A state-of-the-Art review of heuristic and metaheuristic optimization techniques for the management of water resources

Water resource management is a complex engineering problem, due to the stochastic nature of inflow, various demands and environmental flow downstream. With the increase in water consumption for domestic use and irrigation, it becomes more challenging. Many more difficulties, such as non-convex, nonlinear, multi-objective, and discontinuous functions, exist in real-life. From the past two decades, heuristic and metaheuristic optimization techniques have played a significant role in managing and providing better performance solutions. The popularity of heuristic and metaheuristic optimization techniques has increased among researchers due to their numerous benefits and possibilities. Researchers are attempting to develop more accurate and efficient models by incorporating novel methods and hybridizing existing ones. This paper's main contribution is to show the state-of-the-art of heuristic and metaheuristic optimization techniques in water resource management. The research provides a comprehensive overview of the various techniques within the context of a thorough evaluation and discussion. As a result, for water resource management problems, this study introduces the most promising evolutionary and swarm intelligence techniques. Hybridization, modifications, and algorithm variants are reported to be the most successful for improving optimization techniques. This survey can be used to aid hydrologists and scientists in deciding the proper optimization techniques.

Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

Electric companies face flow control and inventory obstacles such as reliability, outlays, and time-consuming tasks. Convolutional Neural Networks (CNNs) combined with computational vision approaches can process image classification in warehouse management applications to tackle this problem. This study uses synthetic and real images applied to CNNs to deal with classification of inventory items. The results are compared to seek the neural networks that better suit this application. The methodology consists of fine-tuning several CNNs on Red–Green–Blue (RBG) and Red–Green–Blue-Depth (RGB-D) synthetic and real datasets, using the best architecture of each domain in a blended ensemble approach. The proposed blended ensemble approach was not yet explored in such an application, using RGB and RGB-D data, from synthetic and real domains. The use of a synthetic dataset improved accuracy, precision, recall and f1-score in comparison with models trained only on the real domain. Moreover, the use of a blend of DenseNet and Resnet pipelines for colored and depth images proved to outperform accuracy, precision and f1-score performance indicators over single CNNs, achieving an accuracy measurement of 95.23%. The classification task is a real logistics engineering problem handled by computer vision and artificial intelligence, making full use of RGB and RGB-D images of synthetic and real domains, applied in an approach of blended CNN pipelines.

Controlling forever love

A stable and rewarding love relationship is considered a key ingredient for happiness in Western culture. Building a successful long-term relationship can be viewed as a control engineering problem, where the control variable is the effort to be made to keep the relationship alive and well. We introduce a new mathematical model for the effort control problem of a couple in love who wants to stay together forever. The problem can be naturally formulated as a dynamic game in continuous time with nonlinearities. Adopting a dynamic programming approach, a tractable computational formulation of the problem is proposed together with an accompanying algorithm to find numerical solutions of the couple’s effort problem. The computational analysis of the model is used to explore feeling trajectories, effort control paths, happiness, and stabilization mechanisms for different types of successful couples. In particular, the simulation analysis provides insight into the pattern of change of both marital quality and effort making in intact marriages and how they are affected by certain level of heterogamy in the couple.

Playing Ping Pong With Light: Directional Emission of White Light

Over the last decades, light-emitting diodes (LED) have replaced common light bulbs in almost every application, from flashlights in smartphones to automotive headlights. Illuminating nightly streets requires LEDs to emit a light spectrum that is perceived as pure white by the human eye. The power associated with such a white light spectrum is not only distributed over the contributing wavelengths but also over the angles of vision. For many applications, the usable light rays are required to exit the LED in forward direction, namely under small angles to the perpendicular. In this work, we demonstrate that a specifically designed multi-layer thin film on top of a white LED increases the power of pure white light emitted in forward direction. Therefore, the deduced multi-objective optimization problem is reformulated via a real-valued physics-guided objective function that represents the hierarchical structure of our engineering problem. Variants of Bayesian optimization are employed to maximize this non-deterministic objective function based on ray tracing simulations. Eventually, the investigation of optical properties of suitable multi-layer thin films allowed to identify the mechanism behind the increased directionality of white light: angle and wavelength selective filtering causes the multi-layer thin film to play ping pong with rays of light.

