CBCC3 — A contribution-based cooperative co-evolutionary algorithm with improved exploration/exploitation balance

Cooperative Co-evolution (CC) is a promising framework for solving large-scale optimization problems. However, the round-robin strategy of CC is not an efficient way of allocating the available computational resources to components of imbalanced functions. The imbalance problem happens when the components of a partially separable function have non-uniform contributions to the overall objective value. Contribution-Based Cooperative Co-evolution (CBCC) is a variant of CC that allocates the available computational resources to the individual components based on their contributions. CBCC variants (CBCC1 and CBCC2) have shown better performance than the standard CC in a variety of cases. In this paper, we show that over-exploration and over-exploitation are two major sources of performance loss in the existing CBCC variants. On that basis, we propose a new contribution-based algorithm that maintains a better balance between exploration and exploitation. The empirical results show that the new algorithm is superior to its predecessors as well as the standard CC

Bandit-based cooperative coevolution for tackling contribution imbalance in large-scale optimization problems

This paper addresses the issue of computational resource allocation within the context of cooperative coevolution. Cooperative coevolution typically works by breaking a problem down into smaller subproblems (or components) and coevolving them in a round-robin fashion, resulting in a uniform resource allocation among its components. Despite its success on a wide range of problems, cooperative coevolution struggles to perform efficiently when its components do not contribute equally to the overall objective value. This is of crucial importance on large-scale optimization problems where such difference are further magnified. To resolve this imbalance problem, we extend the standard cooperative coevolution to a new generic framework capable of learning the contribution of each component using multi-armed bandit techniques. The new framework allocates the computational resources to each component proportional to their contributions towards improving the overall objective value. This approach results in a more economical use of the limited computational resources. We study different aspects of the proposed framework in the light of extensive experiments. Our empirical results confirm that even a simple bandit-based credit assignment scheme can significantly improve the performance of cooperative coevolution on large-scale continuous problems, leading to competitive performance as compared to the state-of-the-art algorithms

Solving Incremental Optimization Problems via Cooperative Coevolution

Engineering designs can involve multiple stages, where at each stage, the design models are incrementally modified and optimized. In contrast to traditional dynamic optimization problems where the changes are caused by some objective factors, the changes in such incremental optimization problems are usually caused by the modifications made by the decision makers during the design process. While existing work in the literature is mainly focused on traditional dynamic optimization, little research has been dedicated to solving such incremental optimization problems. In this work, we study how to adopt cooperative coevolution to efficiently solve a specific type of incremental optimization problems, namely, those with increasing decision variables. First, we present a benchmark function generator on the basis of some basic formulations of incremental optimization problems with increasing decision variables and exploitable modular structure. Then, we propose a contribution based cooperative coevolutionary framework coupled with an incremental grouping method for dealing with them. On one hand, the benchmark function generator is capable of generating various benchmark functions with various characteristics. On the other hand, the proposed framework is promising in solving such problems in terms of both optimization accuracy and computational efficiency. In addition, the proposed method is further assessed using a real-world application, i.e., the design optimization of a stepped cantilever beam

Efficient Resource Allocation in Cooperative Co-Evolution for Large-Scale Global Optimization

Cooperative co-evolution (CC) is an explicit means of problem decomposition in multipopulation evolutionary algorithms for solving large-scale optimization problems. For CC, subpopulations representing subcomponents of a large-scale optimization problem co-evolve, and are likely to have different contributions to the improvement of the best overall solution to the problem. Hence, it makes sense that more computational resources should be allocated to the subpopulations with greater contributions. In this paper, we study how to allocate computational resources in this context and subsequently propose a new CC framework named CCFR to efficiently allocate computational resources among the subpopulations according to their dynamic contributions to the improvement of the objective value of the best overall solution. Our experimental results suggest that CCFR can make efficient use of computational resources and is a highly competitive CCFR for solving large-scale optimization problems

Risk assessment of hot and humid environments through an integrated fuzzy AHP-VIKOR method

Working in hot and humid environments can jeopardize the health and safety of the workers and reduce their efficiency. Different physical, environmental, and human factors can influence the risk level of working in these atmospheres. Therefore, the risk assessment of such atmospheres must be carried out from a holistic point of view. This paper aims to introduce a novel risk assessment and prioritization model, using hybrid AHP and VIKOR methods in a fuzzy environment. The AHP method was adopted to determine the importance (weight) of the risk influencing parameters. Also, the VIKOR as a compromise solution method was applied to rank the different working stations against the risk criteria. Fuzzy set theory was used to handle the inherent ambiguity and vagueness of the data encountered in the evaluation process. Furthermore, the fuzzy TOPSIS was adopted to further represent the efficacy of the proposed model. To demonstrate the applicability of the model, a small size foundry shop was selected as the real case and a sensitivity analysis was performed to confirm the validity of the model. The results revealed that the “Environment” has the most contribution to the risk level of hot environments (WE = 0.615). That is followed by “Temperature” (WDBT = 0.268), “Air velocity” (WAV = 0.170), “Safety training” (WST = 0.161), “Mean radiant intensity” (WMRT = 0.110), “Humidity” (WH = 0.066), “Seniority structure” (WSS = 0.063), “Work intensity” (WWI = 0.058), “PPE” (WPPE = 0.047), “Work nature” (WPPE = 0.034), and “ Work duration” (WT = 0.022), in sub-factors. Using the F-VIKOR method, the “melting furnace” workstation was determined as the compromise solution with the index value of Q = 1

