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检索条件"主题词=Multi-objective evolutionary optimization"

共 86 条记录，以下是61-70 订阅

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Improved Wireless Localization of Mobile Devices in Smart Indoor Scenarios 15

Improved Wireless Localization of Mobile Devices in Smart In...

引用

2015 IEEE 15th Mediterranean Microwave Symposium (MMS)

作者： Moriyama, T. Polo, A. Viani, F. Giarola, E. Massa, A. Nagasaki Univ Grad Sch Engn 1-14 Bunkyo Machi Nagasaki 8528521 Japan Univ Trento ELEDIA Res Ctr DISI I-38123 Trento Italy

ISBN: (纸本)9781467376020

In this paper, a method for improving wireless localization of active targets in indoor environments is proposed. The positioning problem is recast to an inverse problem and solved by means of multi-objective evolutionary optimization. The first objective considers received signal strength wireless positioning based on a theoretical propagation model. The second objective relies on probabilistic information that takes into account a-priori knowledge about the environment and semantic profile inferred from the target. The system has been implemented and experimentally validated in a real scenario using smart-phones and the existing Wi-Fi network infrastructure.

关键词： wireless localization and tracking indoor positioning received signal strength semantic localization multi-objective evolutionary optimization

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Gene-Promoter-Sequence Recognition - an Interpretable and Accurate Fuzzy-Genetic Approach

Gene-Promoter-Sequence Recognition - an Interpretable and Ac...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Gorzalczany, Marian B. Rudzinski, Filip Kielce Univ Technol Dept Elect & Comp Engn Al 1000 Lecia PP 7 PL-25314 Kielce Poland

ISBN: (纸本)9781538617281

Different methods applied to gene promoter recognition share, in general, the same drawback, i.e., they are non-interpretable black-box-type techniques. The main objective of this paper is the application of our fuzzy rule-based classification approach characterized by genetically optimized accuracy-interpretability trade off (using multi-objective evolutionary optimization algorithms (M-OEOAs)) to gene promoter recognition. Two publicly accessible bacterial DNA benchmark data sets, i.e., Molecular Biology (Promoter Gene Sequences) and iPromoter-FSEn benchmark data sets are considered. For comparison purposes, two M-OEOAs are used in our experiments, i.e., the well-known Strength Pareto evolutionary Algorithm 2 (SPEA2) and our generalization of SPEA2 (referred to as SPEA3) characterized by a higher spread and better-balanced distribution of solutions. Our results for both considered molecular biology data sets are compared with the results of 16 alternative methods (including several state-of-the-art ones) demonstrating the advantages - in terms of system's accuracy-interpretability trade-off optimization - of our approach.

关键词： bioinformatics gene-promoter-sequence recognition fuzzy rule-based classifiers multi-objective evolutionary optimization accuracy-interpretability trade-off optimization

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A Novel multi-objective Cultural Algorithm Embedding Five-Element Cycle optimization

A Novel Multi-objective Cultural Algorithm Embedding Five-El...

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IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Mao, Zhengyan Xiang, Yue Zhang, Yijie Liu, Mandan East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781728169293

The cultural algorithm, as a dual-inheritance framework designed for optimization problems, can incorporate any population-adopted evolutionary computation technique in its population space. On the other hand, based on the Five-Elements Cycle Model derived from the ancient Chinese Five Elements (metal, wood, water, fire, earth) theory, the five-elements cycle optimization algorithm was proved to be effective in solving continuous function optimization problems. In this work, we propose a multi-objective cultural algorithm with a five-elements-cycle-optimization-based population space, where the five-element cycle model is adopted as the evolution scheme in the population space of the cultural algorithm framework. Simulation results on 12 classic benchmark problems show that the proposed algorithm can effectively solve continuous optimization functions and obtains satisfactory non-dominated solutions compared with 8 representative multi-objective algorithms.

关键词： multi-objective evolutionary optimization cultural algorithm five-elements cycle model

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New evolutionary Method for Studying Physical Properties of Magneto Caloric Materials

New Evolutionary Method for Studying Physical Properties of ...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Leonteva, Anna Ouskova Hamane, Radia Risser, Michel Jeannin-Girardon, Anne Parrend, Pierre Collet, Pierre Univ Strasbourg ICUBE Lab CNRS UMR 7357 Strasbourg France Univ Caen Crismat Lab Caen France Ubiblue Co Strasbourg France ECAM Strasbourg Europe Strasbourg France

ISBN: (纸本)9781728183923

Magnetic refrigeration (MR) is an alternative technology to conventional vapour compression with a high potential to reduce energy consumption and greenhouse gases. The working principle of MR is based on the property of magneto caloric materials (MCMs) to respond to applied external magnetic field by a variation in their temperature. To find the inexpensive MCMs with needed physical properties is still an issue. This paper describes a new method, whose objective is to automate the characterization process of MCM physical properties. This method is based on a multi-objective evolutionary optimization to define the combination of Hamiltonian model free parameters, which corresponds to needed physical properties of MCM. The advantage of this method is that it doesn't need first-principles calculations or huge databases. The description is followed by a thorough validation of the proposed method for two alloys. The obtained results are in good agreement with the experimental measurements.

