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8th International Conference on Flexible Query Answering Systems

作者： Cecchini, Rocio L. Lorenzetti, Carlos M. Maguitman, Ana G. Univ Nacl Sur Dept Ciencias & Ingn Computac RA-8000 Bahia Blanca Buenos Aires Argentina

ISBN: (纸本)9783642049569

Formulating search queries based on a thematic context is a challenging problem due to the large number of combinations in which terms can be used to reflect the topic of interest. This paper presents a novel approach to learn topical queries that simultaneously satisfy multiple retrieval objectives. The proposed method consists in using a topic ontology to train an evolutionary Algorithm that incrementally moves a population of queries towards the proposed objectives. We present an analysis of different single- and multi-objective strategies, discuss their strengths and limitations and test the most promising strategies on a large set of labeled Web pages. Our evaluations indicate that the tested strategies that apply multi-objective evolutionary algorithms are significantly superior to a baseline approach that attempts to generate queries directly from a topic description.

关键词： topic ontologies topical queries semantic similarity multi-objective evolutionary algorithms query effectiveness

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An Algorithm for Calculating the Hypervolume Contribution of a Set

An Algorithm for Calculating the Hypervolume Contribution of...

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World Automation Congress (WAC)

作者： Zhou, Xiuling Guo, Ping Chen, C. L. Philip Beijing Normal Univ Image Proc & Pattern Recognit Lab Beijing 100875 Peoples R China Beijing Normal Univ Artificial Intelligence Inst Beijing Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Uni Macau Fac Sci & Technol Macau Peoples R China

ISBN: (纸本)9781467344975

The reliability model can be optimized with a multi-objective optimization algorithm, while hypervolume-based multi-objective evolutionary algorithms (MOEAs) have been shown to produce better results for multi-objective problem in practice. When hypervolume is used in some MOEAs as archiving strategy, diversity mechanism or selection criterion to guide the search, it is necessary to determine which subset contributes the least hypervolume contribution. Few algorithms have been designed for this purpose. In this paper a new algorithm based on HSO (hypervolume by slicing objective) is proposed for calculating the exclusive hypervolume contributions of each subset to the whole nondominated set directly for small dimension. The new algorithm is composed of two parts: the algorithm SHSO (set hypervolume contribution by slicing objective) and the algorithm SHSO*. SHSO is used to calculate the exclusive hypervolume contribution of a subset to the whole nondominated set. SHSO* is applied to select the subset which contributes the least hypervolume contribution by repeated application of SHSO. Compared with HSO adopted for calculating the exclusive hypervolume contribution, SHSO* outperforms HSO for all of the test fronts with small dimension.

关键词： Reliability model multi-objective evolutionary algorithms Hypervolume contribution

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Inconstant Update of Reference Point Value for Parallel and Distributed MOEA/D

Inconstant Update of Reference Point Value for Parallel and ...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Sato, Mikiko Sato, Yuji Midtlyng, Mads Miyakawa, Minami Tokai Univ Sch Informat & Telecommun Engn Tokyo Japan Hosei Univ Fac Comp & Informat Sci Tokyo Japan Hosei Univ Grad Sch Comp & Informat Sci Tokyo Japan Shinshu Univ Fac Engn Nagano Japan

ISBN: (纸本)9781728183923

This paper proposes an update method to determine the reference point and nadir point when speeding up multi-objective evolutionary optimization algorithm based on decomposition (MOEA/D) with parallel and distributed processing in a many-core environment. We have already proposed a migration method that uses a virtual overlapping zone between partitions. Compared with simply partitioning the objective space or applying the standard island migration model, this method is shown to be effective in improving the hypervolume (HV) value, increasing diversity, and reducing regions where non-dominated solutions do not exist between partitions (sparse areas). However, this proposed method could not completely deal with the sparse areas' problem in the early stage of solution searching. To resolve this problem, in addition to the virtual overlap method, we propose here a method that switches the update interval for determining the reference point and nadir point by taking into account the progress of solution searching. To evaluate our newly proposed method, we compare both convergence and diversity on single-core MOEA/D and parallel MOEA/D using a two-objective constrained knapsack problem, and show that our method is effective not only in reducing sparse areas in the early stage but also in improving diversity and increasing the HV value.

