Journal articles: 'Complex real-world problems'

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Turco, Alessandro, and Carlos Kavka. "MFGA: a GA for complex real-world optimisation problems." International Journal of Innovative Computing and Applications 3, no. 1 (2011): 31. http://dx.doi.org/10.1504/ijica.2011.037949.

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Pelikán, Emil. "FORECASTING OF PROCESSES IN COMPLEX SYSTEMS FOR REAL-WORLD PROBLEMS : TUTORIAL." Neural Network World 24, no. 6 (2014): 567–89. http://dx.doi.org/10.14311/nnw.2014.24.032.

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Li, Z. G. "Using Electron Diffraction Technique to Solve Real World Problems." Microscopy and Microanalysis 7, S2 (August 2001): 554–55. http://dx.doi.org/10.1017/s1431927600028841.

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Electron diffraction can be a very useful technique in solving real world structure-related problems. However, electron diffraction is much less widely used in industry compared to x-ray diffraction for several reasons. So far, application of electron diffraction has been limited to large-sized companies either to characterize newly synthesized materials in a research division or to directly support business activities in an analytical laboratory. New materials produced on a commercial scale are more and more complex with micro-, even nano-meter sized structures. Development of these materials on a commercial scale, for example, high temperature superconducting compounds, fullerenes, and giant magneoresistance devices [1-7], has increased demand for electron diffraction techniques considerably. Here, I would like to review how electron microscopists in industry solve their real world problems using electron diffraction techniques [8].Unit cell determination. Unit cell parameters and atom coordinates of a crystal can be routinely determined by single crystal x-ray technique if the crystal is large enough (about 0.1 mm in size).

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Sintov, Nicole, Debarun Kar, Thanh Nguyen, Fei Fang, Kevin Hoffman, Arnaud Lyet, and Milind Tambe. "Keeping it Real: Using Real-World Problems to Teach AI to Diverse Audiences." AI Magazine 38, no. 2 (July 1, 2017): 35–47. http://dx.doi.org/10.1609/aimag.v38i2.2733.

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In recent years, AI-based applications have increasingly been used in real-world domains. For example, game theory-based decision aids have been successfully deployed in various security settings to protect ports, airports, and wildlife. This article describes our unique problem-to-project educational approach that used games rooted in real-world issues to teach AI concepts to diverse audiences. Specifically, our educational program began by presenting real-world security issues, and progressively introduced complex AI concepts using lectures, interactive exercises, and ultimately hands-on games to promote learning. We describe our experience in applying this approach to several audiences, including students of an urban public high school, university undergraduates, and security domain experts who protect wildlife. We evaluated our approach based on results from the games and participant surveys.

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Niess, Margaret L., Pejmon Sadri, and Kwangho Lee. "Mathematical Explorations: Variables and Spreadsheets Connect with Real-World Problems." Mathematics Teaching in the Middle School 13, no. 7 (March 2008): 423–31. http://dx.doi.org/10.5951/mtms.13.7.0423.

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Spreadsheet software is generally available in schools and is in wide-spread use in business. The use of spreadsheets can help students make mathematical connections with problems in the world around them. Many real-life quantitative problems require algebra for decision making. Examples include the impact of rising gas prices on family budgets; the amount of gasoline left in the tank of a car and the distance to the nearest gas station; and the level of monthly income versus money needed to pay for food, rent, utilities, and clothing. Although these issues do not require complex mathematics, they do require knowledge of basic algebra involving variables and equations.

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Sutherland, Betsy M. "UV effects in “the real world”: problems of UV dosimetry in complex organisms." Journal of Photochemistry and Photobiology B: Biology 40, no. 1 (August 1997): 8–13. http://dx.doi.org/10.1016/s1011-1344(97)82942-9.

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Gürses, Dildar, Nantiwat Pholdee, Sujin Bureerat, Sadiq M. Sait, and Ali Rıza Yıldız. "A novel hybrid water wave optimization algorithm for solving complex constrained engineering problems." Materials Testing 63, no. 6 (June 1, 2021): 560–64. http://dx.doi.org/10.1515/mt-2020-0093.

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Abstract In this work, a new hybrid optimization algorithm (HWW-NM), which combines the Nelder-Mead local search algorithm with the water wave algorithm, is introduced to solve real-world engineering optimization problems. This paper is one of the first studies in which both the water wave algorithm and the HWW-NM are applied to processing parameters optimization for manufacturing processes. HWW-NM performance is measured using the cantilever beam problem. Additionally, a problem for milling manufacturing optimization is posed and solved to evaluate HWW-NM performance in real-world applications. The results reveal that HWW-NM is an attractive optimization approach for optimizing real-life problems.

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He, Cheng, Ye Tian, Handing Wang, and Yaochu Jin. "A repository of real-world datasets for data-driven evolutionary multiobjective optimization." Complex & Intelligent Systems 6, no. 1 (November 11, 2019): 189–97. http://dx.doi.org/10.1007/s40747-019-00126-2.

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Abstract Many real-world optimization applications have more than one objective, which are modeled as multiobjective optimization problems. Generally, those complex objective functions are approximated by expensive simulations rather than cheap analytic functions, which have been formulated as data-driven multiobjective optimization problems. The high computational costs of those problems pose great challenges to existing evolutionary multiobjective optimization algorithms. Unfortunately, there have not been any benchmark problems reflecting those challenges yet. Therefore, we carefully select seven benchmark multiobjective optimization problems from real-world applications, aiming to promote the research on data-driven evolutionary multiobjective optimization by suggesting a set of benchmark problems extracted from various real-world optimization applications.

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VASANT, P. "A NOVEL HYBRID GENETIC ALGORITHMS AND PATTERN SEARCH TECHNIQUES FOR INDUSTRIAL PRODUCTION PLANNING." International Journal of Modeling, Simulation, and Scientific Computing 03, no. 04 (November 18, 2012): 1250020. http://dx.doi.org/10.1142/s1793962312500201.