关键词： magneto caloric materials multi-objective evolutionary optimization industrial optimization problem real-world application

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multi-objective Differential Evolution for automated control of Industrial Process input material composition incorporating supply-side fluctuations

Multi-objective Differential Evolution for automated control...

引用

IEEE Congress on evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Bhattacharya, Arya K. Sambasivam, Debjani Mahindra Ecole Cent Hyderabad 500043 Andhra Pradesh India Tata Steel Automat Div Jamshedpur 831001 Bihar India

ISBN: (纸本)9781509006229

The hitherto unresolved complex industrial problem of automated online optimization of the input material mix of a Blast Furnace is approached from an evolutionary multi-objective perspective with promising results. The optimization requirements are mapped into twin objectives - the core domain goals of cost minimization and iron-content maximization, and the supply chain objective of concurrently matching diverse material consumptions with availabilities based on production at upstream plants. The novelty lies in the manner this complex problem is made tractable by extracting a set of constraints from the overloaded constraint space and transforming them into a new objective within a multi-objective solution space. Only a set of hard constraints based on thermo-chemical considerations are retained to ensure furnace process stability and product quality. An NSDE-II methodology is developed for this application wherein a variant of Differential Evolution serves as the core optimization mechanism, and non-dominated sorting with crowded distance refinement perform the selection function in multi-objective space. The results from this approach are found to be favorable against current manufacturing practice across a large data set of industrial test cases. This methodology can be extended to other sectors of process and manufacturing industry with analogous requirements on production maximization and supply-consumption synchronization.

关键词： industrial process inputs supply chain multi-objective evolutionary optimization Differential Evolution Pareto front convergence

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optimization strategies for Fleet Management based on Wireless Terminals Localization in Smart Cities Scenarios 2

Optimization strategies for Fleet Management based on Wirele...

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2nd IEEE International Smart Cities Conference (ISC2)

作者： Polo, A. Viani, F. Robol, F. Massa, A. Marchesi, S. Zappini, L. Univ Trento ELEDIA UniTN ELEDIA Res Ctr Via Sommar 9 I-38123 Trento Italy Cent Unica Emergenza Prov Autonoma Trento Via G Pedrotti 18 I-38122 Trento Italy

ISBN: (纸本)9781509018451

The increasing availability and diffusion of digital wireless communication systems and localization technologies is offering new opportunities for the introduction of location-based decision support systems for the fleet and mission control management in smart cities. In this paper, the use of multi-objective evolutionary metaheuristic for dispatching fleet assets is addressed. The applications are particularly interesting for goods transportation and emergency management agencies, the proposed approach has been implemented in a full-scale decision support system prototype under experimental validation at the headquarter of the Centrale Unica Emergenza (CUE) of Trento, Italy.

关键词： wireless localization decision support system emergency management metaheuristic multi-objective evolutionary optimization terrestrial trunked radio (TETRA)

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Time-series-dynamics Modeling and Forecasting - An Accurate and Interpretable Genetic-Fuzzy Approach 4th

Time-series-dynamics Modeling and Forecasting - An Accurate ...

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10th Conference of the European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT) / 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets (IWIFSGN)

作者： Gorzalczany, Marian B. Rudzinski, Filip Kielce Univ Technol Dept Elect & Comp Engn Al 1000 Lecia PP 7 PL-25314 Kielce Poland

ISBN: (纸本)9783319668246;9783319668239

In this paper, we present the application of our fuzzy rule-based modeling technique with genetically optimized accuracy-interpretability trade-off to time-series-dynamics discovery, modeling, and forecasting. The so-called Box-Jenkins' benchmark, i.e., measurement-based time series describing the behavior of an industrial gas furnace is considered. We employ - as a multi-objective evolutionary optimization algorithm - our generalization - characterized by a higher spread and a better balanced distribution of solutions - of the well-known SPEA2 method. Our approach has been compared with several alternative techniques applied to the same time series data.

关键词： Time-series-dynamics modeling and forecasting Fuzzy rule-based systems multi-objective evolutionary optimization Accuracy-interpretability trade-off optimization

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An Adaptive Local Search Based Genetic Algorithm for Solving multi-objective Facility Layout Problem

An Adaptive Local Search Based Genetic Algorithm for Solving...

