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This document presents further numerical results of the experiments concerning the classical instances of the flexible job shop scheduling problem, performed in (Lunardi et al., Metaheuristics for the Online Printing Shop Scheduling Problem, submitted). Additionally, this document gathers the best makespan values (upper bounds and lower bounds) found by state-of-the-art algorithms.

Digital twins for performance-oriented applications in industrial environments require systematic model maintenance. Model adaptation requires efficient optimization tools and continuous evaluation of measurement quality. The adaptation and model performance evaluation are based on the modeling error, making the adaptation prone also to the measurement errors. In this paper, a framework for combining model adaptation and measurement quality assurance are discussed. Two examples with simulated industrialscale biopharmaceutical penicillin fermentation are presented to illustrate the usability of the framework.

We consider the optimal control problem of a small nonlinear econometric model under parameter uncertainty and passive learning (open-loop feedback). Traditionally, this type of problems has been approached by applying linear-quadratic optimization algorithms. However, the literature demonstrated that those methods are very sensitive to the choice of random seeds frequently producing very large objective function values (outliers). Furthermore, to apply those established methods, the original nonlinear problem must be linearized first, which runs the risk of solving already a different problem. Following Savin and Blueschke (Comput Econ 48(2):317–338, 2016) in explicitly addressing parameter uncertainty with a large Monte Carlo experiment of possible parameter realizations and optimizing it with the Differential Evolution algorithm, we extend this approach to the case of passive learning. Our approach provides more robust results demonstrating greater benefit from learning, while at the same time does not require to modify the original nonlinear problem at hand. This result opens new avenues for application of heuristic optimization methods to learning strategies in optimal control research. © 2019, The Author(s).

Generator maintenance scheduling presents many engineering issues that provide power system personnel with a variety of challenges, and one can hardly afford to neglect these engineering issues in the future. Additionally, there is vital need for further development of the repair planning task complexity in order to take into account the vast majority of power flow constraints. At present, the question still remains as to which approach is the simplest and most effective, as well as appropriate for further application in the power flow-oriented statement of the repair planning problem. This research compared directed search, differential evolution, and very fast simulated annealing methods based on a number of numerical calculations and made conclusions about their prospective utilization in terms of a more complicated mathematical formulation of the repair planning task. A comparison of results shows that the effectiveness of directed search methods should not be underestimated, and that the pure differential evolution and very fast simulated annealing approaches are not essentially reliable for repair planning. The experimental results demonstrate the perspectivity of unifying single-procedure methods in order to net out risk associated with specific features of these approaches. © 2020 by the authors.

Soil organic carbon (SOC) sequestration plays a key role in reducing the atmospheric greenhouse gas concentration. However, dry forest ecosystems in Ecuador are endangered to become a source of carbon emissions because of deforestation. Often spatial information, necessary to quantify potential carbon loss to the atmosphere, is missing. This particularly applies to remote areas of limited accessibility. This study aims to regionalise the SOC stocks of a small and poorly accessible dry forest ecosystem in southwestern Ecuador by using boosted regression tree (BRT) models. Resampling in a nested repeated k-fold cross validation approach was applied to develop robust models for a dataset of 118 samples with limited predictor information. To select an optimal set of model parameters, optimisation by differential evolution (DE) was applied for parameter tuning. Predictor selection was implemented using the same optimisation algorithm. This study demonstrates how the predictive performance of BRT models can be improved by applying an optimisation approach for parameter tuning and predictor selection. Model performance was improved by approximately 40% concerning the R2. Still, the results also demonstrated the difficulties of machine learning applications in small and highly heterogeneous natural areas. Very variable or even random factors were assumed to distort the relationship between predictor and response variables. We assume that the presented approach is particularly successful in the case of a real-valued multivariate space of tuning parameters. However, this requires testing in further machine learning applications and algorithms.

Statistical fisheries models are frequently used by researchers and agencies to understand the behavior of marine ecosystems or to estimate the maximum acceptable catch of different species of commercial interest. The parameters of these models are usually adjusted through the use of optimization algorithms. Unfortunately, the choice of the best optimization method is far from trivial. This work proposes the use of population-based algorithms to improve the optimization process of the Globally applicable Area Disaggregated General Ecosystem Toolbox (Gadget), a flexible framework that allows the development of complex statistical marine ecosystem models. Specifically, parallel versions of the Differential Evolution (DE) and the Particle Swarm Optimization (PSO) methods are proposed. The proposals include an automatic selection of the internal parameters to reduce the complexity of their usage, and a restart mechanism to avoid local minima. The resulting optimization algorithms were called PMA (Parallel Multirestart Adaptive) DE and PMA PSO respectively. Experimental results prove that the new algorithms are faster and produce more accurate solutions than the other parallel optimization methods already included in Gadget. Although the new proposals have been evaluated on fisheries models, there is nothing specific to the tested models in them, and thus they can be also applied to other optimization problems. Moreover, the PMA scheme proposed can be seen as a template that can be easily applied to other population-based heuristics.

Recent years have seen a drastic increase in the number of small satellites launched per year, as these systems weighing less than 1000 kg have become less expensive alternatives to obtaining scientific data compared to satellites weighing multiple tons. A current drawback with these systems is their price to orbit, often reaching over $100k (2018) per kilogram for rideshare and cluster launches. Dedicated small satellite launch vehicles are a third solution to bringing small satellites to orbit that present potential reductions in price per kilogram. The combination of reusing the first stage of such a system presents a promising solution to further reducing these prices. The scope of this research is to develop a tool capable of costing a small, reusable launch vehicle using a Multidisciplinary Design Analysis approach, before implementing a Multidisciplinary Design Optimization method to optimize such systems for price per flight in the Tudat development environment. An RP1-propelled, 9-engine first stage design is established as the optimal case for a small, reusable launch vehicle with a price per kilogram of $18.2k (2018). Several expendable launch vehicles are optimized to compare these to the reusable system, with the configurations ranging in price per kilogram from $20.5k (2018) to $30.4k (2018), further demonstrating the cost-reduction potential of the small, reusable launch vehicle., Aerospace Engineering

