In the Semantic Web context, OWL ontologies represent the conceptualization of domains of interest while the corresponding assertional knowledge is given by RDF data referring to them. Because of its open, distributed...
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ISBN:
(纸本)9781450349208
In the Semantic Web context, OWL ontologies represent the conceptualization of domains of interest while the corresponding assertional knowledge is given by RDF data referring to them. Because of its open, distributed, and collaborative nature, such knowledge can be incomplete, noisy, and sometimes inconsistent. By exploiting the evidence coming from the assertional data, we aim at discovering hidden knowledge patterns in the form of multi-relational association rules while taking advantage of the intensional knowledge available in ontological knowledge bases. An evolutionary search method applied to populated ontological knowledge bases is proposed for finding rules with a high inductive power. The proposed method, EDMAR, uses problem-aware genetic operators, echoing the refinement operators of ILP, and takes the intensional knowledge into account, which allows it to restrict and guide the search. Discovered rules are coded in SWRL, and as such they can be straightforwardly integrated within the ontology, thus enriching its expressive power and augmenting the assertional knowledge that can be derived. Additionally, discovered rules may also suggest new axioms to be added to the ontology. We performed experiments on publicly available ontologies, validating the performances of our approach and comparing them with the main state-of-the-art systems.
Parent selection in evolutionary algorithms for multi-objective optimization is usually performed by dominance mechanisms or indicator functions that prefer non-dominated points, while the reproduction phase involves ...
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ISBN:
(纸本)9781450349208
Parent selection in evolutionary algorithms for multi-objective optimization is usually performed by dominance mechanisms or indicator functions that prefer non-dominated points, while the reproduction phase involves the application of diversity mechanisms or other methods to achieve a good spread of the population along the Pareto front. We propose to refine the parent selection on evolutionary multi-objective optimization with diversity-based metrics. The aim is to focus on individuals with a high diversity contribution located in poorly explored areas of the search space, so the chances of creating new non-dominated individuals are better than in highly populated areas. We show by means of rigorous runtime analysis that the use of diversity-based parent selection mechanisms in the Simple evolutionary Multi-objective Optimiser (SEMO) and Global SEMO for the well known bi-objective functions OneMinMax and Lotz can significantly improve their performance. Our theoretical results are accompanied by additional experiments that show a correspondence between theory and empirical results.
In this paper, we address a collaborative learning team formation problem in higher education environments. This problem considers a grouping criterion successfully evaluated in a wide variety of higher education cour...
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ISBN:
(纸本)9783319689357;9783319689340
In this paper, we address a collaborative learning team formation problem in higher education environments. This problem considers a grouping criterion successfully evaluated in a wide variety of higher education courses and training programs. To solve the problem, we propose a hybrid evolutionary algorithm based on adaptive mutation and crossover processes. The behavior of these processes is adaptive according to the diversity of the evolutionary algorithm population. These processes are meant to enhance the evolutionary search. The performance of the hybrid evolutionary algorithm is evaluated on ten different data sets, and then, is compared with that of the best algorithm previously proposed in the literature for the addressed problem. The obtained results indicate that the hybrid evolutionary algorithm considerably outperforms the previous algorithm.
The thermodynamic investigations on the thermoelectric devices (TEs) discard the influence produced by the non-linear Thomson effect. It could direct the incomplete/partial modelling solutions laterally through some c...
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The thermodynamic investigations on the thermoelectric devices (TEs) discard the influence produced by the non-linear Thomson effect. It could direct the incomplete/partial modelling solutions laterally through some critical gaps in the performance evaluation of these devices. On the contrary, a suitable arrangement of several designing constraints for TEs is essential to improve their operating characteristics. In this context, the modeling of multi-element single- and two-stage thermoelectric generators based on the thermodynamic principles is done in MATLAB 9.2. The irreversibility due to Thomson influence along with Joule/Fourier effects are undertaken for the system modelling. The optimization of the generators is done in pursuance of obtaining the optimal values of four input parameters using two different evolutionary algorithms, viz., NSGA-II and MOEA/D. The optimum solutions from the Pareto front of two-/three- objective are found using different decision-making methods, viz., TOPSIS, Fuzzy, and LINMAP. It is observed that the proposed optimization yields trivial variances amongst ideal/obtained solutions, named as the deviation index, in comparison with the single/dual ones. In addition to this, sensitivity analysis is done to examine the impact of Thomson effect on the output power/thermal efficiency of the generators. The test results obtained through NSGA-II are in coherence with those of the data and figures reported in the available literature. Published by AIP Publishing.
The school bus routing problem is a hard, widely studied combinatorial optimization problem. However, little attention has been paid in the literature to the integration between the school bus routing problem and the ...
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The school bus routing problem is a hard, widely studied combinatorial optimization problem. However, little attention has been paid in the literature to the integration between the school bus routing problem and the design of the underlying network. This paper aims to present a new variant of the problem in which the following issues are taken into consideration: the determination of the set of stops to visit, the allocation of students to stops, the generation of routes, and the utilization of a heterogeneous fleet, with different fixed costs and capacities. It is presented as an integer programming formulation, a lower-bound technique, as well a greedy genetic and a memetic algorithm for the heterogenous fleet school bus routing problem (HFSBRP). The integer programming formulation has shown limited application to the solution of large size instances. Computational results on a set of 100 instances provide evidence of the quality of the solutions found by the memetic algorithm on large instances. (C) 2018 American Society of Civil Engineers.