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Soft computing has attracted many research scientists, decision makers and practicing researchers in recent years as powerful computational intelligent techniques, for solving unlimited number of complex real-world problems particularly related to research area of optimization. Under the uncertain and turbulence environment, classical and traditional approaches are unable to obtain a complete solution with satisfaction for the real-world problems on optimization. Therefore, new global optimization methods are required to handle these issues seriously. One such method is hybrid Genetic algorithms and Pattern search, a generic, flexible, robust, and versatile framework for solving complex problems of global optimization and search in real-world applications.

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Stojanovic, Boban, Milos Ivanovic, Ana Kaplarevic-Malisic, Visnja Simic, Milovan Milivojevic, Djordje Nedic, Marina Svicevic, Nikola Milivojevic, and Srboljub Mijailovic. "Multi-modeling and multi-scale modeling as tools for solving complex real-world problems." Journal of Serbian Society for Computational Mechanics 10, no. 1 (2016): 34–49. http://dx.doi.org/10.5937/jsscm1601034s.

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Halpern, Diane F., and Dana S. Dunn. "Critical Thinking: A Model of Intelligence for Solving Real-World Problems." Journal of Intelligence 9, no. 2 (April 7, 2021): 22. http://dx.doi.org/10.3390/jintelligence9020022.

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Most theories of intelligence do not directly address the question of whether people with high intelligence can successfully solve real world problems. A high IQ is correlated with many important outcomes (e.g., academic prominence, reduced crime), but it does not protect against cognitive biases, partisan thinking, reactance, or confirmation bias, among others. There are several newer theories that directly address the question about solving real-world problems. Prominent among them is Sternberg’s adaptive intelligence with “adaptation to the environment” as the central premise, a construct that does not exist on standardized IQ tests. Similarly, some scholars argue that standardized tests of intelligence are not measures of rational thought—the sort of skill/ability that would be needed to address complex real-world problems. Other investigators advocate for critical thinking as a model of intelligence specifically designed for addressing real-world problems. Yes, intelligence (i.e., critical thinking) can be enhanced and used for solving a real-world problem such as COVID-19, which we use as an example of contemporary problems that need a new approach.

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Steiner, G., and D. Laws. "How appropriate are two established concepts from higher education for solving complex real‐world problems?" International Journal of Sustainability in Higher Education 7, no. 3 (July 2006): 322–40. http://dx.doi.org/10.1108/14676370610677874.

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Sun, Rui. "Complex Network Evolution Model Based on Node Attraction." Applied Mechanics and Materials 596 (July 2014): 843–46. http://dx.doi.org/10.4028/www.scientific.net/amm.596.843.

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This paper studied the evolution law of the real-world networks, and then proposed a complex network model based on node attraction with tunable parameters in order to solve the problems existing in BA model and the original node attraction model. The model considered the effects of preferential attachments by the changes of degree and node attraction in the evolution process of networks. Theory research and simulation analysis show that we can more flexible adjust the evolution process of network through adjusting model parameters, therefore make it more accord with the network topology and statistical characteristics of real-world networks.

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Chen, Yangyang, Yi Zhao, and Xinyu Han. "Characterization of Symmetry of Complex Networks." Symmetry 11, no. 5 (May 20, 2019): 692. http://dx.doi.org/10.3390/sym11050692.

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Recently, symmetry in complex network structures has attracted some research interest. One of the fascinating problems is to give measures of the extent to which the network is symmetric. In this paper, based on the natural action of the automorphism group Aut ( Γ ) of Γ on the vertex set V of a given network Γ = Γ ( V , E ) , we propose three indexes for the characterization of the global symmetry of complex networks. Using these indexes, one can get a quantitative characterization of how symmetric a network is and can compare the symmetry property of different networks. Moreover, we compare these indexes to some existing ones in the literature and apply these indexes to real-world networks, concluding that real-world networks are far from vertex symmetric ones.

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Bidabad, Bijan, and Behrouz Bidabad. "Complex Probability and Markov Stochastic Process." Indian Journal of Finance and Banking 3, no. 1 (June 4, 2019): 13–22. http://dx.doi.org/10.46281/ijfb.v3i1.290.

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This note discusses the existence of "complex probability" in the real world sensible problems. By defining a measure more general than the conventional definition of probability, the transition probability matrix of discrete Markov chain is broken to the periods shorter than a complete step of the transition. In this regard, the complex probability is implied.

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Schulz, Eric, Rahul Bhui, Bradley C. Love, Bastien Brier, Michael T. Todd, and Samuel J. Gershman. "Structured, uncertainty-driven exploration in real-world consumer choice." Proceedings of the National Academy of Sciences 116, no. 28 (June 24, 2019): 13903–8. http://dx.doi.org/10.1073/pnas.1821028116.

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Making good decisions requires people to appropriately explore their available options and generalize what they have learned. While computational models can explain exploratory behavior in constrained laboratory tasks, it is unclear to what extent these models generalize to real-world choice problems. We investigate the factors guiding exploratory behavior in a dataset consisting of 195,333 customers placing 1,613,967 orders from a large online food delivery service. We find important hallmarks of adaptive exploration and generalization, which we analyze using computational models. In particular, customers seem to engage in uncertainty-directed exploration and use feature-based generalization to guide their exploration. Our results provide evidence that people use sophisticated strategies to explore complex, real-world environments.

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Kola, Jyothi Vishnu Vardhan. "An Optimized way to Solve Regression Problems." International Journal of Engineering and Advanced Technology 10, no. 6 (August 30, 2021): 61–65. http://dx.doi.org/10.35940/ijeat.e2873.0810621.

