evolutionaryalgorithm has gained a worldwide popularity among multi-objective optimization. This paper proposes a novel multi-objective evolutionary algorithm based on the fuzzy similarity measure. First, the best so...
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ISBN:
(纸本)0878492712
evolutionaryalgorithm has gained a worldwide popularity among multi-objective optimization. This paper proposes a novel multi-objective evolutionary algorithm based on the fuzzy similarity measure. First, the best solution of every objective among the multi-objectives is obtained and they are regarded on as the referenced vector. Second, the fuzzy similarity measure between every individual and the referenced vector is solved and the fuzzy similarity measure is acted as fitness of the individual. Moreover, the pareto optimal sets are solved by means of adaptive genetic algorithm. The variety of population is kept by means of adaptive probability of crossover and mutation. At last, the algorithm is used to optimize the design parameters of cylinder helical compression spring. Simulation examples show the effectiveness of the approach proposed.
evolutionaryalgorithms are stochastic heuristics which can optimize over special functions, known as fitness functions, by manipulating the structure of candidate solutions known as individuals. multi-objective evolu...
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evolutionaryalgorithms are stochastic heuristics which can optimize over special functions, known as fitness functions, by manipulating the structure of candidate solutions known as individuals. multi-objective evolutionary algorithms can deal with many objectives to be optimized, whether concurrent or divergent, ending by returning an optimal frontier, i.e. a set of solutions all defined as Pareto optimal. The idea behind using evolutionaryalgorithms to perform computer automated design is to be able to formulate a fitness function that, starting from a candidate solution, could reflect the impact the evaluated individual has on those objectives to be optimized. The case study presented in this work is an application for computer automated exterior lighting design which has some concurrent objectives to be optimized: the energy efficiency and the illumination quality. This work investigates four metrics to illumination quality and two metrics for energy efficiency as possible proposals to the fitness function formulation. Eight variations were designed as combinations of pairs from those metrics. To help in the decision process, the statistical hypothesis test known as difference of means is then used to enable comparisons between those variations. This test is performed two by two and three decision matrices is then derived, the ones about global uniformity, mean electrical power, and mean efficiency class index. The concept of Pareto's "statistical dominance", defined in this work and based on statistical evidences, indicates a final decision about which one from the previous designed variations of fitness function is the more appropriated for the presented problem.
Selection methods are a key component of all multi-objective and, consequently, many-objective optimisation evolutionaryalgorithms. They must perform two main tasks simultaneously. First of all, they must select indi...
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Selection methods are a key component of all multi-objective and, consequently, many-objective optimisation evolutionaryalgorithms. They must perform two main tasks simultaneously. First of all, they must select individuals that are as close as possible to the Pareto optimal front (convergence). Second, but not less important, they must help the evolutionary approach to provide a diverse population. In this paper, we carry out a comprehensive analysis of state-of-the-art selection methods with different features aimed to determine the impact that this component has on the diversity preserved by well-known multi-objective optimisers when dealing with many-objective problems. The algorithms considered herein, which incorporate Pareto-based and indicator based selection schemes, are analysed through their application to the Walking Fish Group (WFG) test suite taking into account an increasing number of objective functions. algorithmic approaches are assessed via a set of performance indicators specifically proposed for measuring the diversity of a solution set, such as the Diversity Measure and the Diversity Comparison Indicator. Hypervolume, which measures convergence in addition to diversity, is also used for comparison purposes. The experimental evaluation points out that the reference-point-based selection scheme of the Non-dominated Sorting Genetic algorithm III (NSGA-III) and a modified version of the Non-dominated Sorting Genetic algorithm II (NSGA-II), where the crowding distance is replaced by the Euclidean distance, yield the best results. (C) 2017 The Authors. Published by Elsevier B.V.
In this paper, we address multi-step ahead time series Prediction Intervals (PI). We extend two Neural Network (NN) methods, Lower Upper Bound Estimation (LUBE) and multi-objective evolutionary algorithm (MOEA) LUBE (...
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ISBN:
(纸本)9783319653402;9783319653396
In this paper, we address multi-step ahead time series Prediction Intervals (PI). We extend two Neural Network (NN) methods, Lower Upper Bound Estimation (LUBE) and multi-objective evolutionary algorithm (MOEA) LUBE (MLUBE), for multi-step PI. Furthermore, we propose two new MOEA methods based on a 2-phase gradient and MOEA based learning: M2LUBET1 and M2LUBET2. Also, we present a robust evaluation procedure to compare PI methods. Using four distinct seasonal time series, we compared all four PI methods. Overall, competitive results were achieved by the 2-phase learning methods, in terms of both predictive performance and computational effort.
Transportation systems of the future need to be adaptive, adoptive, and responsive in order to meet the diverse challenges and ever-evolving demands. Conventional method of adding more resources on the road does not e...