Hydroelectric power is a relatively cheap, reliable, sustainable, and renewable source of energy that can be generated without toxic waste and considerably lower emissions of greenhouse gases than fossil fuel energy plants. Conventional hydroelectric plants produce energy by the controlled release of dammed reservoir water to one or more turbines via a penstock. The kinetic energy of the falling water produces a rotational motion of the turbine shaft and this mechanical energy is converted into electricity via a power generator. Dam-based plants are among the largest and most flexible power producing facilities in the world, yet their construction and operation is costly and can damage and disrupt upstream and downstream ecosystems and have catastrophic effects on downriver settlements and infrastructure. Run-of-the-river (RoR) hydroelectric stations are an attractive and environmentally friendly alternative to dam-based facilities. These plants divert water from a flowing river to a turbine and do not require the formation of a reservoir. Despite their minimal impact on the surrounding environment and communities, the potential of RoR plants has not been fully explored and exploited. For example, in the United States it is estimated that RoR plants could annually produce 60,000 MW, or about 13% of the total electricity consumption in 2016. Here, we introduce a numerical model, called HYdroPowER or HYPER, which uses a daily time step to simulate the technical performance, energy production, maintenance and operational costs, and economic profit of a RoR plant in response to a suite of different design and construction variables and record of river flows. The model is coded in MATLAB and includes a built-in evolutionary algorithm that enables the user to maximize the RoR plant's power production or net economic profit by optimizing (among others) the penstock diameter, and the type (Kaplan, Francis, Pelton and Crossflow) design flow, and configuration (single/paralle

This paper describes a gender classification scheme based on voice signals in which 16 different fuzzy models are proposed and optimized using four bio-inspired optimization algorithms and the quasi-Newton method. The classification scheme considers four data sets and five different voice features to define the input values of an algorithm in the optimization process. The inputs of each fuzzy model define the mean and variance of their Gaussian membership functions, and their fitness is evaluated by the input values of the algorithm and mean squared error as objective function to be minimized. A comparative analysis between models, algorithms and data sets is made to obtain conclusions according to the results of each optimized model., En este documento se describe un esquema de clasificación de género, basado en señales de voz, en el que se proponen y prueban 16 modelos difusos diferentes que son optimizados mediante cuatro algoritmos bioinspirados y el método cuasi-Newton. El esquema de clasificación considera cuatro conjuntos de datos y cinco características de voz diferentes para definir los valores de entrada de un algoritmo en el proceso de optimización. Los valores de entrada de cada modelo difuso definen la media y varianza de sus funciones de pertenencia gaussianas, y su desempeño se evalúa mediante los valores de entrada del algoritmo de optimización y el error cuadrático medio como función objetivo para minimizar. Se hace un análisis comparativo entre modelos, algoritmos y conjuntos de datos para obtener conclusiones de acuerdo con los resultados de cada modelo optimizado.

Urbanization and population growth lead to the construction of higher buildings in the 21st century. This causes an increment on energy consumption as the amount of constructed floor areas is rising steadily. Integrating daylight performance in building design supports reducing the energy consumption and satisfying occupants’ comfort. This study presents a methodology to optimise the daylight performance of a high-rise building located in a dense urban district. The purpose is to deal with optimisation problems by dividing the high-rise building into five zones from the ground level to the sky level, to achieve better daylight performance. Therefore, the study covers five optimization problems. Overhang length and glazing type are considered to optimise spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE). A total of 500 samples in each zone are collected to develop surrogate models. A self-adaptive differential evolution algorithm is used to obtain near-optimal results for each zone. The developed surrogate models can estimate the metrics with minimum 98.25% R2 which is calculated from neural network prediction and Diva simulations. In the case study, the proposed methodology improves daylight performance of the high-rise building, decreasing ASE by approx. 27.6% and increasing the sDA values by around 88.2% in the dense urban district., Design Informatics

Floating neighborhoods are innovative and promising urban areas for challenges in the development of cities and settlements. However, this design task requires a lot of considerations and technical challenges. Computational tools and methods can be beneficial to tackle the complexity of floating neighborhood design. This paper considers the design of a self-sufficient floating neighborhood by using computational intelligence techniques. In this respect, we consider a design problem for locating each neighborhood function in each cluster with a certain density within a floating neighborhood. In order to develop a self-sufficient floating neighborhood, we propose multi-objective evolutionary algorithms, namely, a self-adaptive real-coded genetic algorithm (CGA) as well as a self-adaptive real-coded genetic algorithm (CGA_DE) employing mutation operator of differential evolution algorithm. The only difference between CGA and CGA_DE is the fact that CGA uses random immigration of certain individuals into the population as a mutation operator whereas in the mutation phase of CGA_DE algorithm, the traditional mutation operator DE/rand/1/bin of DE algorithms. The arrangement of individual functions to develop each neighborhood function is further elaborated and formed by using Voronoi diagram algorithm. An application to design a self-sufficient floating neighborhood in Urla district, which is on the west coast of Turkey, İzmir, is presented., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Design Informatics

In this contribution a method for correcting bathymetric measurements affected by inaccurate water column sound speed profiles (SSPs) is presented. The method exploits the redundancy in the multibeam echosounder measurements obtained from the overlap of adjacent swaths by minimizing the difference between depths along overlapping swaths. Two optimization methods are used, i.e., Differential Evolution (DE) and Gauss-Newton (GN). While DE inverts for the sound speed by minimizing the depth variation, GN inverts for both bathymetry and sound speed by minimizing the squared sum of the differences between the modeled and measured travel times. The inversion method assumes a constant SSP in the water column. Applying the method to a salt wedge survey area with large variations in the water column sound speed indicates a good agreement between the original depth measurements and those derived after the inversion with the mean and standard deviation of the depth differences equaling 0.009m and 0.024m, respectively. This indicates that even with a simple parametrization of the sound speed in the water column, the correct bathymetry can be derived from the inversion. The SSP inversion method is also applied to an area with existing refraction artefacts. It corrects the bathymetry and reduces the mean and standard deviation of the depth standard deviation by a factor of around 2.75 compared to the case where the measured SSPs were used. Furthermore, the SSP inversion method neither manipulates the existing morphology nor introduces artificial bathymetric features in the areas where such refraction artefacts are not present. Considering constant SSPs, both DE and GN give almost identical results with GN being faster. However, GN is less flexible with regards to varying sound speed parameterizations., Aircraft Noise and Climate Effects