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In many real world scenarios, regression is a commonly used technique to predict continuous variables. In case of noisy(inconsistent) and incomplete datasets, a large number of previous works adopted complex non traditional machine learning approaches in order to get accurate predictions. However, compromising on time and space overheads. In this paper, we work with complex data yet by using traditional machine learning regression algorithms by working on data cleaning and data transformation according to the working principle of those machine learning algorithms.

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Steiner, Gerald, and Johannes Scherr. "Higher Education for Complex Real-World Problems and Innovation: A Tribute to Heufler’s Industrial Design Approach." Creative Education 04, no. 07 (2013): 130–36. http://dx.doi.org/10.4236/ce.2013.47a2016.

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Rake, Steve. "Widening Their Horizons: Encouraging Students to Understand a Complex World." International Journal of Electrical Engineering & Education 38, no. 4 (October 2001): 279–89. http://dx.doi.org/10.7227/ijeee.38.4.2.

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Professional Issues attempts to demonstrate to undergraduates that they need skills wider than those they will acquire while studying for a degree in computer science or electronic engineering. A study of catastrophic failures gives relevance to activities such as keeping logbooks and group work, while lectures address subjects such as management and the ethical problems that can be encountered in the ‘real world’. There is evidence (somewhat anecdotal) that this approach encourages students to understand the complexities of the world they will be joining on graduation.

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Peters, Sarah L., Lesley K. Fellows, and Signy Sheldon. "The Ventromedial Frontal Lobe Contributes to Forming Effective Solutions to Real-world Problems." Journal of Cognitive Neuroscience 29, no. 6 (June 2017): 991–1001. http://dx.doi.org/10.1162/jocn_a_01088.

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Although the ventromedial frontal lobe (VMF) has been implicated in several complex cognitive tasks such as decision-making and problem solving, the processes for which this region is critical remain unclear. Laboratory studies have largely focused on how the VMF contributes to decision-making when outcomes or options are provided, but in the real world generating appropriate options is likely a crucial and rate-limiting initial step. Here, we determined how VMF damage affected the option generation phase of naturalistic problem solving. A group of patients with VMF damage and two controls groups—age-matched healthy participants and patients with frontal damage sparing VMF—were asked to generate as many options as possible to five scenarios depicting open-ended, real-world problems (e.g., having lunch at a restaurant and forgetting your wallet at home). Both the number of options and the effectiveness of each option generated were examined. Damage to VMF led to a significant reduction in both the number of options produced across all problem-solving scenarios and the ability to generate effective options, most notably for scenarios that were social in nature. We discuss these findings in terms of the mechanisms by which the VMF may contribute to option generation, focusing on proposals suggesting this region is important for integrating subjective value and retrieving schematic representations.

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Stanujkic, Dragisa, Nedeljko Magdalinovic, Rodoljub Jovanovic, and Sanja Stojanovic. "AN OBJECTIVE MULTI-CRITERIA APPROACH TO OPTIMIZATION USING MOORA METHOD AND INTERVAL GREY NUMBERS." Technological and Economic Development of Economy 18, no. 2 (June 29, 2012): 331–63. http://dx.doi.org/10.3846/20294913.2012.676996.

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Many real-world problems are complex and/or related to the manifestation of some form of uncertainty and/or prediction. Therefore the use of extended MCDM methods is more appropriate than the use of the other classic decision making methods. These methods are improved by the use of a form of fuzzy or interval grey numbers. In the field of operational research, during the previous period, numerous MCDM methods were formed, but one newly proposed, the MOORA method, is very specific and yet has no extension. Therefore, in this paper we combine concept of interval grey numbers and MOORA method in order to propose extended MOORA method which will be more appropriate to solve many complex real-world problems.

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Tanweer, M. R., S. Suresh, and N. Sundararajan. "Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problems." Information Sciences 326 (January 2016): 1–24. http://dx.doi.org/10.1016/j.ins.2015.07.035.

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Robson-Williams, Melissa, Bruce Small, Roger Robson-Williams, and Nick Kirk. "Handrails through the Swamp? A Pilot to Test the Integration and Implementation Science Framework in Complex Real-World Research." Sustainability 13, no. 10 (May 14, 2021): 5491. http://dx.doi.org/10.3390/su13105491.

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The socio-environmental challenges the world faces are ‘swamps’: situations that are messy, complex, and uncertain. The aim of this paper is to help disciplinary scientists navigate these swamps. To achieve this, the paper evaluates an integrative framework designed for researching complex real-world problems, the Integration and Implementation Science (i2S) framework. As a pilot study, we examine seven inter and transdisciplinary agri-environmental case studies against the concepts presented in the i2S framework, and we hypothesise that considering concepts in the i2S framework during the planning and delivery of agri-environmental research will increase the usefulness of the research for next users. We found that for the types of complex, real-world research done in the case studies, increasing attention to the i2S dimensions correlated with increased usefulness for the end users. We conclude that using the i2S framework could provide handrails for researchers, to help them navigate the swamps when engaging with the complexity of socio-environmental problems.

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Barták, Roman, Amedeo Cesta, Lee McCluskey, and Miguel A. Salido. "Preface to special issue on planning and scheduling." Knowledge Engineering Review 25, no. 3 (September 2010): 247–48. http://dx.doi.org/10.1017/s0269888910000196.

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AbstractPlanning, scheduling and constraint satisfaction are important areas in artificial intelligence (AI) with broad practical applicability. Many real-world problems can be formulated as AI planning and scheduling (P&S) problems, where resources must be allocated to optimize overall performance objectives. Frequently, solving these problems requires an adequate mixture of planning, scheduling and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints. Constraint satisfaction plays an important role in solving such real-life problems, and integrated techniques that manage P&S with constraint satisfaction are particularly useful. Knowledge engineering supports the solution of such problems by providing adequate modelling techniques and knowledge extraction techniques for improving the performance of planners and schedulers. Briefly speaking, knowledge engineering tools serve as a bridge between the real world and P&S systems.

