This paper demonstrates multiobjective optimization of a multi cross-section pin fin heat sink (MCSPFHS) for use in electronic devices. The design problem is assigned to optimize junction temperature and fan pumping p...
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This paper demonstrates multiobjective optimization of a multi cross-section pin fin heat sink (MCSPFHS) for use in electronic devices. The design problem is assigned to optimize junction temperature and fan pumping power of the heat sink. Design variables are encoded to shape the fin geometry with several pin fin cross-sections. The heat sink is set to be side-inlet-side-outlet (SISO) while several multiobjective evolutionary algorithms (MOEAs) including hybrid real code population-based incremental learning and differential evolution (PBIL-DE), second version of strength Pareto evolutionary algorithm (SPEA2), and unrestricted population size evolutionarymultiobjective optimization algorithm (UPSEMOA) are applied to solve the bi-objective optimization problem. The results obtained are superior to those conventional pin fin heat sinks. (C) 2017 Elsevier Ltd. All rights reserved.
Urban waste collection is an important problem in modern cities, where efficient techniques are demanded to reduce large budgetary expenses, and avoid environmental and social problems. This article presents two state...
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This paper addresses the problem of planning and allocation of trucks in open-pit mines in terms of three conflicting objectives, and adapts three algorithms for its solution: NSGA-II, SPEA2, and a variant of the Pare...
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ISBN:
(纸本)9781450349390
This paper addresses the problem of planning and allocation of trucks in open-pit mines in terms of three conflicting objectives, and adapts three algorithms for its solution: NSGA-II, SPEA2, and a variant of the Pareto Iterated Local Search using Reduced Variable Neighborhood Search as its local exploration mechanism. Results on four different mining scenarios are also reported and compared.
This paper presents a comparative study of six multiobjective evolutionary algorithms (MOEAs) with the node-depth encoding (NDE) which have been used to solve the service restoration in distribution systems. The study...
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This paper presents a comparative study of six multiobjective evolutionary algorithms (MOEAs) with the node-depth encoding (NDE) which have been used to solve the service restoration in distribution systems. The study has been divided into three steps: (1) the MOEAs have been evaluated taking into account the switching operations necessary to find adequate restoration plans considering multiple nonlinear constraints and objective functions;(2) the MOEAs have been employed to solve four different datasets with 3860, 7720, 15,440 and 30,880 buses, respectively;(3) comparisons have been performed using the hypervolume indicator and the results obtained with each approach are statistically compared using Kruskal-Wallis nonparametric tests and multiple comparisons. In addition, this paper provides a comprehensive evaluation of six combinations of MOEAs based on NDE and our objective is to identify the features of each approach that consistently produce best results applied to network reconfiguration for service restoration in distribution systems. Simulations results have shown that MOEA based on NDE with crowding distance and strength pareto found good configurations with low switching operations and explored the search space better than others approaches used in this paper, approximating better the pareto-optimal front.
multiobjectiveevolutionary clustering algorithms are based on the optimization of several objective functions that guide the search following a cycle based on evolutionaryalgorithms. Their capabilities allow them to...
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multiobjectiveevolutionary clustering algorithms are based on the optimization of several objective functions that guide the search following a cycle based on evolutionaryalgorithms. Their capabilities allow them to find better solutions than with conventional clustering algorithms when more than one criterion is necessary to obtain understandable patterns from the data. However, these kind of techniques are expensive in terms of computational time and memory usage, and specific strategies are required to ensure their successful scalability when facing large-scale data sets. This work proposes the application of a data subset approach for scaling-up multiobjective clustering algorithms and it also analyzes the impact of three stratification methods. The experiments show that the use of the proposed data subset approach improves the performance of multiobjectiveevolutionary clustering algorithms without considerably penalizing the accuracy of the final clustering solution. (C) 2016 Elsevier B.V. All rights reserved.
Changes in demand when manufacturing different products require an optimization model that includes robustness in its definition and methods to deal with it. In this work we propose the r-TSALBP, a multiobjective mode...