关键词： MOEA/D parallel and distributed processing reference point multi-objective evolutionary algorithms

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Towards a More Balanced Reference Set Adaptation Method: First Results

Towards a More Balanced Reference Set Adaptation Method: Fir...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Marquez-Vega, Luis A. Guillermo Falcon-Cardona, Jesus Covantes Osuna, Edgar Tecnol Monterrey Sch Engn & Sci Monterrey Nuevo Leon Mexico UAM Cuajimalpa Dept Appl Math & Syst Mexico City DF Mexico

ISBN: (纸本)9781728183923

Reference sets are widely used by many multi-objective evolutionary algorithms (MOEAs) to decompose the objective space, define search directions, or calculate quality indicators (QIs) embedded into the selection mechanisms. Well-known MOEAs adopt the generation of uniformly distributed points on a unit simplex to construct such reference sets. Although these mechanisms are useful for approximating Pareto fronts with regular shapes, i.e., simplex-like shapes, they have difficulties representing Pareto fronts with irregular geometries. To overcome this drawback, many reference set adaptation methods have been proposed so far. However, some adaptation methods present a degraded performance on regular Pareto front shapes, while others promote a balanced performance. Nevertheless, an extensive assessment has not been made. In this paper, a MOEA based on the inverted generational distance plus indicator, using an adaptive reference set, is used to study the performance of well-known adaptation methods. Although an adaptation method promotes a balanced performance on both regular and irregular Pareto front shapes, results show some difficulties related to the distribution of solutions in complex Pareto front shapes. The results of this study allow detecting the main drawbacks of adaptation methods, which can be addressed by using diversity-oriented selection mechanisms in the generation of reference sets. Hence, these could impact the generation of reference set-based MOEAs achieving good coverage, convergence, and diversity regardless of the Pareto front shape.

关键词： multi-objective evolutionary algorithms reference set adaptation methods irregular Pareto front shapes

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A multi-objective Genetic Local Search Algorithm for Optimal Feature Subset Selection

A Multi-objective Genetic Local Search Algorithm for Optimal...

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International Conference on Computational Science and Computational Intelligence (CSIC)

作者： Tian, David Leeds Beckett Univ Leeds Sustainabil Inst Leeds LS1 3HE W Yorkshire England

ISBN: (纸本)9781509055104

Feature selection algorithms select the most relevant features of a data set to improve the classification performance of the machine learning classifiers trained using the data set. This paper proposes a feature selection algorithm called multi-objective genetic local search (MOGLS) which integrates a 3-objective genetic algorithm with a local search heuristic to find feature subsets with the maximum prediction accuracy, the smallest sizes and the minimum redundancy. The performance of MOGLS is compared with 4 algorithms: a wrapper genetic algorithm, correlation-based feature selection, mutual information ranking and C4.5 on 8 datasets from the UCI machine learning repository. MOGLS performs better than or as good as the 4 algorithms on the 8 datasets.

关键词： Machine learning technologies multi-objective evolutionary algorithms Genetic algorithms Metaheuristic optimization algorithms Fuzzy and rough data analysis

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multi-objective Design Optimization of Three-Phase Induction Motor Using NSGA-II Algorithm 1

Multi-objective Design Optimization of Three-Phase Induction...

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1st International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Ranjan, Soumya Mishra, Sudhansu Kumar NIST Dept Elect & Elect Engn Berhampur Orissa India Birla Inst Technol Dept Elect & Elect Engn Ranchi Bihar India

ISBN: (纸本)9788132222088;9788132222071

The modeling of electrical machine is approached as a system optimization, more than a simple machine sizing. Hence wide variety of designs are available and the task of comparing the different options can be very difficult. A number of parameters are involved in the design optimization of the induction motor and the performance relationship between the parameters also is implicit. In this paper, a multi-objective problem is considered in which three phase squirrel cage induction motor (SCIM) has been designed subject to the efficiency and power density as objectives. The former is maximized where the latter is minimized simultaneously considering various constraints. Three single objective methods such as Tabu Search (TS), Simulated Annealing (SA) and Genetic Algorithm (GA) is used for comparing the Pareto solutions. Performance comparison of techniques is done by performing different numerical experiments. The result shows that NSGA-II outperforms other three for the considered test cases.

关键词： multi-objective optimization Induction motors multi-objective evolutionary algorithms Single objective evolutionary algorithm

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COMPARISON OF COMPUTATIONAL EFFICIENCY OF MOEA\D AND NSGA-II FOR PASSIVE VEHICLE SUSPENSION OPTIMIZATION

COMPARISON OF COMPUTATIONAL EFFICIENCY OF MOEA\D AND NSGA-II...