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Zhu, Tiantian, Zhengqiu Weng, Guolang Chen, and Lei Fu. "A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors." Sensors 20, no. 14 (July 11, 2020): 3876. http://dx.doi.org/10.3390/s20143876.

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With the popularity of smartphones and the development of hardware, mobile devices are widely used by people. To ensure availability and security, how to protect private data in mobile devices without disturbing users has become a key issue. Mobile user authentication methods based on motion sensors have been proposed by many works, but the existing methods have a series of problems such as poor de-noising ability, insufficient availability, and low coverage of feature extraction. Based on the shortcomings of existing methods, this paper proposes a hybrid deep learning system for complex real-world mobile authentication. The system includes: (1) a variational mode decomposition (VMD) based de-noising method to enhance the singular value of sensors, such as discontinuities and mutations, and increase the extraction range of the feature; (2) semi-supervised collaborative training (Tri-Training) methods to effectively deal with mislabeling problems in complex real-world situations; and (3) a combined convolutional neural network (CNN) and support vector machine (SVM) model for effective hybrid feature extraction and training. The training results under large-scale, real-world data show that the proposed system can achieve 95.01% authentication accuracy, and the effect is better than the existing frontier methods.

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VASSILIADIS, VASSILIOS, and GEORGIOS DOUNIAS. "NATURE–INSPIRED INTELLIGENCE: A REVIEW OF SELECTED METHODS AND APPLICATIONS." International Journal on Artificial Intelligence Tools 18, no. 04 (August 2009): 487–516. http://dx.doi.org/10.1142/s021821300900024x.

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The successful handling of numerous real–world complex problems has increased the popularity of nature–inspired intelligent (NII) algorithms and techniques. Their successful implementation primarily on difficult and complicated optimization problems, stresses their upcoming importance in the broader area of artificial intelligence. NII techniques take advantage of the way that biological systems deal with real–world situations. Specifically, they simulate the way real biological systems, such as the human brain, ant colonies and human immune system work, when solving complex real–world situations. In this survey paper, we briefly present a number of selected NII approaches and we point particular suitable areas of application for each of them. Specifically, five major categories of nature inspired approaches are presented, namely, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), DNA computing, artificial immune systems and membrane computing. Applications include problems related to optimization (financial, industrial and medical), task scheduling, system design (optimization of the system's parameters), image processing and data processing (feature selection and classification). We also refer to collaboration between NII techniques and classical AI methodologies, such as neural networks, genetic algorithms, fuzzy logic, etc. The current survey states that NII techniques are likely to become the next step in the rapid evolution of artificial intelligence tools.

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Du, Dawei, and Dan Simon. "Complex System Optimization Using Biogeography-Based Optimization." Mathematical Problems in Engineering 2013 (2013): 1–17. http://dx.doi.org/10.1155/2013/456232.

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Complex systems are frequently found in modern industry. But with their multisubsystems, multiobjectives, and multiconstraints, the optimization of complex systems is extremely hard. In this paper, a new algorithm adapted from biogeography-based optimization (BBO) is introduced for complex system optimization. BBO/Complex is the combination of BBO with a multiobjective ranking system, an innovative migration approach, and effective diversity control. Based on comparisons with three complex system optimization algorithms (multidisciplinary feasible (MDF), individual discipline feasible (IDF), and collaborative optimization (CO)) on four real-world benchmark problems, BBO/Complex demonstrates competitive performance. BBO/Complex provides the best performance in three of the benchmark problems and the second best in the fourth problem.

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Peng, Kua-Hsin, and Gwo-Hshiung Tzeng. "A HYBRID DYNAMIC MADM MODEL FOR PROBLEM-IMPROVEMENT IN ECONOMICS AND BUSINESS." Technological and Economic Development of Economy 19, no. 4 (December 12, 2013): 638–60. http://dx.doi.org/10.3846/20294913.2013.837114.

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A typical multiple attribute decision making (MADM) model is a scientific analytical model for evaluating and improving a set of alternatives based on multiple criteria. However, this study identified some important new concepts and limitations/defects of traditional MADM for solving the real-world problems. First, the traditional MADM model assumes that criteria considered are independent and hierarchical in structure; however, the real-world problems often involve interdependent criteria, and thus interdependent models are required. Second, relatively good solutions from existing alternatives are replaced by the aspiration levels. Third, the trend has shifted from how to “rank” or “select” the most preferable alternatives, to how to “improve” their performances. Fourth, information fusion/aggregation, such as fuzzy integrals, basically, a non-additive/super-additive model, has been developed for performance aggregation. Therefore, to overcome the defects of the conventional MADM method and solve complex and dynamic real world problems, a Hybrid Dynamic Multiple Criteria Decision Making (HDMADM) method is needed. Finally, this study presented real cases to demonstrate the effectiveness of the HDMADM method for overcoming the defects of the conventional MADM method.

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Aboura, Khalid, Miroljub Kljajić, and Ali Eskandarian. "The need for simulation in complex industrial systems." Organizacija 45, no. 5 (October 1, 2012): 219–27. http://dx.doi.org/10.2478/v10051-012-0022-4.

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We discuss the concept of simulation and its application in the resolution of problems in complex industrial systems. Most problems of serious scale, be it an inventory problem, a production and distribution problem, a management of resources or process improvement, all real world problems require a mix of generic, data algorithmic and Ad-hoc solutions making the best of available information. We describe two projects in which analytical solutions were applied or contemplated. The first case study uses linear programming in the optimal allocation of advertising resources by a major internet service provider. The second study, in a series of projects, analyses options for the expansion of the production and distribution network of mining products, as part of a sensitive strategic business review. Using the examples, we make the case for the need of simulation in complex industrial problems where analytical solutions may be attempted but where the size and complexity of the problem forces a Monte Carlo approach.