Most of the architectural design problems are basically real-parameter optimization problems. So, any type of evolutionary and swarm algorithms can be used in this field. However, there is a little attention on using optimization methods within the computer aided design (CAD) programs. In this paper, we present Optimus, which is a new optimization tool for grasshopper algorithmic modeling in Rhinoceros CAD software. Optimus implements self-adaptive differential evolution algorithm with ensemble of mutation strategies (jEDE). We made an experiment using standard test problems in the literature and some of the test problems proposed in IEEE CEC 2005. We reported minimum, maximum, average, standard deviations and number of function evaluations of five replications for each function. Experimental results on the benchmark suite showed that Optimus (jEDE) outperforms other optimization tools, namely Galapagos (genetic algorithm), SilverEye (particle swarm optimization), and Opossum (RbfOpt) by finding better results for 19 out of 20 problems. For only one function, Galapagos presented slightly better result than Optimus. Ultimately, we presented an architectural design problem and compared the tools for testing Optimus in the design domain. We reported minimum, maximum, average and number of function evaluations of one replication for each tool. Galapagos and Silvereye presented infeasible results, whereas Optimus and Opossum found feasible solutions. However, Optimus discovered a much better fitness result than Opossum. As a conclusion, we discuss advantages and limitations of Optimus in comparison to other tools. The target audience of this paper is frequent users of parametric design modelling e.g., architects, engineers, designers. The main contribution of this paper is summarized as follows. Optimus showed that near-optimal solutions of architectural design problems can be improved by testing different types of algorithms with respect to no-free lunch theorem. Mor, Design Informatics

This paper presents the results of the "Distance Based Parameter Adaptation for Success-History Based Differential Evolution Algorithm" (DISH) on the CEC 2019 100-Digit Challenge. The algorithm has shown satisfying performance when solving 8 out of 10 benchmark functions ISH algorithm can be labeled as robust. © 2019 IEEE.

This article is focused on the optimisation of LDPC codes in order to achieve high efficiency in encoding and decoding messages that also respect Transmission Channel Properties: this issue is related to the frequency band of the assumed transmission, and on models of - faults that affect individual symbols - or groups of symbols, according to their Transmission Environment Properties. © Springer Nature Switzerland AG 2019.

In this paper, we are introducing a novel ensemble based adaptive strategy for the Self Organizing Migrating Algorithm (SOMA), namely the "Ensemble of Strategies and Perturbation Parameter in SOMA" (ESP-SOMA). The proposed algorithm as well as several other state of the art selected metaheuristic algorithms are utilized in the task of optimization of waste processing batch reactor geometry and control. Since there is a growing demand for intelligent and fast problem solution or optimal utilization of resources in modern industrial field, especially in the Industry 4.0 era, this paper represents an insight into the applicability and effectivity of modern adaptive state of the art metaheuristic optimization algorithms in the task of highly constrained industrial design optimization problem. The simple statistical comparison of the results given by three different metaheuristic algorithms is also reported here. © 2019 IEEE.

This research represents a detailed insight into the modern and popular hybridization of unconventional quasiperiodic/chaotic sequences and evolutionary computation. It is aimed at the influence of different randomization schemes on the population diversity, thus on the performance, of two selected adaptive Differential Evolution (DE) variants. Experiments are focused on the extensive investigation of totally ten different randomization schemes for the selection of individuals in DE algorithm driven by the default pseudo-random generator of Java environment and nine different two-dimensional discrete chaotic systems, as the unconventional chaotic pseudo-random number generators. The population diversity is recorded for 15 test functions from the CEC 2015 benchmark set in 10D. © 2019, Springer Nature Switzerland AG.

This paper presents the analysis of the difference in control parameter adaptation between jSO and DISH algorithms. The DISH algorithm uses a distance based parameter adaptation and therefore, is based on the distance between successful offspring and its parent solution rather than on the difference in their corresponding objective function values. The DISH algorithm outperforms the jSO algorithm on the CEC 2015 benchmark set and the adaptation behavior on functions, where the performance is significantly different, is analyzed and commented. The findings from this paper might be used in the future design of jSO based single-objective optimization algorithms. © 2019, Springer Nature Switzerland AG.

In this work, a spiral extrusion die for industrial production of plastic foil has been designed using a modified differential evolution algorithm. The proposed method managed to provide a die design that was compliant with all demands of the foil manufacturer and lowered the production cost. Third-Party software is used to compute the die characteristics from the geometry designed by modified differential evolution. © 2019, Brno University of Technology. All rights reserved.

This work studied a relationship between optimization qualities of Success-History based Adaptive Differential Evolution algorithm (SHADE) and its self-adaptive parameter strategy. Original SHADE with improvement based adaptation is compared to the SHADE with Distance based parameter adaptation (Db_SHADE) on the basis of the CEC2015 benchmark set for continuous optimization and a novel approach combining both distance and improvement adaptation (DIb_SHADE) is presented and tested as a trade-off between both approaches. © 2019, Springer International Publishing AG, part of Springer Nature.