Impact of exposure to ambient air pollutants on the admission rate of hospitals for asthma disease in Shiraz, southern Iran

Asthma is a common chronic respiratory disease in the world. Short-term exposure to ambient air pollutants is closely related to acute respiratory diseases and asthmatic symptoms. The purpose of this research was to estimate the correlation between exposure to three air pollutants (O3, NO2, and SO2) and hospital admission because of asthmatic disease (HAAD) in the city of Shiraz, southern Iran. The data were collected from the two real-time monitoring stations located in this city. The acquired information was used for developing predictive models by the AirQ software. The findings of this study were reported for two age groups (<15 and 15–64 years old). The highest levels of O3, NO2, and SO2 were obtained 187.33 μg/m3, 34.1 μg/m3, and 491.2 μg/m3 in 2016, respectively, and 227.75 μg/m3, 92.26 μg/m3, and 190.21 μg/m3, respectively, in 2017. Among the mentioned pollutants, the yearly average concentration of SO2 was 8.62 times more than the WHO guideline, during the studied times. The number of extra cases of HAAD for <15 years and 15–64 years caused by the air pollutants in Shiraz were estimated to be 273 and 36, respectively, in 2016, and 243 and 30 for 2017, respectively. The results of this work displayed that air pollutants have caused respiratory problems in Shiraz city. The AirQ model is a facile and potential tool for the prediction of asthma disease to reduce the health risk of atmospheric pollutants in the worldwide

Gis-based gully erosion susceptibility mapping: a comparison of computational ensemble data mining models

Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision-recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran

Food and nutrition literacy status and its correlates in Iranian senior high-school students

Background: Planning interventions to promote food and nutrition literacy (FNL) require a better understanding of the FNL status of the target group and its correlates. Aims: This study aimed to examine the FNL status and its determinants in Iranian senior high-school students. Methods: In this cross-sectional study, FNL and its components (food and nutrition knowledge, functional skills, interactive skills, advocacy, critical analysis of information, and food label reading skill) were evaluated by a locally designed and validated, self-administered questionnaire. Besides, socioeconomic, demographic, anthropometric measures, as well as academic performance of 626 senior high-school students were assessed. Results: The mean ± SD of the total FNL score (within potential range of 0 to 100) was 52.1 ± 10.96, which is below the minimum adequate level of 60. The probability of high FNL knowledge score was significantly higher among students who majored in Natural Sciences (OR = 1.73, CI = 1.09�2.75), had better school performance (OR = 1.13, CI = 1.06�1.20) and higher SES score (OR = 1.20, CI = 1.01�1.44). The score for food label reading was significantly lower in girls (OR = 0.45, CI = 0.31�0.67), while those who had a family member with the nutrition-related disease were more likely to have a higher score of food label reading skill (OR = 1.48, CI = 1.01�1.64). Conclusion: The level of FNL in senior high-school students in Tehran was relatively low. These findings have key messages for the education system and curriculum designers to have more consideration for food and nutrition-related knowledge and skills in schools. © 2021, The Author(s)

Translocation or just location? Pseudopodia affect fluorescent signals

The use of fluorescent probes is one of the most powerful techniques for gaining spatial and temporal knowledge of dynamic events within living cells. Localized increases in the signal from cytosolic fluorescent protein constructs, for example, are frequently used as evidence for translocation of proteins to specific sites within the cell. However, differences in optical and geometrical properties of cytoplasm can influence the recorded intensity of the probe signal. Pseudopodia are especially problematic because their cytoplasmic properties can cause abrupt increases in fluorescent signal of both GFP and fluorescein. Investigators should therefore be cautious when interpreting fluorescence changes within a cell, as these can result from either translocation of the probe or changes in the optical properties of the milieu surrounding the probe

Radiative recombination of bare Bi83+: Experiment versus theory

Electron-ion recombination of completely stripped Bi83+ was investigated at the Experimental Storage Ring (ESR) of the GSI in Darmstadt. It was the first experiment of this kind with a bare ion heavier than argon. Absolute recombination rate coefficients have been measured for relative energies between ions and electrons from 0 up to about 125 eV. In the energy range from 15 meV to 125 eV a very good agreement is found between the experimental result and theory for radiative recombination (RR). However, below 15 meV the experimental rate increasingly exceeds the RR calculation and at Erel = 0 eV it is a factor of 5.2 above the expected value. For further investigation of this enhancement phenomenon the electron density in the interaction region was set to 1.6E6/cm3, 3.2E6/cm3 and 4.7E6/cm3. This variation had no significant influence on the recombination rate. An additional variation of the magnetic guiding field of the electrons from 70 mT to 150 mT in steps of 1 mT resulted in periodic oscillations of the rate which are accompanied by considerable changes of the transverse electron temperature.Comment: 12 pages, 14 figures, to be published in Phys. Rev. A, see also http://www.gsi.de/ap/ and http://www.strz.uni-giessen.de/~k