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DODARO, CARMINE, PHILIP GASTEIGER, NICOLA LEONE, BENJAMIN MUSITSCH, FRANCESCO RICCA, and KOSTYANTYN SHCHEKOTYKHIN. "Combining Answer Set Programming and domain heuristics for solving hard industrial problems (Application Paper)." Theory and Practice of Logic Programming 16, no. 5-6 (September 2016): 653–69. http://dx.doi.org/10.1017/s1471068416000284.

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AbstractAnswer Set Programming (ASP) is a popular logic programming paradigm that has been applied for solving a variety of complex problems. Among the most challenging real-world applications of ASP are two industrial problems defined by Siemens: the Partner Units Problem (PUP) and the Combined Configuration Problem (CCP). The hardest instances of PUP and CCP are out of reach for state-of-the-art ASP solvers. Experiments show that the performance of ASP solvers could be significantly improved by embedding domain-specific heuristics, but a proper effective integration of such criteria in off-the-shelf ASP implementations is not obvious. In this paper the combination of ASP and domain-specific heuristics is studied with the goal of effectively solving real-world problem instances of PUP and CCP. As a byproduct of this activity, the ASP solverwaspwas extended with an interface that eases embedding new external heuristics in the solver. The evaluation shows that our domain-heuristic-driven ASP solver finds solutions for all the real-world instances of PUP and CCP ever provided by Siemens.

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Ugon, J., S. Kouhbor, M. Mammadov, A. Rubinov, and A. Kruger. "Facility location via continuous optimization with discontinuous objective functions." ANZIAM Journal 48, no. 3 (January 2007): 315–25. http://dx.doi.org/10.1017/s1446181100003515.

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AbstractFacility location problems are one of the most common applications of optimization methods. Continuous formulations are usually more accurate, but often result in complex problems that cannot be solved using traditional optimization methods. This paper examines theuse of a global optimization method—AGOP—for solving location problems where the objective function is discontinuous. This approach is motivated by a real-world application in wireless networks design.

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Li, Chao, and Il Yong Kim. "Multi-material topology optimization for automotive design problems." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 232, no. 14 (November 24, 2017): 1950–69. http://dx.doi.org/10.1177/0954407017737901.

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The algorithms for multi-material topology optimization were developed to solve compliance-minimization problems and applied to engineering problems in automotive concepts and lightweight design. Two small-scale problems of a long cantilever and a control arm were studied initially to verify the effectiveness of the developed algorithms and in-house program. Optimal solutions achieved by the multi-material topology optimization method developed were compared to their counterparts obtained by standard single-material topology optimization. To efficiently solve real-world engineering problems, the algorithms were further advanced to incorporate extrusion constraints and to handle multiple load cases. The effectiveness and the efficiency of the proposed method were demonstrated by the study of two real-world engineering problems: (a) the conceptual design of a cross-member for a chassis frame; and (b) the conceptual design of an automotive engine cradle. The two optimization design problems both involved complex geometries, design and non-design domains, prescribed regions with specific material allocations, multiple load cases, and manufacturing extrusion constraints. It was explicitly demonstrated that, for the same weight, the optimum designs achieved by the multi-material topology optimization method were stiffer than those achieved by standard single-material topology optimization.

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Matis, Peter. "FINDING A SOLUTION FOR A COMPLEX STREET ROUTING PROBLEM USING THE MIXED TRANSPORTATION MODE." TRANSPORT 25, no. 1 (March 31, 2010): 29–35. http://dx.doi.org/10.3846/transport.2010.05.

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The Street Routing Problem (SRP) is a special case of the well-known Vehicle Routing Problem (VRP). The goal is to service a large number of customers in the city zone. New heuristics for solving a complex SRP is evaluated based on real data. This paper presents several approximations to the length of SRP using the mixed transportation mode and compares them with the published approximations used for VRP or Travelling Salesman Problems (TSP). The system was tested in five real world instances ranging from 12 000 to 29 000 customers.

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MacMillan, Jean, Eileen B. Entin, and Daniel Serfaty. "Evaluating Expertise in a Complex Domain–Measures Based on Theory." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, no. 17 (October 1993): 1152–55. http://dx.doi.org/10.1177/154193129303701706.

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Human factors practitioners are often concerned with defining and evaluating expertise in complex domains where there may be no agreed-upon expertise levels, no single right answers to problems, and where the observation and measurement of real-world expert performance is difficult. This paper reports the results of an experiment in which expertise was assessed in an extremely complex and demanding domain–military command decision making in tactical warfare. The hypotheses of the experiment were: 1) command decisionmaking expertise can be recognized in practice by domain experts; 2) differences in the command decisionmaking expertise of individuals can be identified even under conditions that do not fully replicate the real world; and 3) observers who are not domain experts can recognize the expert behaviors predicted by a mental-model theory about the nature of expertise. In the experiment, the expertise of military officers in developing tactical plans was assessed independently by three “super-expert” judges, and these expertise-level ratings were correlated with independent theory-based measures used by observers who were not domain experts. The results suggest that experts in a domain have a shared underlying concept of expertise in that domain even if they cannot articulate that concept, that this expertise can be elicited and measured in situations that do not completely mimic the real world, and that expertise measures based on a mental-model theory can be used effectively by observers who are not experts in the domain.

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Oortwijn, Wija, Laura Sampietro-Colom, and Rebecca Trowman. "How to Deal with the Inevitable: Generating Real-World Data and Using Real-World Evidence for HTA Purposes – From Theory to Action." International Journal of Technology Assessment in Health Care 35, no. 4 (2019): 346–50. http://dx.doi.org/10.1017/s0266462319000400.

