Preference incorporation in MOEA/D using an outranking approach with imprecise model parameters

Multi-objective Optimization Evolutionary Algorithms (MOEAs) face numerous challenges when they are used to solve Many-objective Optimization Problems (MaOPs). Decomposition-based strategies, such as MOEA/D, divide an MaOP into multiple single-optimization sub-problems, achieving better diversity and a better approximation of the Pareto front, and dealing with some of the challenges of MaOPs. However, these approaches still require one to solve a multi-criteria selection problem that will allow a Decision-Maker (DM) to choose the final solution. Incorporating preferences may provide results that are closer to the region of interest of a DM. Most of the proposals to integrate preferences in decomposition-based MOEAs prefer progressive articulation over the “a priori” incorporation of preferences. Progressive articulation methods can hardly work without comparable and transitive preferences, and they can significantly increase the cognitive effort required of a DM. On the other hand, the “a priori” strategies do not demand transitive judgements from the DM but require a direct parameter elicitation that usually is subject to imprecision. Outranking approaches have properties that allow them to suitably handle non-transitive preferences, veto conditions, and incomparability, which are typical characteristics of many real DMs. This paper explores how to incorporate DM preferences into MOEA/D using the “a priori” incorporation of preferences, based on interval outranking relations, to handle imprecision when preference parameters are elicited. Several experiments make it possible to analyze the proposal's performance on benchmark problems and to compare the results with the classic MOEA/D without preference incorporation and with a recent, state-of-the-art preference-based decomposition algorithm. In many instances, our results are closer to the Region of Interest, particularly when the number of objectives increases

An ACO-based Hyper-heuristic for Sequencing Many-objective Evolutionary Algorithms that Consider Different Ways to Incorporate the DM's Preferences

Many-objective optimization is an area of interest common to researchers, professionals, and practitioners because of its real-world implications. Preference incorporation into Multi-Objective Evolutionary Algorithms (MOEAs) is one of the current approaches to treat Many-Objective Optimization Problems (MaOPs). Some recent studies have focused on the advantages of embedding preference models based on interval outranking into MOEAs; several models have been proposed to achieve it. Since there are many factors influencing the choice of the best outranking model, there is no clear notion of which is the best model to incorporate the preferences of the decision maker into a particular problem. This paper proposes a hyper-heuristic algorithm—named HyperACO—that searches for the best combination of several interval outranking models embedded into MOEAs to solve MaOPs. HyperACO is able not only to select the most appropriate model but also to combine the already existing models to solve a specific MaOP correctly. The results obtained on the DTLZ and WFG test suites corroborate that HyperACO can hybridize MOEAs with a combined preference model that is suitable to the problem being solved. Performance comparisons with other state-of-the-art MOEAs and tests for statistical significance validate this conclusion

Epidemiology of intra-abdominal infection and sepsis in critically ill patients: “AbSeS”, a multinational observational cohort study and ESICM Trials Group Project

Purpose: To describe the epidemiology of intra-abdominal infection in an international cohort of ICU patients according to a new system that classifies cases according to setting of infection acquisition (community-acquired, early onset hospital-acquired, and late-onset hospital-acquired), anatomical disruption (absent or present with localized or diffuse peritonitis), and severity of disease expression (infection, sepsis, and septic shock). Methods: We performed a multicenter (n = 309), observational, epidemiological study including adult ICU patients diagnosed with intra-abdominal infection. Risk factors for mortality were assessed by logistic regression analysis. Results: The cohort included 2621 patients. Setting of infection acquisition was community-acquired in 31.6%, early onset hospital-acquired in 25%, and late-onset hospital-acquired in 43.4% of patients. Overall prevalence of antimicrobial resistance was 26.3% and difficult-to-treat resistant Gram-negative bacteria 4.3%, with great variation according to geographic region. No difference in prevalence of antimicrobial resistance was observed according to setting of infection acquisition. Overall mortality was 29.1%. Independent risk factors for mortality included late-onset hospital-acquired infection, diffuse peritonitis, sepsis, septic shock, older age, malnutrition, liver failure, congestive heart failure, antimicrobial resistance (either methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci, extended-spectrum beta-lactamase-producing Gram-negative bacteria, or carbapenem-resistant Gram-negative bacteria) and source control failure evidenced by either the need for surgical revision or persistent inflammation. Conclusion: This multinational, heterogeneous cohort of ICU patients with intra-abdominal infection revealed that setting of infection acquisition, anatomical disruption, and severity of disease expression are disease-specific phenotypic characteristics associated with outcome, irrespective of the type of infection. Antimicrobial resistance is equally common in community-acquired as in hospital-acquired infection

May Measurement Month 2018: a pragmatic global screening campaign to raise awareness of blood pressure by the International Society of Hypertension