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17th International Conference on Neural Information Processing

作者： Ripon, Kazi Shah Nawaz Glette, Kyrre Hovin, Mats Torresen, Jim Univ Oslo Dept Informat N-0316 Oslo Norway

ISBN: (纸本)9783642175367

Due to the combinatorial nature of the facility layout problem (FLP), several heuristic and meta-heuristic approaches have been developed to obtain good rather than optimal solutions. Unfortunately, most of these approaches are predominantly on a single objective. However, the real-world FLPs are multi-objective by nature and only very recently have meta-heuristics been designed and used in multi-objective FLP. These most often use the weighted sum method to combine the different objectives and thus, inherit the well-known problems of this method. This paper presents an adaptive local search based genetic algorithm (GA) for solving the multi-objective FLP that presents the layouts as a set of Pareto-optimal solutions optimizing both quantitative and qualitative objectives simultaneously. Unlike the conventional local search, the proposed adaptive local search scheme automatically determines whether local search is used in a GA loop or not. The results obtained show that the proposed algorithm outperforms the other competing algorithms and can find near-optimal and non-dominated solutions by optimizing multiple criteria simultaneously.

关键词： Adaptive local search multi-objective facility layout problem Pareto-optimal solution multi-objective evolutionary optimization

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Simulation-Based Crossover for the Firefighter Problem 17

Simulation-Based Crossover for the Firefighter Problem

引用

Genetic and evolutionary Computation Conference (GECCO)

作者： Michalak, Krzysztof Wroclaw Univ Econ Inst Business Informat Dept Informat Technol Komandorska 118-120 PL-53345 Wroclaw Poland

ISBN: (纸本)9781450349208

The Firefighter Problem (FFP) is a combinatorial optimization problem in which the goal is to find the best way of protecting nodes in a graph from spreading fire or other threat given limited resources. Because of high computational complexity the FFP is often solved using metaheuristic methods such as evolutionary algorithms (EAs). The problem that arises is that many crossover operators used in EAs were developed with problems such as the TSP or the flowshop problem in mind and are therefore not optimized towards the FFP. In this paper a new crossover operator SimX is proposed that determines how to combine information from parent specimens using a simulation of fire spreading and some problem-specific heuristics. The proposed crossover operator was compared to a set of ten permutation-based crossover operators in experiments performed using a multipopulation algorithm with operator autoadaptation. In the experiments it was observed that the proposed simulation-based crossover operator produced good offspring more often than the other operators.

关键词： Graph-based optimization multi-objective evolutionary optimization Firefighter problem Operator autoadaptation

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Exploring the Design Space of Lunar GNSS in Frozen Orbit Conditions

Exploring the Design Space of Lunar GNSS in Frozen Orbit Con...

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IEEE/ION Position, Location and Navigation Symposium (PLANS)

作者： Pereira, Filipe Selva, Daniel Cornell Univ Syst Engn Ithaca NY 14850 USA Texas A&M Univ Aerosp Engn College Stn TX USA

ISBN: (纸本)9781728102443

We have witnessed a growing interest in lunar exploration missions in the last few years, with the announcement of NASA's Deep Space Gateway, the launch of several uncrewed missions and the involvement of private companies. However, there is currently no reliable means of accurate and instantaneous navigation in the vicinity of the Moon. This poses operational limitations on the autonomy of lunar robotic and small satellite missions. We present the preliminary results of a systems architecture study conducted on a new satellite navigation system orbiting the Moon that would effectively extend the Global Navigation Satellite Systems (GNSS) space service volume to cislunar space. We constrain the analysis to lunar frozen orbits under J2, C22 and third-body perturbations, to achieve stable lunar orbits. The problem formulation includes the following design decisions: (1) Orbit semi-major axis, (2) Number of satellites, (3) Number of orbital planes, (4) Satellite phasing in adjacent planes, (5) Orbit eccentricity and (6) Argument of periapsis. The Borg multi-objective evolutionary Algorithm (MOEA) is used to optimize the satellite constellation design using multiple crossover and mutation operators that can adaptively be selected at runtime. The fitness function takes into account performance, cost, availability and station-keeping delta-V. Specifically, the performance metric assessment is based on the Geometric Dilution of Precision (GDOP), which is computed over a grid of 500 equidistant points on the lunar surface. The input satellite orbits used in the GDOP computation are obtained from high-fidelity orbit propagation using NASA's General Mission Analysis Toolbox (GMAT). The space segment cost model considers satellite constellation development and production costs. Single satellite costs are based on satellite dry mass estimates derived from the power budget analysis assuming a satellite lifetime of 10 years. The results of the study show that highperforming Lunar

关键词： Global Navigation Satellite Systems multi-objective evolutionary optimization Frozen orbits Cislunar space

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