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AbstractObjectivesReal-world evidence (RWE), derived from real-world data (RWD), is already used, to some extent, for health technology assessment (HTA) purposes. With the increased availability of RWD, there is potential for more widespread use but also challenges ensuring reliable RWE for HTA. Opportunities to overcome key challenges, identified at a scoping meeting, were discussed during the 2019 HTA international (HTAi) Global Policy Forum (GPF).MethodsReflection of discussions using Design Thinking (an interactive process aimed to solve complex problems) between seventy-three representatives from not-for-profit, for-profit organizations, and HTAi leadership. The discussions were informed by a background paper, and presentations from three invited keynote speakers and eleven GPF members.ResultsSeveral options were listed for addressing the identified key challenges: quality and acceptability, governance and accountability, transferability, and informing decision making. The GPF emphasized that the HTA community should first understand what questions could be answered with RWE. Additionally, more clarity on methods, standards, streamlining RWD collection, data sharing across jurisdictions, replication of RWD, and expert analysis were mentioned as important priorities.ConclusionsThe HTA community is currently standing at a cross-road as it is not yet fully equipped to address these key challenges. It is, therefore, time for action. The community should start aligning on what is the best source of evidence according to purpose and how the data should be collected to create reliable evidence. It should also initiate the development of actions and guidance to properly develop and manage RWD/RWE to inform decision making across the technology lifecycle.

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Nicholls, B. "Challenge based learning: a real-world approach for secondary students to solve complex problems using geoscience knowledge and skills." Terrae Didatica 14, no. 4 (October 30, 2018): 369–72. http://dx.doi.org/10.20396/td.v14i4.8653828.

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Students are regularly confronted with ethical and environmental issues of a global, regional or local nature which require knowledge of the geosciences to solve. Having the skills to engage, explore and evaluate these problems enhances student’s capacity for creative, real-world problem solving. At the Australian Science and Mathematics School, a Challenge Based Learning approach has been used across a range of learning programs including those with a focus on geoscience. Challenge Based Learning in the context of this paper is defined as a method of teaching and learning which allows students to engage in learning goals through meaningful contexts. An unfamiliar ‘ill-structured’ problem is presented to the students by the teacher and the students are required to determine for themselves how they will go about solving the problem. This process occurs through small groups of students working collaboratively and allows sharing their prior knowledge, skills and understandings to identify gaps in their collective understanding as they attempt to offer solutions to the problem. The example chosen is based around a problem requiring significant time in the field where secondary students come to know and understand the problem in greater depth while developing scientific field investigation skills.

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Steiner, Gerald, and Alfred Posch. "Higher education for sustainability by means of transdisciplinary case studies: an innovative approach for solving complex, real-world problems." Journal of Cleaner Production 14, no. 9-11 (January 2006): 877–90. http://dx.doi.org/10.1016/j.jclepro.2005.11.054.

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Lab, Damon, Ines Santos, and Felicity de Zulueta. "Treating post-traumatic stress disorder in the ‘real world’: evaluation of a specialist trauma service and adaptations to standard treatment approaches." Psychiatric Bulletin 32, no. 1 (January 2008): 8–12. http://dx.doi.org/10.1192/pb.bp.105.008664.

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Aims and MethodTo evaluate the effectiveness of treatment at the Traumatic Stress Service (TSS) by comparing pre- and post-treatment scores on patient self-report measures. Through a questionnaire survey, to explore therapists' views of problems presenting in addition to post-traumatic stress disorder (PTSD) and how, as a result, they adapted their approach to trauma work.ResultsTherapists reported that their patients present with a range of complex problems, and self-report measures show that patients suffer particularly high levels of psychopathology. Therapists identified a number of adaptations to trauma-focused work to deal with these additional problems. Of the 112 patients who completed therapy, 43% filled in pre- and post-treatment questionnaire measures. Analysis showed clinically and statistically significant improvements in levels of PTSD, depression and social functioning.Clinical ImplicationsThe typical presentation of trauma survivors is often not ‘simple’ PTSD, but PTSD resulting from chronic and multiple traumas and complicated by additional psychological and social difficulties. Adaptations to trauma-focused work can successfully treat such ‘complex’ PTSD.

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Yu, Yadong, Haiping Ma, Mei Yu, Sengang Ye, and Xiaolei Chen. "Multipopulation Management in Evolutionary Algorithms and Application to Complex Warehouse Scheduling Problems." Complexity 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/4730957.

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Multipopulation is an effective optimization strategy which is often used in evolutionary algorithms (EAs) to improve optimization performance. However, it is of remarkable difficulty to determine the number of subpopulations during the evolution process for a given problem, which may significantly affect optimization ability of EAs. This paper proposes a simple multipopulation management strategy to dynamically adjust the subpopulation number in different evolution phases throughout the evolution. The proposed method makes use of individual distances in the same subpopulation as well as the population distances between multiple subpopulations to determine the subpopulation number, which is substantial in maintaining population diversity and enhancing the exploration ability. Furthermore, the proposed multipopulation management strategy is embedded into popular EAs to solve real-world complex automated warehouse scheduling problems. Experimental results show that the proposed multipopulation EAs can easily be implemented and outperform other regular single-population algorithms to a large extent.

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James, Nikki, Andrea Humez, and Philipp Laufenberg. "Using Technology to Structure and Scaffold Real World Experiential Learning in Distance Education." TechTrends 64, no. 4 (June 6, 2020): 636–45. http://dx.doi.org/10.1007/s11528-020-00515-2.