This paper provides a closer insight into applicability and performance of the hybridization of symbolic regression open framework, which is Analytical Programming (AP) and Differential Evolution (DE) algorithm in the task of time series regression. AP can be considered as a robust open framework for symbolic regression thanks to its usability in any programming language with arbitrary driving metaheuristic. The motivation behind this research is to explore and investigate the applicability and differences in performance of AP driven by basic canonical entirely random or best solution driven mutation strategies of DE. An experiment with four case studies has been carried out here with the several time series consisting of GBP/USD exchange rate. The differences between regression/prediction models synthesized using AP as a direct consequence of different DE strategies performances are statistically compared and briefly discussed in conclusion section of this paper. © 2019, Springer International Publishing AG, part of Springer Nature.

This paper proposes a simple, yet effective, modification to scaling factor and crossover rate adaptation in Success-History based Adaptive Differential Evolution (SHADE), which can be used as a framework to all SHADE-based algorithms. The performance impact of the proposed method is shown on the real-parameter single objective optimization (CEC2015 and CEC2017) benchmark sets in 10, 30, 50, and 100 dimensions for all SHADE, L-SHADE (SHADE with linear decrease of population size), and jSO algorithms. The proposed distance based parameter adaptation is designed to address the premature convergence of SHADE–based algorithms in higher dimensional search spaces to maintain a longer exploration phase. This design effectiveness is supported by presenting a population clustering analysis, along with a population diversity measure. Also, the new distance based algorithm versions (Db_SHADE, DbL_SHADE, and DISH) have obtained significantly better optimization results than their canonical counterparts (SHADE, L_SHADE, and jSO) in 30, 50, and 100 dimensional functions. © 2018 Elsevier B.V.

This research paper analyses an external archive of inferior solutions used in Success-History based Adaptive Differential Evolution (SHADE) and its variant with a linear decrease in population size L-SHADE. A novel implementation of an archive is proposed and compared to the original one on CEC2015 benchmark set of test functions for two distinctive dimensionality settings. The proposed archive implementation is referred to as Enhanced Archive (EA) and therefore two Differential Evolution (DE) variants are titled EA-SHADE and EA-L-SHADE. The results on CEC2015 benchmark set are analyzed and discussed. © Springer Nature Switzerland AG 2019.

This paper describes a gender classification scheme based on voice signals in which 16 different fuzzy models are proposed and optimized using four bio-inspired optimization algorithms and the quasi-Newton method. The classification scheme considers four data sets and five different voice features to define the input values of an algorithm in the optimization process. The inputs of each fuzzy model define the mean and variance of their Gaussian membership functions, and their fitness is evaluated by the input values of the algorithm and mean squared error as objective function to be minimized. A comparative analysis between models, algorithms and data sets is made to obtain conclusions according to the results of each optimized model., En este documento se describe un esquema de clasificación de género, basado en señales de voz, en el que se proponen y prueban 16 modelos difusos diferentes que son optimizados mediante cuatro algoritmos bioinspirados y el método cuasi-Newton. El esquema de clasificación considera cuatro conjuntos de datos y cinco características de voz diferentes para definir los valores de entrada de un algoritmo en el proceso de optimización. Los valores de entrada de cada modelo difuso definen la media y varianza de sus funciones de pertenencia gaussianas, y su desempeño se evalúa mediante los valores de entrada del algoritmo de optimización y el error cuadrático medio como función objetivo para minimizar. Se hace un análisis comparativo entre modelos, algoritmos y conjuntos de datos para obtener conclusiones de acuerdo con los resultados de cada modelo optimizado.

Regression testing is just as important for the quality assurance of a system, as it is time consuming. Several techniques exist with the purpose of lowering the execution times of test suites and provide faster feedback to the developers, examples are ones based on transition-models or string-distances. These techniques are called test case selection (TCS) techniques, and focuses on selecting subsets of the test suite deemed relevant for the modifications made to the system under test. This thesis project focused on evaluating the use of a topic model, latent dirichlet allocation, as a means to create a diverse selection of test cases for coverage of certain test characteristics. The model was tested on authentic data sets from two different companies, where the results were compared against prior work where TCS was performed using similarity-based techniques. Also, the model was tuned and evaluated, using an algorithm based on differential evolution, to increase the model’s stability in terms of inferred topics and topic diversity. The results indicate that the use of the model for test case selection purposes was not as efficient as the other similarity-based selection techniques studied in work prior to thist hesis. In fact, the results show that the selection generated using the model performs similar, in terms of coverage, to a randomly selected subset of the test suite. Tuning of the model does not improve these results, in fact the tuned model performs worse than the other methods in most cases. However, the tuning process results in the model being more stable in terms of inferred latent topics and topic diversity. The performance of the model is believed to be strongly dependent on the characteristics of the underlying data used to train the model, putting emphasis on word frequencies and the overall sizes of the training documents, and implying that this would affect the words’ relevance scoring to the better.

Accurate modelling and representation of traffic flows is an important element of intelligent transportation systems, urban planning, and smart environments in general. In this work, location-specific hourly traffic flows are represented by finite mixtures of circular normal statistical distributions. The parameters of the finite mixtures are found by differential evolution, an evolutionary algorithm that is able to fit the statistical models to data with a high level of accuracy. The results are represented by circular plots that can be used as a form of visually appealing and easily understandable fingerprints of the underlying traffic flow patterns., Přesné modelování a reprezentace dopravních toků je důležitým prvkem inteligentních dopravních systémů, městského plánování a inteligentního prostředí obecně. V této práci jsou hodinové dopravní toky specifické pro danou lokalitu reprezentovány konečnou směsí kruhových normálních rozdělení. Parametry tohoto rozdělení směsí jsou nalezeny diferenciální evolucí, evolučním algoritmem, který je schopný přizpůsobit statistické modely datům s vysokou mírou přesnosti. Výsledky jsou reprezentovány kruhovými grafy, které lze použít jako formu vizuálně přitažlivých a snadno srozumitelných vzorů dopravního toku.