Aims Raised blood pressure (BP) is the biggest contributor to mortality and disease burden worldwide and fewer than half of those with hypertension are aware of it. May Measurement Month (MMM) is a global campaign set up in 2017, to raise awareness of high BP and as a pragmatic solution to a lack of formal screening worldwide. The 2018 campaign was expanded, aiming to include more participants and countries. Methods and results Eighty-nine countries participated in MMM 2018. Volunteers (≥18 years) were recruited through opportunistic sampling at a variety of screening sites. Each participant had three BP measurements and completed a questionnaire on demographic, lifestyle, and environmental factors. Hypertension was defined as a systolic BP ≥140 mmHg or diastolic BP ≥90 mmHg, or taking antihypertensive medication. In total, 74.9% of screenees provided three BP readings. Multiple imputation using chained equations was used to impute missing readings. 1 504 963 individuals (mean age 45.3 years; 52.4% female) were screened. After multiple imputation, 502 079 (33.4%) individuals had hypertension, of whom 59.5% were aware of their diagnosis and 55.3% were taking antihypertensive medication. Of those on medication, 60.0% were controlled and of all hypertensives, 33.2% were controlled. We detected 224 285 individuals with untreated hypertension and 111 214 individuals with inadequately treated (systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg) hypertension. Conclusion May Measurement Month expanded significantly compared with 2017, including more participants in more countries. The campaign identified over 335 000 adults with untreated or inadequately treated hypertension. In the absence of systematic screening programmes, MMM was effective at raising awareness at least among these individuals at risk

Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking

In this paper, we enriched Ant Colony Optimization (ACO) with interval outranking to develop a novel multi-objective ACO optimizer to approach problems with many objective functions. This proposal is suitable if the preferences of the Decision Maker (DM) can be modeled through outranking relations. The introduced algorithm (Interval Outranking-based ACO, IO-ACO) is the first ant-colony optimizer that embeds an outranking model to bear vagueness and ill-definition of the DM's preferences. This capacity is the most differentiating feature of IO-ACO because this issue is highly relevant in practice. IO-ACO biases the search towards the Region of Interest (RoI), the privileged zone of the Pareto frontier containing the solutions that better match the DM's preferences. Two widely studied benchmarks were utilized to measure the efficiency of IO-ACO, i.e., the DTLZ and WFG test suites. Accordingly, IO-ACO was compared with four competitive multi-objective optimizers: The Indicator-based Many-Objective ACO, the Multi-objective Evolutionary Algorithm Based on Decomposition, the Reference Vector-Guided Evolutionary Algorithm using Improved Growing Neural Gas, and the Indicator-based Multi-objective Evolutionary Algorithm with Reference Point Adaptation. The numerical results show that IO-ACO approximates the RoI better than leading metaheuristics based on approximating the Pareto frontier alone

Parameters used for KonD mathematical model simulation of variability of [Ca²⁺]_i responses to caffeine for control condition (20 mM caffeine), lower caffeine (2 mM) and inhibited SERCA pump with thapsigargin.

<p>All parameters values were the same as those obtained with 20mM caffeine-induced [Ca²⁺]_i response except where indicated with bold letters. The description of parameters can be found in Supporting Information (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138195#pone.0138195.s005" target="_blank">S1 Text</a>).</p

Refractory [Ca²⁺]_i response to the application of caffeine.

<p>A set of experimental results (blue line) of simultaneous recording of [Ca²⁺]_i (upper trace) and SR Ca²⁺ levels (bottom trace) in response to the application of two pulses of caffeine (middle trace) are presented. Notice that by 30 s after the first application of caffeine both [Ca²⁺]_i and SR Ca²⁺ levels have recovered to resting levels. A second application of caffeine produce the same response in the SR Ca²⁺ with minimal effect on the [Ca²⁺]_i. The numerical simulations of KonD model (red line) and SK model (green line) are also shown. The simulation for both models was calculated by solving numerically the differential equations with the parameters shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138195#pone.0138195.t001" target="_blank">Table 1</a> for 20mM of caffeine with parameter gamma equals to 2.54%. Changing parameter gamma helps SK model to fit the caffeine-induced [Ca²⁺]_i transient but does not show the same time course for the recovery of the [Ca²⁺]_FSR (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138195#pone.0138195.s004" target="_blank">S4 Fig</a>).</p

Caffeine-induced Ca²⁺ release involves four phases that require active SERCA pumps in smooth muscle cells.

<p>Single freshly isolated smooth muscle cells from guinea pig urinary bladder were loaded with both fura-2 and Mag-Fluo-4 to measure [Ca²⁺]_i and [Ca²⁺]_FSR, respectively. (A) Caffeine application (20 mM with a puffer pipette for 5 seconds) induced a transient increase of the [Ca²⁺]_i and a transient reduction of the [Ca²⁺]_FSR. Inhibition of SERCA pumps with thapsigargin reduced both the amplitude and the rate of [Ca²⁺]_i response; however, (B) close examination of these responses show that caffeine induced an increase in the [Ca²⁺]_i before any reduction of the [Ca²⁺]_FSR (phase 1) followed by a sharp reduction in the [Ca²⁺]_FSR without any effect on the [Ca²⁺]_i (phase 2). Interestingly, the inhibition of SERCA pumps with thapsigargin fused phase1 and phase 2 in a linear reduction of the [Ca²⁺]_FSR associated with a smaller increase of the [Ca²⁺]_i. Ca²⁺ recording were carried out as previously described (10).</p

Effect of SERCA pump inhibition by thapsigargin on 20 mM caffeine-induced [Ca²⁺]_i responses.

<p>(A) [Ca²⁺]_i responses of 12 different cells that were exposed to thapsigargin for 5 seconds, 30 seconds before the application of caffeine, were smaller in amplitude and slower and (B) with a larger rise time. The mathematical model fitted this relationship but only after switching from KonD model to SK model (blue lines). (C) Time course of the [Ca²⁺]_i response to 20 mM caffeine from a cell that had been previously exposed to thapsigargin together with fitting by modified SK model (blue line) and the resulting reduction in the [Ca²⁺]_SR from this model (green line). Notice there was no recovery of the [Ca²⁺]_FSR as expected for inhibited SERCA pumps.</p