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Abstract Team projects allow students to apply their technical skills to a real-world context and build twenty-first century competencies, including teamwork, project management and communication skills. However, the complex challenges that such experiential learning projects present for students and faculty can limit the scale of implementation. This article argues that Virtual Business Projects (VBP), a model of team-based experiential learning where teams of students complete a virtual business project for an industry sponsor, can mitigate these problems by leveraging instructional technology and learning analytics. The VBP model is deployed in multiple universities, which have provided more than 2500 Virtual Business Projects since 2015. We will discuss how innovative technology, embedded in thoughtful learning design, supports experiential learning by taking advantage of features such as customizable scaffolding, automated reflection and feedback loops, and learning analytics.

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ORTIZ, G., and M. D. JONES. "EXPLORING THE QUANTUM WORLD OF COMPLEX STATES." International Journal of Modern Physics B 13, no. 05n06 (March 10, 1999): 525–34. http://dx.doi.org/10.1142/s0217979299000424.

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Solving the fundamental microscopic equations of interacting quantum particles is a goal of many-body physicists. Statistical methods reduce the complexity of the problem by sampling phase space selectively using random-walks and real states. Many interesting physical phenomena (e.g., electrons in external magnetic fields) involve systems whose state functions are inherently complex-valued. The Fixed-Phase method is a stochastic approach to deal with such problems. Its key ingredient is a trial phase that plays the role of gauge function in the transformation that maps the original fermion (or boson) problem to a boson problem for the modulus of the state function. The Released-Phase method relaxes that constraint and allows us to obtain, in principle, the "exact" properties, although it is subjected to the infamous "phase problem." In our tour of the (complex) Quantum World, we will show how these methods have been successfully applied to a wide variety of physical phenomena ranging from quantum Hall topological fluids and Wigner crystals to the study of the core structure of vortices in superfluid 4 He and atomic systems in superstrong magnetic fields found in astrophysical settings.

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Sternberg, Robert J. "Rethinking what we mean by intelligence." Phi Delta Kappan 102, no. 3 (October 26, 2020): 36–41. http://dx.doi.org/10.1177/0031721720970700.

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Society largely has accepted a conception of general intelligence that is ill-fitted to the world we live in today. Our standardized tests are based on this conception, as is much of our instruction. The problem is that the kinds of problems we test for and teach to are unlike the complex problems we face in real life. In this article, Robert Sternberg introduces a conception of adaptive intelligence as the kind of intelligence we need to cope adequately with the problems the world is currently throwing at us. He discusses adaptive intelligence and how it differs from general intelligence and gives examples of the kinds of problems schools can use to teach and assess for such intelligence.

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Aly, Shaban, Ali Al-Qahtani, Houari B. Khenous, and Gamal M. Mahmoud. "Impulsive Control and Synchronization of Complex Lorenz Systems." Abstract and Applied Analysis 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/932327.

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In this paper, we continue our investigations on control and synchronization of the complex Lorenz systems by investigating impulsive control and synchronization. Nonlinear systems involving impulse effects, appear as a natural description of observed evolution phenomena of several real world problems; For example, many biological phenomena involving thresholds, bursting rhythm models in medicine and biology, optimal control models in economics, population dynamics and so forth do exhibit impulsive effects. Some new and more comprehensive criteria for global exponential stability and asymptotical stability of impulsively controlled complex Lorenz systems are established with varying impulsive intervals. The effectiveness of the proposed technique is verified through numerical simulations.

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Fioretto, Ferdinando, Enrico Pontelli, and William Yeoh. "Distributed Constraint Optimization Problems and Applications: A Survey." Journal of Artificial Intelligence Research 61 (March 29, 2018): 623–98. http://dx.doi.org/10.1613/jair.5565.

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The field of multi-agent system (MAS) is an active area of research within artificial intelligence, with an increasingly important impact in industrial and other real-world applications. In a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as a prominent agent model to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have been proposed to enable support of MAS in complex, real-time, and uncertain environments. This survey provides an overview of the DCOP model, offering a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.

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Liu, Ziang, and Tatsushi Nishi. "Multipopulation Ensemble Particle Swarm Optimizer for Engineering Design Problems." Mathematical Problems in Engineering 2020 (November 11, 2020): 1–30. http://dx.doi.org/10.1155/2020/1450985.

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Particle swarm optimization (PSO) is an efficient optimization algorithm and has been applied to solve various real-world problems. However, the performance of PSO on a specific problem highly depends on the velocity updating strategy. For a real-world engineering problem, the function landscapes are usually very complex and problem-specific knowledge is sometimes unavailable. To respond to this challenge, we propose a multipopulation ensemble particle swarm optimizer (MPEPSO). The proposed algorithm consists of three existing efficient and simple PSO searching strategies. The particles are divided into four subpopulations including three indicator subpopulations and one reward subpopulation. Particles in the three indicator subpopulations update their velocities by different strategies. During every learning period, the improved function values of the three strategies are recorded. At the end of a learning period, the reward subpopulation is allocated to the best-performed strategy. Therefore, the appropriate PSO searching strategy can have more computational expense. The performance of MPEPSO is evaluated by the CEC 2014 test suite and compared with six other efficient PSO variants. These results suggest that MPEPSO ranks the first among these algorithms. Moreover, MPEPSO is applied to solve four engineering design problems. The results show the advantages of MPEPSO. The MATLAB source codes of MPEPSO are available at https://github.com/zi-ang-liu/MPEPSO.

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Almufti, Saman. "The novel Social Spider Optimization Algorithm: Overview, Modifications, and Applications." ICONTECH INTERNATIONAL JOURNAL 5, no. 2 (June 28, 2021): 32–51. http://dx.doi.org/10.46291/icontechvol5iss2pp32-51.