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Changes in demand when manufacturing different products require an optimization model that includes robustness in its definition and methods to deal with it. In this work we propose the r-TSALBP, a multiobjective model for assembly line balancing to search for the most robust line configurations when demand changes. The robust model definition considers a set of demand scenarios and presents temporal and spatial overloads of the stations in the assembly line of the products to be assembled. We present two multiobjective evolutionary algorithms to deal with one of the r-TSALBP variants. The first algorithm uses an additional objective to evaluate the robustness of the solutions. The second algorithm employs a novel adaptive method to evolve separate populations of robust and non-robust solutions during the search. Results show the improvements of using robustness information during the search and the outstanding behavior of the adaptive evolutionary algorithm for solving the problem. Finally, we analyze the managerial impacts of considering the r-TSALBP model for the different organization departments by exploiting the values of the robustness metrics. (C) 2015 Elsevier Ltd. All rights reserved.
In this paper, a multiobjective optimization model is formulated to determine the optimal preventive maintenance plans for a repairable multicomponent manufacturing system with increasing rate of occurrence of failure...
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In this paper, a multiobjective optimization model is formulated to determine the optimal preventive maintenance plans for a repairable multicomponent manufacturing system with increasing rate of occurrence of failure. The operational planning horizon is segmented into discrete and equally sized periods, and in each period, three possible maintenance actions (repair, replacement, or do nothing) have been considered for each component. The developed multiobjective nonlinear mixed-integer programming model is then utilized to find the Pareto-optimal preventive maintenance schedules for a computer numerical control (CNC) machine. In order to obtain the Pareto-optimal solutions, five multiobjective evolutionary algorithms are considered, and computational performance of the algorithms is evaluated using different metrics. It is found that three of these algorithms outperform the others in obtaining more and high-quality nondominated preventive maintenance and replacement schedules along with maintaining high level of diversity among generated solutions. Such a modeling approach and the comparison of algorithms would be useful for maintenance planners tasked with the problem of developing optimal maintenance plans for multicomponent manufacturing machines.
Local Indicators of Spatial Aggregation (LISA) can be used as objectives in a multicriteria framework when highly autocorrelated areas (hot-spots) must be identified and geographically located in complex areas. To do ...
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Local Indicators of Spatial Aggregation (LISA) can be used as objectives in a multicriteria framework when highly autocorrelated areas (hot-spots) must be identified and geographically located in complex areas. To do so, a Multi-Objective evolutionary Algorithm (MOEA) based on SPEA2 (Strength Pareto evolutionary Algorithm v.2) has been designed to evaluate three different fitness functions (fine-grained strength, the weighted sum of objectives and fuzzy evaluation of weighted objectives) and three LISA methods. MOEA makes it possible to achieve a compromise between spatial econometric methods as it highlights areas where a specific phenomenon shows significantly high autocorrelation. The spatial distribution of financially compromised olive-tree farms in Andalusia (Spain) was selected for analysis and two fuzzy hot-spots were statistically identified and spatially located. Hot-spots can be considered to be spatial fuzzy sets where the spatial units have a membership degree that can also be calculated.
This paper is the second part of a two-part paper, which is a survey of multiobjective evolutionary algorithms for data mining problems. In Part I [1], multiobjective evolutionary algorithms used for feature selection...
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This paper is the second part of a two-part paper, which is a survey of multiobjective evolutionary algorithms for data mining problems. In Part I [1], multiobjective evolutionary algorithms used for feature selection and classification have been reviewed. In this part, different multiobjective evolutionary algorithms used for clustering, association rule mining, and other data mining tasks are surveyed. Moreover, a general discussion is provided along with scopes for future research in the domain of multiobjective evolutionary algorithms for data mining.
The aim of any data mining technique is to build an efficient predictive or descriptive model of a large amount of data. Applications of evolutionaryalgorithms have been found to be particularly useful for automatic ...
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The aim of any data mining technique is to build an efficient predictive or descriptive model of a large amount of data. Applications of evolutionaryalgorithms have been found to be particularly useful for automatic processing of large quantities of raw noisy data for optimal parameter setting and to discover significant and meaningful information. Many real-life data mining problems involve multiple conflicting measures of performance, or objectives, which need to be optimized simultaneously. Under this context, multiobjective evolutionary algorithms are gradually finding more and more applications in the domain of data mining since the beginning of the last decade. In this two-part paper, we have made a comprehensive survey on the recent developments of multiobjective evolutionary algorithms for data mining problems. In this paper, Part I, some basic concepts related to multiobjective optimization and data mining are provided. Subsequently, various multiobjectiveevolutionary approaches for two major data mining tasks, namely feature selection and classification, are surveyed. In Part II of this paper, we have surveyed different multiobjective evolutionary algorithms for clustering, association rule mining, and several other data mining tasks, and provided a general discussion on the scopes for future research in this domain.
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