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ISBN:
(纸本)9789897582424
Transportation systems of the future need to be adaptive, adoptive, and responsive in order to meet the diverse challenges and ever-evolving demands. Conventional method of adding more resources on the road does not enhance its utility, rather it creates traffic congestion. Optimization of the usage of existing resources has been found to be one of the most effective solution to manage traffic congestion. The method we propose consists in increasing the occupancy rate of each vehicle and utilize other untapped resources in existing infrastructure. The resource optimization problem studied in this paper is NP hard, due to the vehicle routing and resource matching problem. In this paper, we are focused on developing a multi-objective evolutionary algorithm to optimize the use of taxi service not just as a carrier for people but also as a transport system for parcel delivery. Preliminary experiment with real-world data shows that our approach is able to quickly produce satisfactory solutions and the algorithm is able to provide an average of 17.7% improvement over conventional methods.
Microcontroller-based systems often include peripheral devices such as matrix keyboard and character LCD module among others. We propose the application of the multi-objective linear genetic programming, for automatic...
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ISBN:
(纸本)9781538626528
Microcontroller-based systems often include peripheral devices such as matrix keyboard and character LCD module among others. We propose the application of the multi-objective linear genetic programming, for automatic generation of the assembly driver routines for these devices, to perform the operations: matrix keyboard scan, LCD module initialization and character display on LCD. For fitness evaluation, we assign a function to be maximized to each bit of the binary result or to the timing diagram of each used microcontroller Port pins. This decomposition of the problem used in a multi-objective evolutionary algorithm allows generating programs, in some cases, with smaller code size or shorter execution time than programs written by a human programmer.
The Maximum Diversity (MD) problem is the process of selecting a subset of elements where the diversity among selected elements is maximized. Several diversity measures were already studied in the literature, optimizi...
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ISBN:
(纸本)9781538630570
The Maximum Diversity (MD) problem is the process of selecting a subset of elements where the diversity among selected elements is maximized. Several diversity measures were already studied in the literature, optimizing the problem considered in a pure mono-objective approach. This work presents for the first time multi-objective approaches for the MD problem, considering the simultaneous optimization of the following five diversity measures: (i) Max-Sum, (ii) Max-Min, (iii) Max-MinSum, (iv) Min-Diff and (v) Min-P-center. Two different optimization models are proposed: (i) multi-objective Maximum Diversity (MMD) model, where the number of elements to be selected is defined a-priori, and (ii) multi-objective Maximum Average Diversity (MMAD) model, where the number of elements to be selected is also a decision variable. To solve the formulated problems, a multi-objective evolutionary algorithm (MOEA) is presented. Experimental results demonstrate that the proposed MOEA found good quality solutions, i.e. between 89.20% and 99.92% of the optimal Pareto front when considering the hyper-volume for comparison purposes.
Restoring the hydrologic flow regime of urban areas by promoting infiltration, retention, and evapotranspiration on the site is one of the goals of low-impact development (LID). These goals can be achieved through the...
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Restoring the hydrologic flow regime of urban areas by promoting infiltration, retention, and evapotranspiration on the site is one of the goals of low-impact development (LID). These goals can be achieved through the implementation of stormwater control measures (SCMs) such as green roofs and permeable pavements. The effectiveness of SCMs can be influenced not only by their design but also by their location. The present study applies multi-objectiveevolutionary optimization and Monte Carlo simulation approaches to help identify nearoptimal locations of green roofs and permeable pavements in the catchment scale. The Nondominated Sorting Genetic algorithm II was connected to the stormwater management model (SWMM) to identify the location of SCMs and characterize the tradeoffs between flow regime alteration and implementation costs. The impact of implementing SCMs is measured by peak flow, runoff volume, and the hydrologic footprint residence (HFR). The HFR is a new stormwater metric that represents dynamics of inundated areas and residence time of flood waves throughout downstream segments. The approach was tested in an illustrative case study of an 11.7-ha urban catchment divided into five subcatchments. The results indicate that locating SCMs in downstream subcatchments can reduce peak flow more effectively, whereas SCMs placed in upstream subcatchments better reduce the HFR. The proposed methodology can help stormwater managers to better assess the combined performance of LID-SCMs in different hydrologic scales and generate guidelines for prioritizing the implementation or retrofitting of urban areas with green infrastructure. (C) 2017 American Society of Civil Engineers.
In this paper we describe a multi-objective genetic programming algorithm which can be used to create complete machine learning workflows. The algorithm is an extension of a single-objective one. In a series of test o...
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ISBN:
(纸本)9781538627266
In this paper we describe a multi-objective genetic programming algorithm which can be used to create complete machine learning workflows. The algorithm is an extension of a single-objective one. In a series of test on four datasets, we show that the additional objectives can be used to search for smaller or faster models. The algorithm is also in some cases much faster than the single-objective one while obtaining results of similar quality.
Location recommendation has attracted increasing attention in recent years. This paper proposes a novel multi-objective framework for location recommendation based on user preference. Under this framework, user prefer...
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ISBN:
(纸本)9781538648223
Location recommendation has attracted increasing attention in recent years. This paper proposes a novel multi-objective framework for location recommendation based on user preference. Under this framework, user preference can be separated into common preference and individual preference. Then two contradictory objective functions are designed to describe these two kinds of preferences. It is difficult to optimize these two objective functions simultaneously. In this paper, a novel multi-objective evolutionary algorithm is proposed to optimize these two objective functions. The proposed algorithm can make a good balance between these two objective functions. Experiments on two real application recommendation scenarios: Foursquare dataset and Gowalla dataset show that the proposed algorithm is effective to recommend locations.
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