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The continues in real-world problems increasing complexity motivated computer scientists and researchers to search for more-efficient problem-solving strategies. Generally natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation and swarm intelligence algorithms have been frequently used for solving complex, real-world optimization problems because of their ability to adjust to variety of conditions. This paper present a swarm based algorithm that is based on the cooperative behaviors between social spider, it called Social Spider Optimization (SSO) algorithm. In SSO, search agents characterize a set of spiders which together move according to a biological behavior in colony. During the past years after SSO introduction, many modifications has improved the performance of the algorithm and has been applied in several fields. In this paper, the improvements, and applications of the SSO are reviewed.

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Girsh, A. "In Favor of Imaginaries in Geometry." Geometry & Graphics 8, no. 2 (August 17, 2020): 33–40. http://dx.doi.org/10.12737/2308-4898-2020-33-40.

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“Complex numbers are something complicated”, as they are perceived in most cases. The expression “real numbers are also complex numbers” sounds strange as well. And for all that complex numbers are good for many areas of knowledge, since they allow solve problems, that are not solved in the field of real numbers. First and most important is that in the field of complex numbers all algebraic equations are solved, including the equation x2 + a = 0, which has long been a challenge to human thought. In the field of complex numbers, the problem solutions remain free from listing special cases in the form of "if ... then", for example, solving the problem for the intersection of the line g with the circle (O, r) always gives two points. And in the field of real numbers, three cases have to be distinguished: | Og | <r → there are two real points; | Og |> r → there is no intersection; | Og | = r → there is one double point. The benefit of complex numbers also lies in the fact that with their help not only problems that previously had no solutions are solved, they not only greatly simplify the solution result, but they also hold shown in this text further amazing properties in geometric figures, and open door to the amazing and colorful world of fractals.

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Martínez, Víctor, Fernando Berzal, and Juan-Carlos Cubero. "NOESIS: A Framework for Complex Network Data Analysis." Complexity 2019 (October 31, 2019): 1–14. http://dx.doi.org/10.1155/2019/1439415.

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Network data mining has attracted a lot of attention since a large number of real-world problems have to deal with complex network data. In this paper, we present NOESIS, an open-source framework for network-based data mining. NOESIS features a large number of techniques and methods for the analysis of structural network properties, network visualization, community detection, link scoring, and link prediction. The proposed framework has been designed following solid design principles and exploits parallel computing using structured parallel programming. NOESIS also provides a stand-alone graphical user interface allowing the use of advanced software analysis techniques to users without prior programming experience. This framework is available under a BSD open-source software license.

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DIOŞAN, LAURA, and MIHAI OLTEAN. "EVOLVING THE UPDATE STRATEGY OF THE PARTICLE SWARM OPTIMISATION ALGORITHMS." International Journal on Artificial Intelligence Tools 16, no. 01 (February 2007): 87–109. http://dx.doi.org/10.1142/s0218213007003217.

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A complex model for evolving the update strategy of a Particle Swarm Optimisation (PSO) algorithm is described in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The Evolved PSO algorithm is compared to several human-designed PSO algorithms by using ten artificially constructed functions and one real-world problem. Numerical experiments show that the Evolved PSO algorithm performs similarly and sometimes even better than the Standard approaches for the considered problems. The Evolved PSO is highly scalable (regarding the size of the problem's input), being able to solve problems having different dimensions.

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Gupta, Nikita, Nishant Bhardwaj, Gulam Muhammad Khan, and Vivek Dave. "Global Trends of Computational Fluid Dynamics to Resolve Real World Problems in the Contemporary Era." Current Biochemical Engineering 6, no. 3 (December 28, 2020): 136–55. http://dx.doi.org/10.2174/2212711906999200601121232.

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Background: Computational fluid dynamics (CFD) came into existence with great success, thereby replacing the traditional methods used to simulate the problems related to the flow of fluid. First CFD utilitarian was introduced to the world in 1957, which was developed by a team at Los Alamos National Lab. For tremendous performance and to meet the expected results with ease for modern process conditions, engineers are now more inclined towards the use of simulation software rather than traditional methods. Hence, in the current scenario with the advancement of computer technologies, “CFD is recognized as an excellent tool for engineers to resolve real-world problems.” Introduction: CFD is defined as a branch of fluid dynamics which involves the use of numerical analysis and data structure to solve complications related to the flow of fluids (gasses or liquids). CFD is based on three major principles that are mass conservation, Newton's second law, and energy conservation. CFD has extended to a number of applications at an alarming rate in every field such as in aerospace, sports, food industry, engineering, hydraulics, HVAC (Heating, Ventilating, and Air conditioning), automotive, environmental, power generation, biomedical, pharmaceutical, and many more. Hence, a number of software like ANSYS, Open Foam, SimScale, Gerris, Auto desk simulation, Code_Saturne, etc, are beneficial in order to execute the operations, and to find the solution of realworld problems within a fraction of seconds. Methods: CFD analysis involves three major steps; pre-processing, solution, and post-processing. Preprocessing deals with defining model goals, identification of domain, designing, and creating the grid. Solution involves setting up the numerical model, computing, and monitoring the solution; whereas, post-processing includes results of the examination and revision of the model. Results: The review includes current challenges about the computational fluid dynamics. It is relevant in different areas of engineering to find answers for the problems occurring globally with the aid of a number of simulation-based software hereby, making the world free from complex problems in order to have a non-complicated scenario. Conclusion: Computational fluid dynamics are relevant in each, and every kind of problem related to the fluid flow, either existing in the human body or anywhere. In the contemporary era, there are enormous numbers of simulation-based software, which provide excellent results with just one click, thereby resolving the problems within microseconds. Hence, we cannot imagine our present and upcoming future without CFD, which has ultimately made the execution of work easier, leaving behind non-complicating scenarios. Lastly, we can conclude that “CFD is a faster, smarter, and lighter way in designing process.”

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Journal articles on the topic 'Complex real-world problems'

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