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

Operational reliability and quality loss of diversely configurated manufacturing cells with heterogeneous feedstocks

Machine reliability in cellular manufacturing is a challenging engineering problem in the formation and design of manufacturing cells. The heterogeneity of feedstock quality is also common in manufacturing industry. However, so far, no work has been done to investigate the performance of diversely configurated manufacturing cells under the heterogeneous feedstocks. In this paper, considering the actual engineering condition, the uniformly random arrival and the clustered arrival of low-quality feedstocks are proposed and modeled by the homogeneous Poisson process and Hawkes process, respectively. Also, to study the mixed reliability of a machine under the impact of heterogeneous feedstocks, a mixed failure-rate model is constructed by the mixture of exponential and Weibull distributions, and the processing quality is modeled by a non-homogeneous Poisson process with a dynamic intensity function. Then, we achieve a contrastive analysis for operational reliability and quality loss of manufacturing cells with basic serial and parallel configurations under the impact of heterogeneous feedstocks. At last, the designed simulation illustrates the effectiveness of our proposed models, and some results are concluded to provide some guidelines for the design of manufacturing cells.

Optimal Port Design Minimizing Standing Waves with A Posteriori Long Term Shoreline Sustainability Analysis

Optimization theory is applied to a coastal engineering problem that is the design of a port. This approach was applied to the redesign of La Turballe Port in order to increase the exploitable surface area and simultaneously reduce the occurrence of long waves within the port. Having defined the cost function as a weighted function of wave amplitude and with the chosen parameterization of the port, results show that an extended jetty and a widened mole yield a unique optimal solution. This work demonstrates that numerical optimization may be quick and efficient in the identification of port solutions consistent with classic engineering even in the context of complex problems.

COMPARISON OF ALGORITHMS FOR DETERMINING THE THICKNESS OF OPTICAL COATING LAYERS BASED ON THE MONOCHROMATIC MONITORING DATA

The reverse engineering problem of determining the layer thicknesses of deposited optical coatings from on-line monochromatic measurements is considered. To solve this inverse problem, non-local algorithms are proposed that use all the data accumulated during the deposition process. For the proposed algorithms, the accuracy of solving the inverse problem is compared in the presence of random and systematic errors. It is shown that in the case when the measured data contains only random errors, the best accuracy is provided by the algorithm based on minimizing the discrepancy functional. In the case of systematic errors, the advantage of one the algorithms based on minimizing the variance functionals is demonstrated. Key words: inverse problems, reverse engineering, optical coatings, thin films.

Developing an Observation Tool to Measure Preschool Children’s Problem-Solving Skills

In the Next Generation Science Standards (NGSS), problem-solving skills are part of science and engineering practices for K–12 students in the United States. Evaluating these skills for the youngest learners is difficult due to the lack of established measures. This paper reports on our process of developing an observation instrument to measure preschool children’s learning and their application of problem-solving skills, namely, the steps of the engineering design process (EDP). The instrument, Engineering Preschool Children Observation Tool (EPCOT), was intended to evaluate the frequencies of problem-solving behaviors and use of EDP-related vocabulary by observing preschoolers engaged with the Seeds of STEM eight-unit curriculum in the classroom. In this paper, we describe the development process and revision of EPCOT, its current constructs, and present descriptive findings from using the tool in a pilot study with sixteen classrooms: eight intervention classrooms who received the entire curriculum, and eight comparison classrooms who received only the eighth unit of the curriculum (to enable comparison). We found that, out of 34 possible behaviors across the problem-solving process, children in all classrooms engaged in 31 unique problem-solving behaviors, suggesting that preschool children are indeed capable of meaningfully engaging in solving problems. We also observed a trend that children who were exposed to more of the curriculum (the intervention group) produced more novel vocabulary words than those in the comparison group, who tended to repeat vocabulary words. Since EPCOT was developed in alignment with state and national standards, we believe it has the potential to be used with other early childhood engineering/problem-solving curricula.