[Abstract] Statistical fisheries models are frequently used by researchers and agencies to understand the behavior of marine ecosystems or to estimate the maximum acceptable catch of different species of commercial interest. The parameters of these models are usually adjusted through the use of optimization algorithms. Unfortunately, the choice of the best optimization method is far from trivial. This work proposes the use of population-based algorithms to improve the optimization process of the Globally applicable Area Disaggregated General Ecosystem Toolbox (Gadget), a flexible framework that allows the development of complex statistical marine ecosystem models. Specifically, parallel versions of the Differential Evolution (DE) and the Particle Swarm Optimization (PSO) methods are proposed. The proposals include an automatic selection of the internal parameters to reduce the complexity of their usage, and a restart mechanism to avoid local minima. The resulting optimization algorithms were called PMA (Parallel Multirestart Adaptive) DE and PMA PSO respectively. Experimental results prove that the new algorithms are faster and produce more accurate solutions than the other parallel optimization methods already included in Gadget. Although the new proposals have been evaluated on fisheries models, there is nothing specific to the tested models in them, and thus they can be also applied to other optimization problems. Moreover, the PMA scheme proposed can be seen as a template that can be easily applied to other population-based heuristics.

Актуальність. Короткохвильові інфрачервоні об’єктиви знайшли широке розповсюдження у військовій, медичній та промисловій галузях. Їх використовують для проведення спектрального аналізу та контролю якості продукції, в поганих погодних умовах та нічний час. Головною особливістю короткохвильових інфрачервоних об’єктивів є те, що для їх виготовлення можна використовувати оптичні елементи, призначені для видимого спектру. Ця особливість відрізняє їх від інших приладів, які працюють в більш довгохвильовому інфрачервоному діапазоні та робить їх виробництво більш вигідним. Існуючі підходи до проектування оптичних систем короткохвильових інфрачервоних об’єктивів ґрунтуються на використанні теорії аберацій 3-го та 5-го порядків або оптимізації готових оптичних систем. Недоліком першого підходу є обмеженість розв’язку абераціями кінцевого порядку, а другого – необхідність використання вихідної оптичної системи з задовільною якістю зображення. Тому актуальним є розробка простого та ефективного методу проектування оптичних систем короткохвильових інфрачервоних об’єктивів. Мета дослідження: доведення можливості параметричного синтезу оптичних систем короткохвильових інфрачервоних об’єктивів. Завдання дослідження: 1. Проаналізувати відомі методи глобальної оптимізації та обрати найбільш ефективний для його подальшого використання в процесі параметричного синтезу оптичних систем короткохвильових інфрачервоних об’єктивів. 2. Розробити метод проектування оптичних систем короткохвильових інфрачервоних об’єктивів. 3. Виконати експериментальну перевірку запропонованого методу шляхом розрахунку оптичних систем короткохвильових інфрачервоних об’єктивів та порівняти їх з аналогами. Об’єкт дослідження: процес проектування оптичних систем короткохвильових інфрачервоних об’єктивів. Предмет дослідження: метод автоматизованого розрахунку оптичної системи короткохвильових інфрачервоних об’єктивів., Topic relevance. Shortwave infrared lenses are widespread in the military, medical and industrial fields. They are used for spectral analysis and quality control of products, in foggy weather and at night. The main feature of shortwave infrared lenses is that optical elements designed for the visible spectrum can be used to manufacture them. This feature sets them apart from other devices that operate in the longer wavelengths of the infrared range and make them more profitable to produce. Existing approaches to the design of optical shortwave infrared lens systems are based on the use of 3rd- and 5th-order aberration theory or optimization of existing optical systems. The disadvantage of the first approach is the limited resolution of finite-order aberrations, while the second one needs to use the original optical system with the satisfactory image quality. Therefore, it is important to develop a simple and effective method of designing optical systems for short-wave infrared lenses. Research goal: demonstrating the possibility of parametric synthesis of optical systems of short-wave infrared lenses. Research objectives: 1. To analyze the known methods of global optimization and to choose the most effective for its further use in the process of parametric synthesis of optical systems of short-wave infrared lenses. 2. To develop a method for designing optical systems of short-wave infrared lenses. 3. Perform an experimental verification of the proposed method by calculating optical systems of short-wave infrared lenses and compare them with analogs. Object of research: the process of designing optical systems for short-wave infrared lenses. Subject of research: method of automated calculation of optical systems of short-wave infrared lenses.

The transformation of the traditional transmission power systems due to the current rise of non-synchronous generation on it presents new engineering challenges. One of the challenges is the degradation of the inertial response due to the large penetration of high power converters used for the interconnection of renewables energy sources. The addition of a supplementary synthetic inertia control loop can contribute to the improvement of the inertial response. This paper proposes the application of a novel Fuzzy Adaptive Differential Evolution (FADE) algorithm for the tuning of a fuzzy controller for the improvement of the synthetic inertia control in power systems. The method is validated with two test power systems: (i) an aggregated power system and its purpose is to understand the controller-system behavior, and (ii) a two-area test power system where one of the synchronous machine has been replaced by a full aggregated model of a Wind Turbine Generator (WTG), whereby different limits in the tuning process can be analyzed. Results demonstrate the evolution of the membership functions and the inertial response enhancement in the respective test cases. Moreover, the appropriate tuning of the controller shows that it is possible to substantially reduce the instantaneous frequency deviation., QC 20181120

Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the data centers of the world running. It has become crucial for large IT companies to monitor the energy efficiency of their data-center facilities and to take actions on the optimization of these heavy electricity consumers. This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center. The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE), significantly improving the accuracy of PUE prediction. This model has been evaluated by using real-world data collected from a Facebook data center located in Luleå, Sweden. In addition, to prove the robustness of the predictive model, it has been compared with other machine learning techniques, such as an Artificial Neural Network (ANN) and an Adaptive Neuro Fuzzy Inference System (ANFIS), where it showed a better result. Further, due to the flexibility of the BRBES-based predictive model, it can be used to capture the nonlinear dependencies of many variables of a data center, allowing the prediction of PUE with much accuracy. Consequently, this plays an important role to make data centers more energy-efficient., Validerad;2019;Nivå 2;2019-09-09 (johcin), A belief-rule-based DSS to assess flood risks by using wireless sensor networks, PERCCOM

Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the data centers of the world running. It has become crucial for large IT companies to monitor the energy efficiency of their data-center facilities and to take actions on the optimization of these heavy electricity consumers. This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center. The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE), significantly improving the accuracy of PUE prediction. This model has been evaluated by using real-world data collected from a Facebook data center located in Luleå, Sweden. In addition, to prove the robustness of the predictive model, it has been compared with other machine learning techniques, such as an Artificial Neural Network (ANN) and an Adaptive Neuro Fuzzy Inference System (ANFIS), where it showed a better result. Further, due to the flexibility of the BRBES-based predictive model, it can be used to capture the nonlinear dependencies of many variables of a data center, allowing the prediction of PUE with much accuracy. Consequently, this plays an important role to make data centers more energy-efficient., Validerad;2019;Nivå 2;2019-09-09 (johcin), A belief-rule-based DSS to assess flood risks by using wireless sensor networks, PERCCOM

Regression testing is just as important for the quality assurance of a system, as it is time consuming. Several techniques exist with the purpose of lowering the execution times of test suites and provide faster feedback to the developers, examples are ones based on transition-models or string-distances. These techniques are called test case selection (TCS) techniques, and focuses on selecting subsets of the test suite deemed relevant for the modifications made to the system under test. This thesis project focused on evaluating the use of a topic model, latent dirichlet allocation, as a means to create a diverse selection of test cases for coverage of certain test characteristics. The model was tested on authentic data sets from two different companies, where the results were compared against prior work where TCS was performed using similarity-based techniques. Also, the model was tuned and evaluated, using an algorithm based on differential evolution, to increase the model’s stability in terms of inferred topics and topic diversity. The results indicate that the use of the model for test case selection purposes was not as efficient as the other similarity-based selection techniques studied in work prior to thist hesis. In fact, the results show that the selection generated using the model performs similar, in terms of coverage, to a randomly selected subset of the test suite. Tuning of the model does not improve these results, in fact the tuned model performs worse than the other methods in most cases. However, the tuning process results in the model being more stable in terms of inferred latent topics and topic diversity. The performance of the model is believed to be strongly dependent on the characteristics of the underlying data used to train the model, putting emphasis on word frequencies and the overall sizes of the training documents, and implying that this would affect the words’ relevance scoring to the better.

Differential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to search for optimal solutions in the search space. However, promising solutions/ regions are not adequately exploited by a traditional DE algorithm. Memetic computing has been popular in recent years to enhance the exploitation of global algorithms via incorporation of local search. This paper proposes a new memetic framework to enhance DE algorithms using Alopex Local Search (MFDEALS). The novelty of the proposed MFDEALS framework lies in that the behavior of exploitation (by Alopex local search) can be controlled based on the DE global exploration status (population diversity and search stage). Additionally, an adaptive parameter inside the Alopex local search enables smooth transition of its behavior from exploratory to exploitative during the search process. A study of the important components of MFDEALS shows that there is a synergy between them. MFDEALS has been integrated with both the canonical DE method and the adaptive DE algorithm L-SHADE, leading to the MDEALS and ML-SHADEALS algorithms, respectively. Both algorithms were tested on the benchmark functions from the IEEE CEC'2014 Conference. The experiment results show that Memetic Differential Evolution with Alopex Local Search (MDEALS) not only improves the original DE algorithm but also outperforms other memetic DE algorithms by obtaining better quality solutions. Further, the comparison between ML-SHADEALS and L-SHADE demonstrates that applying the MFDEALS framework with Alopex local search can significantly enhance the performance of L-SHADE.

Urbanization and population growth lead to the construction of higher buildings in the 21st century. This causes an increment on energy consumption as the amount of constructed floor areas is rising steadily. Integrating daylight performance in building design supports reducing the energy consumption and satisfying occupants’ comfort. This study presents a methodology to optimise the daylight performance of a high-rise building located in a dense urban district. The purpose is to deal with optimisation problems by dividing the high-rise building into five zones from the ground level to the sky level, to achieve better daylight performance. Therefore, the study covers five optimization problems. Overhang length and glazing type are considered to optimise spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE). A total of 500 samples in each zone are collected to develop surrogate models. A self-adaptive differential evolution algorithm is used to obtain near-optimal results for each zone. The developed surrogate models can estimate the metrics with minimum 98.25% R2 which is calculated from neural network prediction and Diva simulations. In the case study, the proposed methodology improves daylight performance of the high-rise building, decreasing ASE by approx. 27.6% and increasing the sDA values by around 88.2% in the dense urban district., Design Informatics

Floating neighborhoods are innovative and promising urban areas for challenges in the development of cities and settlements. However, this design task requires a lot of considerations and technical challenges. Computational tools and methods can be beneficial to tackle the complexity of floating neighborhood design. This paper considers the design of a self-sufficient floating neighborhood by using computational intelligence techniques. In this respect, we consider a design problem for locating each neighborhood function in each cluster with a certain density within a floating neighborhood. In order to develop a self-sufficient floating neighborhood, we propose multi-objective evolutionary algorithms, namely, a self-adaptive real-coded genetic algorithm (CGA) as well as a self-adaptive real-coded genetic algorithm (CGA_DE) employing mutation operator of differential evolution algorithm. The only difference between CGA and CGA_DE is the fact that CGA uses random immigration of certain individuals into the population as a mutation operator whereas in the mutation phase of CGA_DE algorithm, the traditional mutation operator DE/rand/1/bin of DE algorithms. The arrangement of individual functions to develop each neighborhood function is further elaborated and formed by using Voronoi diagram algorithm. An application to design a self-sufficient floating neighborhood in Urla district, which is on the west coast of Turkey, İzmir, is presented., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Design Informatics

In this work, a spiral extrusion die for industrial production of plastic foil has been designed using a modified differential evolution algorithm. The proposed method managed to provide a die design that was compliant with all demands of the foil manufacturer and lowered the production cost. Third-Party software is used to compute the die characteristics from the geometry designed by modified differential evolution. © 2019, Brno University of Technology. All rights reserved.