The deployment of smart grid concept at the power distribution level has great potential for improvements to the consumers, e.g., better power quality and higher reliability. The available data allow designing a more ...
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The deployment of smart grid concept at the power distribution level has great potential for improvements to the consumers, e.g., better power quality and higher reliability. The available data allow designing a more efficient control framework aiming to improve operational variables such as power losses and nodal voltage levels. This paper presents a multiobjective control framework for a multifeeder distribution grid, which uses the data gathered from available voltage control devices, power, and current measures, and offline databases. The goal is to determine the best positions of taps in voltage control devices aiming to decrease power losses, switching operations, and fines due to low-quality steady-state voltages in a multiobjective problem. Different academic and real distribution networks with multiple feeders are used to test the proposed framework.
Most third-world locations have a record of very limited types of measured meteorological parameters. In such locations, reliable single-parameter models are disposed to have more applications than the multi-parameter...
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Most third-world locations have a record of very limited types of measured meteorological parameters. In such locations, reliable single-parameter models are disposed to have more applications than the multi-parameter models when solar energy is needed as an explicit function. This fact motivated the effort in this work to improve empirical modeling of solar energy in terms of temperature as the sole predictor. Building on the notion of Hargreaves and Samani that daily solar radiation can sufficiently be modeled empirically in terms of daily T-min and T-max, new temperature-based models for daily solar radiation are proposed. The uniqueness of the proposed models is rooted on the novel inclusion of interactions of T-min and T-max. The proposed models were calibrated and validated for locations in Southern Nigeria and shown to be more statistically reliable for the studied area than the other single-parameter models that are based on air temperature as the sole predictor. The included interactions of T-min and T-max are further verified through ANOVA to improve predictive accuracy. Published by AIP Publishing.
Predicting the propagation of forest fires is a crucial point to mitigate their effects. Therefore, several computational tools or simulators have been developed to predict the fire propagation. Such tools consider th...
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Predicting the propagation of forest fires is a crucial point to mitigate their effects. Therefore, several computational tools or simulators have been developed to predict the fire propagation. Such tools consider the scenario (topography, vegetation types, fire front situation), and the particular conditions where the fire is evolving (vegetation conditions, meteorological conditions) to predict the fire propagation. However, these parameters are usually difficult to measure or estimate precisely, and there is a high degree of uncertainty in many of them. This uncertainty provokes a certain lack of accuracy in the predictions with the consequent risks. So, it is necessary to apply methods to reduce the uncertainty in the input parameters. This work presents a comparison of ESSIM-EA and ESSIM-DE: two methods to reduce the uncertainty in the input parameters. These methods combine evolutionary algorithms, Parallelism and Statistical Analysis to improve the propagation prediction. (C) 2017 The Authors. Published by Elsevier B.V.
Optimal control problems with multiple conflicting objectives in chemical processes are quite challenging. To solve such problems, we put forward a multistrategy-based multiobjective differential evolution, in which (...
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Optimal control problems with multiple conflicting objectives in chemical processes are quite challenging. To solve such problems, we put forward a multistrategy-based multiobjective differential evolution, in which (1) a hybrid selection strategy is incorporated from the motivation of no single strategy outperforming all other ones in every stage;(2) a multipopulation strategy is applied to represent the main population and current optimum, and a cyclic crowding estimation is developed to maintain these optimum;and (3) a multimutation strategy is constructed to improve both exploration and exploitation ability. The effectiveness and efficiency of the proposed algorithm are validated by comparisons with some representative multiobjective evolutionary algorithms over 12 test instances. Moreover, the proposed algorithm is applied to solve 3 multiobjective optimal control problems in chemical processes. The obtained results indicate the efficiency and effectiveness of the proposed algorithm for solving multiobjective optimal control problems.
Simulation-optimization frameworks, such as multiobjective evolutionary algorithms (MOEAs), are increasingly used for real-world water resources problems. Constraints in MOEA optimization commonly represent decision m...
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Simulation-optimization frameworks, such as multiobjective evolutionary algorithms (MOEAs), are increasingly used for real-world water resources problems. Constraints in MOEA optimization commonly represent decision maker preference, which differs from their role in classical optimization. As a result, constraints are often considered an optional aspect of the problem formulation. However, the impact of including constraints on optimization search has not been rigorously examined. This study explores how constraints impact the effectiveness, efficiency, and consistency of MOEA optimization for two water resources problems. For each problem, algorithm performance metrics are compared for two cases: (1)with constraints included during search, eliminating solutions that do not meet preference requirements, and (2)with constraints applied a posteriori to filter the full set of solutions. Results show that constraints aid in the search process by favoring solutions that meet decision maker preferences, despite the increased difficulty of finding feasible solutions. This study highlights the importance of constraints in the problem formulation for simulation-optimization applications in water resources, balancing the performance of search algorithms with the decision relevance of the solution set.
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