driven Multiobjective Optimization

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordIn offline data-driven multiobjective optimization, no new data is available during the optimization process. Approximation models, also known as surrogates, are built using the provided offline data. A multiobjective evolutionary algorithm can be utilized to find solutions by using these surrogates. The accuracy of the approximated solutions depends on the surrogates and approximations typically involve uncertainties. In this paper, we propose probabilistic selection approaches that utilize the uncertainty information of the Kriging models (as surrogates) to improve the solution process in offline data-driven multiobjective optimization. These approaches are designed for decomposition-based multiobjective evolutionary algorithms and can, thus, handle a large number of objectives. The proposed approaches were tested on distance-based visualizable test problems and the DTLZ suite. The proposed approaches produced solutions with a greater hypervolume, and a lower root mean squared error compared to generic approaches and a transfer learning approach that do not use uncertainty information

Optimal management of mixed hydraulic barriers in coastal aquifers using multi-objective Bayesian optimization

This is the final version. Available on open access from Elsevier via the DOI in this recordMixed hydraulic barriers is an effective method to control seawater intrusion (SWI), particularly in regions that suffer from water shortages. However, determining the optimal well locations and rates for injection and abstraction is challenging due to the computational burden resulting from the huge number of calls for the high-fidelity hydrogeological simulation model. To alleviate this issue, we utilized a constrained multi-objective Bayesian optimization (BO) approach to optimize rates and locations of the hydraulic barriers to minimize total cost, aquifer salinity, and salt-wedge intrusion length, while satisfying regional abstractions with acceptable salinity levels. BO is useful for optimizing computationally expensive problems in few iterations by using a surrogate model and an acquisition function. Despite being an efficient optimization tool, the use of BO in the field of coastal aquifer management has not been explored. The proposed framework was evaluated on an unconfined aquifer subjected to three management scenarios considering different physical and technical constraints and was benchmarked against the widely used robust NSGA-II (Non-dominated Sorting Genetic Algorithm II) method. The results proved the effectiveness of BO in achieving an optimum mixed hydraulic barriers design in much fewer runs of the variable density aquifer model. BO with 350 evaluations yielded comparable results to 4150 evaluations using NSGA-II. BO solutions were spatially well-distributed along the approximated Pareto front. For the same number of evaluations, the hypervolume obtained by BO was larger by 30%. Based on different scenarios, the average amount of water required for abstraction ranged from 1.5% to 25% of that for injection. The injection has a significant impact on SWI management, but the abstracted water provides an alternative source of water. A sensitivity analysis was conducted on the optimization problem to illustrate its efficiency by omitting the barriers one at a time and assessing impacts on objective and constraint functions.Ministry of Higher Education of the Arab Republic of Egyp

Does intellectual capital and corporate governance have an impact on annual report readability? Evidence from an emerging market

Purpose: This study examines the impact of intellectual capital efficiency and corporate governance mechanisms on the annual report readability of Oman's financial sector companies. Design/methodology/approach: The study uses a sample of 150 firm-year observations of listed financial sector companies in the Muscat Securities Market, Oman, from 2014 to 2018. Flesch Reading ease and Flesch Kinkaid Index are used as proxies for annual report readability. As part of sensitivity analysis, the study also uses the natural logarithm of annual report pages as alternative readability measures. The investigation is conducted using random effects regression analysis and supported with system GMM estimation for robustness. Findings: The findings of this study demonstrate a decrease in intellectual capital efficiency associated with better readability of annual reports for the financial sector firms. Alternatively, banks report a positive association of intellectual capital efficiency with the Flesch Reading ease score of the annual report. The structural capital and capital employed efficiency are also found to be negatively associated with annual report readability. Corporate governance mechanisms such as dispersed ownership and audit committee size also result in easy-to-read annual reports that support agency theory. Research limitations/implications: The research was conducted for financial firms of Oman, and thereby the findings can be generalized to the financial sector of countries with similar settings, such as the Gulf Cooperation Council (GCC) region. Practical implications: The policy implications arising from this study suggest a strengthening of the intellectual capital efficiency and corporate governance mechanisms to improve the readability of the firms and thereby increase investor confidence. Originality/value: This paper's uniqueness is in the model used to investigate the impact of intellectual capital efficiency and corporate governance mechanisms on the annual report readability of an emerging market

Fingerprints Indicating Superior Properties of Internal Interfaces in Cu(In,Ga)Se2 Thin-Film Solar Cells

Growth of Cu(In,Ga)Se2 (CIGS) absorbers under Cu-poor conditions gives rise to incorporation of numerous defects into the bulk, whereas the same absorber grown under Cu-rich conditions leads to a stoichiometric bulk with minimum defects. This suggests that CIGS absorbers grown under Cu-rich conditions are more suitable for solar cell applications. However, the CIGS solar cell devices with record efficiencies have all been fabricated under Cu-poor conditions, despite the expectations. Therefore, in the present work, both Cu-poor and Cu-rich CIGS cells are investigated, and the superior properties of the internal interfaces of the Cu-poor CIGS cells, such as the p-n junction and grain boundaries, which always makes them the record-efficiency devices, are shown. More precisely, by employing a correlative microscopy approach, the typical fingerprints for superior properties of internal interfaces necessary for maintaining a lower recombination activity in the cell is discovered. These are a Cu-depleted and Cd-enriched CIGS absorber surface, near the p-n junction, as well as a negative Cu factor (∆β) and high Na content (>1.5 at%) at the grain boundaries. Thus, this work provides key factors governing the device performance (efficiency), which can be considered in the design of next-generation solar cells

Inflammatory reaction in the retina after focal non-convulsive status epilepticus in mice investigated with high resolution magnetic resonance and diffusion tensor imaging

Pathophysiological consequences of focal non-convulsive status epilepticus (fNCSE) have been difficult to demonstrate in humans. In rats fNCSE pathology has been identified in the eyes. Here we evaluated the use of high-resolution 7 T structural T1-weighted magnetic resonance imaging (MRI) and 9.4 T diffusion tensor imaging (DTI) for detecting hippocampal fNCSE-induced retinal pathology ex vivo in mice. Seven weeks post-fNCSE, increased number of Iba1+ microglia were evident in the retina ipsilateral to the hemisphere with fNCSE, and morphologically more activated microglia were found in both ipsi- and contralateral retina compared to non-stimulated control mice. T1-weighted intensity measurements of the contralateral retina showed a minor increase within the outer nuclear and plexiform layers of the lateral retina. T1-weighted measurements were not performed in the ipsilateral retina due to technical difficulties. DTI fractional anisotropy(FA) values were discretely altered in the lateral part of the ipsilateral retina and unaltered in the contralateral retina. No changes were observed in the distal part of the optic nerve. The sensitivity of both imaging techniques for identifying larger retinal alteration was confirmed ex vivo in retinitis pigmentosa mice where a substantial neurodegeneration of the outer retinal layers is evident. With MR imaging a 50 % decrease in DTI FA values and significantly thinner retina in T1-weighted images were detected. We conclude that retinal pathology after fNCSE in mice is subtle and present bilaterally. High-resolution T1-weighted MRI and DTI independently did not detect the entire pathological retinal changes after fNCSE, but the combination of the two techniques indicated minor patchy structural changes