Soil organic carbon (SOC) sequestration plays a key role in reducing the atmospheric greenhouse gas concentration. However, dry forest ecosystems in Ecuador are endangered to become a source of carbon emissions because of deforestation. Often spatial information, necessary to quantify potential carbon loss to the atmosphere, is missing. This particularly applies to remote areas of limited accessibility. This study aims to regionalise the SOC stocks of a small and poorly accessible dry forest ecosystem in southwestern Ecuador by using boosted regression tree (BRT) models. Resampling in a nested repeated k-fold cross validation approach was applied to develop robust models for a dataset of 118 samples with limited predictor information. To select an optimal set of model parameters, optimisation by differential evolution (DE) was applied for parameter tuning. Predictor selection was implemented using the same optimisation algorithm. This study demonstrates how the predictive performance of BRT models can be improved by applying an optimisation approach for parameter tuning and predictor selection. Model performance was improved by approximately 40% concerning the R2. Still, the results also demonstrated the difficulties of machine learning applications in small and highly heterogeneous natural areas. Very variable or even random factors were assumed to distort the relationship between predictor and response variables. We assume that the presented approach is particularly successful in the case of a real-valued multivariate space of tuning parameters. However, this requires testing in further machine learning applications and algorithms.

This work proposes and describes a modular and innovative beamforming network (BFN) to feed a nonequally spaced circular antenna array. The structure is based on a set of alternated power combiners and dividers that delivers a Gaussian-like amplitude distribution and coherent (in-phase) signals. A multiple beam antenna system to generate two main beams in the same aperture with a coherent network for a nonuniform array with beam shaping and beam steering properties is simulated and analyzed. Furthermore, a comparative analysis based on uniform and nonuniform circular antenna arrays fed by the proposed coherent network is conducted. The complex signals and the nonuniform circular aperture are optimized using the well known differential evolution technique. Numerical experiments show the efficiency and improvement of the coherent network with a nonuniform aperture over uniform, with an advantage in average equal to 1.8dB of directivity and -2dB of side lobe level. Moreover, the simulation results exhibit an aperture reuse and complexity reduction of the proposed coherent network configuration compared with a conventional antenna array with direct feeding, where each main beam is shaped and steered with the half of control signal inputs.

In this work, a spiral extrusion die for industrial production of plastic foil has been designed using a modified differential evolution algorithm. The proposed method managed to provide a die design that was compliant with all demands of the foil manufacturer and lowered the production cost. Third-Party software is used to compute the die characteristics from the geometry designed by modified differential evolution.

In this paper, a new variant of an efficient adaptive jSO algorithm is presented. The original jSO uses popular binomial crossover which is applied in a standard coordinate system. Many problems tend to rotate the coordinate system in one or more axes. This is the reason why a crossover variant using Eigen coordinate system replaces the original binomial version of crossover in jSO. The newly proposed jSOe performs significantly better compared with the original jSO when solving 90 problems of the CEC 2017 benchmark set.

This work proposes and describes a modular and innovative beamforming network (BFN) to feed a nonequally spaced circular antenna array. The structure is based on a set of alternated power combiners and dividers that delivers a Gaussian-like amplitude distribution and coherent (in-phase) signals. A multiple beam antenna system to generate two main beams in the same aperture with a coherent network for a nonuniform array with beam shaping and beam steering properties is simulated and analyzed. Furthermore, a comparative analysis based on uniform and nonuniform circular antenna arrays fed by the proposed coherent network is conducted. The complex signals and the nonuniform circular aperture are optimized using the well known differential evolution technique. Numerical experiments show the efficiency and improvement of the coherent network with a nonuniform aperture over uniform, with an advantage in average equal to 1.8dB of directivity and -2dB of side lobe level. Moreover, the simulation results exhibit an aperture reuse and complexity reduction of the proposed coherent network configuration compared with a conventional antenna array with direct feeding, where each main beam is shaped and steered with the half of control signal inputs.

In this contribution a method for correcting bathymetric measurements affected by inaccurate water column sound speed profiles (SSPs) is presented. The method exploits the redundancy in the multibeam echosounder measurements obtained from the overlap of adjacent swaths by minimizing the difference between depths along overlapping swaths. Two optimization methods are used, i.e., Differential Evolution (DE) and Gauss-Newton (GN). While DE inverts for the sound speed by minimizing the depth variation, GN inverts for both bathymetry and sound speed by minimizing the squared sum of the differences between the modeled and measured travel times. The inversion method assumes a constant SSP in the water column. Applying the method to a salt wedge survey area with large variations in the water column sound speed indicates a good agreement between the original depth measurements and those derived after the inversion with the mean and standard deviation of the depth differences equaling 0.009m and 0.024m, respectively. This indicates that even with a simple parametrization of the sound speed in the water column, the correct bathymetry can be derived from the inversion. The SSP inversion method is also applied to an area with existing refraction artefacts. It corrects the bathymetry and reduces the mean and standard deviation of the depth standard deviation by a factor of around 2.75 compared to the case where the measured SSPs were used. Furthermore, the SSP inversion method neither manipulates the existing morphology nor introduces artificial bathymetric features in the areas where such refraction artefacts are not present. Considering constant SSPs, both DE and GN give almost identical results with GN being faster. However, GN is less flexible with regards to varying sound speed parameterizations., Aircraft Noise and Climate Effects

This paper presents the results of the "Distance Based Parameter Adaptation for Success-History Based Differential Evolution Algorithm" (DISH) on the CEC 2019 100-Digit Challenge. The algorithm has shown satisfying performance when solving 8 out of 10 benchmark functions ISH algorithm can be labeled as robust. © 2019 IEEE.