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24th European Conference on Modelling and Simulation

作者： Yuen, Tey Jing Ramli, Rahizar Univ Malaya Dept Mech Engn Kuala Lumpur 50603 Malaysia

ISBN: (纸本)9780956494405

This paper evaluates new optimization algorithms for optimizing automotive suspension systems employing stochastic methods. This method is introduced as an alternative over the conventional approach, namely trial and error, or design of experiment (DOE), to efficiently optimize the suspension system. Optimizations algorithms employed are the multi-objective evolutionary algorithms based on decomposition (MOEA\D), and non-sorting genetic algorithm II (NSGA-II). A two-degree-of-freedom (2- DOF) linear quarter vehicle model (QVM) traversing a random road profile is utilized to describe the ride dynamics. The road irregularity is assumed as a Gaussian random process and represented as a simple exponential power spectral density (PSD). The evaluated performance indices are the discomfort parameter (ACC), suspension working space (SWS) and dynamic tyre load (DTL). The optimised design variables are the suspension stiffness, K-s and damping coefficient, C-s. In this paper, both algorithms are analyzed with different sets of experiments to compare their computational efficiency. The results indicated that MOEA\D is computationally efficient in searching for Pareto solutions compared to NSGA-II, and showed reasonable improvement in ride comfort.

关键词： Passive suspension optimization multi-objective evolutionary algorithms genetic algorithms quarter vehicle model ride comfort

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multiobjective Discrete Differential Evolution for Service Restoration in Energy Distribution Systems

Multiobjective Discrete Differential Evolution for Service R...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Sanches, Danilo S. London, Joao Bosco A., Jr. Delbem, Alexandre C. B. Univ Tecnol Fed Parana Cornelio Procopio PR Brazil Univ Sao Paulo Sao Carlos Sch Engn Sao Carlos SP Brazil Univ Sao Paulo Inst Math & Comp Sci Sao Carlos SP Brazil

ISBN: (纸本)9781450343237

This paper presents a new multiobjective discrete differential evolution for service restoration in distribution systems. The proposed approach was compared with other five multiobjective evolutionary algorithms (MOEAs), which use Node-Depth Encoding (NDE). The proposed approach have been evaluated taking into account the switching operations necessary to find adequate restoration plans considering multiple non-linear constraints and objective functions. The MOEAs used in this paper have been employed to solve four different datasets with 3,860, 7,720, 15,440 and 30,880 buses, respectively. Simulations results have shown that proposed approach reached good solutions with low switching operations and reduced running time when compared with others MOEAs.

关键词： Large-Scale Distribution Systems Service Restoration Node-depth Encoding multi-objective evolutionary algorithms Differential Evolution

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Analyzing the Performance of a Two-Tail-Measures-Utility multi-objective Portfolio Optimization Model

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Operations Research Forum 2021年第4期2卷 1-18页

作者： Mamanis, Georgios CGSoft Athens Greece

This research paper proposes and experimentally investigates the out-of-sample performance of a multi(three)-objective portfolio optimization model. The three objectives used to evaluate the return distribution of the portfolio are two tail performance measures and a utility function in order to evaluate the middle part of the return distribution. Five different utility functions are considered, thus forming five instances of the proposed multi-objective portfolio selection model. For solving the problem, a multi-objective evolutionary algorithm, namely Strength Pareto evolutionary Algorithm 2 (SPEA2), was employed. The results show that the majority of the portfolios generated by the solution technique produce better portfolios than S&P 500 Index considering the final wealth, Sharpe ratio, and Sortino ratio. The five portfolio models defined by different utility functions return, on average, approximately 10% more than the return on S&P 500 Index for an evaluation period of one and a half year. Furthermore, the computational results show that the proposed multi-objective portfolio optimization models are competitive to portfolios that have shown good out-of-sample performance in past studies, like the minimum variance portfolio with and without short sales and the second-order stochastic dominance portfolio. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： multi-objective evolutionary algorithms multi-objective optimization Portfolio optimization Portfolio selection Utility functions

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Revisiting the NSGA-II Crowding-Distance Computation 13

Revisiting the NSGA-II Crowding-Distance Computation

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15th Genetic and evolutionary Computation Conference (GECCO)

作者： Fortin, Felix-Antoine Parizeau, Marc Univ Laval Dept Genie Elect & Genie Informat Lab Vis & Syst Numer Quebec City PQ G1V 0A6 Canada

ISBN: (纸本)9781450319638

This paper improves upon the reference NSGA-II procedure by removing an instability in its crowding distance operator. This instability stems from the cases where two or more individuals on a Pareto front share identical fitnesses. In those cases, the instability causes their crowding distance to either become null, or to depend on the individual's position within the Pareto front sequence. Experiments conducted on nine different benchmark problems show that, by computing the crowding distance on unique fitnesses instead of individuals, both the convergence and diversity of NSGA-II can be significantly improved.

关键词： multi-objective evolutionary algorithms NSGA-II crowding distance

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