In this paper, we are introducing a novel ensemble based adaptive strategy for the Self Organizing Migrating Algorithm (SOMA), namely the "Ensemble of Strategies and Perturbation Parameter in SOMA" (ESP-SOMA). The proposed algorithm as well as several other state of the art selected metaheuristic algorithms are utilized in the task of optimization of waste processing batch reactor geometry and control. Since there is a growing demand for intelligent and fast problem solution or optimal utilization of resources in modern industrial field, especially in the Industry 4.0 era, this paper represents an insight into the applicability and effectivity of modern adaptive state of the art metaheuristic optimization algorithms in the task of highly constrained industrial design optimization problem. The simple statistical comparison of the results given by three different metaheuristic algorithms is also reported here. © 2019 IEEE.

This paper proposes a simple, yet effective, modification to scaling factor and crossover rate adaptation in Success-History based Adaptive Differential Evolution (SHADE), which can be used as a framework to all SHADE-based algorithms. The performance impact of the proposed method is shown on the real-parameter single objective optimization (CEC2015 and CEC2017) benchmark sets in 10, 30, 50, and 100 dimensions for all SHADE, L-SHADE (SHADE with linear decrease of population size), and jSO algorithms. The proposed distance based parameter adaptation is designed to address the premature convergence of SHADE–based algorithms in higher dimensional search spaces to maintain a longer exploration phase. This design effectiveness is supported by presenting a population clustering analysis, along with a population diversity measure. Also, the new distance based algorithm versions (Db_SHADE, DbL_SHADE, and DISH) have obtained significantly better optimization results than their canonical counterparts (SHADE, L_SHADE, and jSO) in 30, 50, and 100 dimensional functions. © 2018 Elsevier B.V.

Recent years have seen a drastic increase in the number of small satellites launched per year, as these systems weighing less than 1000 kg have become less expensive alternatives to obtaining scientific data compared to satellites weighing multiple tons. A current drawback with these systems is their price to orbit, often reaching over $100k (2018) per kilogram for rideshare and cluster launches. Dedicated small satellite launch vehicles are a third solution to bringing small satellites to orbit that present potential reductions in price per kilogram. The combination of reusing the first stage of such a system presents a promising solution to further reducing these prices. The scope of this research is to develop a tool capable of costing a small, reusable launch vehicle using a Multidisciplinary Design Analysis approach, before implementing a Multidisciplinary Design Optimization method to optimize such systems for price per flight in the Tudat development environment. An RP1-propelled, 9-engine first stage design is established as the optimal case for a small, reusable launch vehicle with a price per kilogram of $18.2k (2018). Several expendable launch vehicles are optimized to compare these to the reusable system, with the configurations ranging in price per kilogram from $20.5k (2018) to $30.4k (2018), further demonstrating the cost-reduction potential of the small, reusable launch vehicle., Aerospace Engineering

This paper presents the analysis of the difference in control parameter adaptation between jSO and DISH algorithms. The DISH algorithm uses a distance based parameter adaptation and therefore, is based on the distance between successful offspring and its parent solution rather than on the difference in their corresponding objective function values. The DISH algorithm outperforms the jSO algorithm on the CEC 2015 benchmark set and the adaptation behavior on functions, where the performance is significantly different, is analyzed and commented. The findings from this paper might be used in the future design of jSO based single-objective optimization algorithms. © 2019, Springer Nature Switzerland AG.

This article is focused on the optimisation of LDPC codes in order to achieve high efficiency in encoding and decoding messages that also respect Transmission Channel Properties: this issue is related to the frequency band of the assumed transmission, and on models of - faults that affect individual symbols - or groups of symbols, according to their Transmission Environment Properties. © Springer Nature Switzerland AG 2019.

This research represents a detailed insight into the modern and popular hybridization of unconventional quasiperiodic/chaotic sequences and evolutionary computation. It is aimed at the influence of different randomization schemes on the population diversity, thus on the performance, of two selected adaptive Differential Evolution (DE) variants. Experiments are focused on the extensive investigation of totally ten different randomization schemes for the selection of individuals in DE algorithm driven by the default pseudo-random generator of Java environment and nine different two-dimensional discrete chaotic systems, as the unconventional chaotic pseudo-random number generators. The population diversity is recorded for 15 test functions from the CEC 2015 benchmark set in 10D. © 2019, Springer Nature Switzerland AG.

Most of the architectural design problems are basically real-parameter optimization problems. So, any type of evolutionary and swarm algorithms can be used in this field. However, there is a little attention on using optimization methods within the computer aided design (CAD) programs. In this paper, we present Optimus, which is a new optimization tool for grasshopper algorithmic modeling in Rhinoceros CAD software. Optimus implements self-adaptive differential evolution algorithm with ensemble of mutation strategies (jEDE). We made an experiment using standard test problems in the literature and some of the test problems proposed in IEEE CEC 2005. We reported minimum, maximum, average, standard deviations and number of function evaluations of five replications for each function. Experimental results on the benchmark suite showed that Optimus (jEDE) outperforms other optimization tools, namely Galapagos (genetic algorithm), SilverEye (particle swarm optimization), and Opossum (RbfOpt) by finding better results for 19 out of 20 problems. For only one function, Galapagos presented slightly better result than Optimus. Ultimately, we presented an architectural design problem and compared the tools for testing Optimus in the design domain. We reported minimum, maximum, average and number of function evaluations of one replication for each tool. Galapagos and Silvereye presented infeasible results, whereas Optimus and Opossum found feasible solutions. However, Optimus discovered a much better fitness result than Opossum. As a conclusion, we discuss advantages and limitations of Optimus in comparison to other tools. The target audience of this paper is frequent users of parametric design modelling e.g., architects, engineers, designers. The main contribution of this paper is summarized as follows. Optimus showed that near-optimal solutions of architectural design problems can be improved by testing different types of algorithms with respect to no-free lunch theorem. Mor, Design Informatics