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检索条件"主题词=Multi-Objective Evolutionary Algorithm"

共 545 条记录，以下是441-450 订阅

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Chromatic Selection - An Oversimplified Approach to multi-objective Optimization 18

引用

18th European Conference on Applications of evolutionary Computation (EvoApplications) as part of EvoSTAR Conference

作者： Squillero, Giovanni Politecn Torino Corso Duca Abruzzi 24 I-10129 Turin Italy

ISBN: (纸本)9783319165486;9783319165493

This short paper introduces the chromatic selection, a simple technique implementable with few tens of lines of code, that enable handling multi-value fitness functions with a single-objective evolutionary optimizer. The chromatic selection is problem independent, requires no parameter tuning, and can be used as a drop-in replacement for both parent and survival selections. The resulting tool will not be a full-fledged multi-objective optimizer, lacking the ability to manage Pareto fronts, but it will efficiently seek a single, reasonable, compromise solution. In several practical problems, the time saved, both in computation and development, could represent a substantial advantage.

关键词： multi-objective evolutionary algorithm evolutionary optimization Selection scheme

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Optimal Placement of Data Concentrators and Repeaters in PLC-enabled Smart Grids

Optimal Placement of Data Concentrators and Repeaters in PLC...

引用

14th IEEE Madrid PowerTech Conference (IEEE POWERTECH)

作者： Stiri, Souhaima Chaoub, Abdelaali Grilo, Antonio Bennani, Rachid Lakssir, Brahim Tamtaoui, Ahmed INPT MUSICS Team STRS Lab Rabat Morocco MAScIR D&MSD Lab Rabat Morocco Univ Lisbon IST Lisbon Portugal INESC ID Lisbon Portugal

ISBN: (纸本)9781665435970

The focus of this paper is on Power Line Communication (PLC) technology which has a great potential to accommodate the huge amount of data produced by different applications and nodes in the context of Smart Grids (SGs). In particular, this paper dives deeper into the planning and placement of Data Concentrator (DC) in various territorial categories: urban, suburban and rural environments. Supplemental repeaters, usually omitted in previous stat-of-the-art, are inserted throughout the electrical grid to reach the entire network. A multi-criteria optimization approach has been followed and the popular NSGA-II algorithm has been exploited to find a good trade-off between the conflicting performance and profitability objectives. Results of extensive numerical simulation are provided confirming the effective need of repeaters especially in remote areas where Smart Meters (SMs) are sparsely distributed. Moreover and despite the destructive influence of fading and noise along with the involved investments, PLC is still able to provide a satisfactory balance between performance and cost.

关键词： Smart Metering PLC Data Concentrator and Repeater Placement multi-objective Optimization Problem multi-objective evolutionary algorithm

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The Investment Models Based on the Fundamental and Technical Indicators of Listed Companies 37

The Investment Models Based on the Fundamental and Technical...

引用

37th Chinese Control Conference (CCC)

作者： Meng, Xiaolian Chen, Xiuzhen Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Jiangsu Peoples R China

ISBN: (纸本)9789881563958

The fundamental and technical approaches are often used to analyze the operation status of listed companies and the development tendency of the stock market. The investment models using financial ratios and technical indicators are proposed in this article, and a multi-objective evolutionary algorithm with benefits and risks is used to optimize them. Two different chromosomes are used for representing different investment models with real constraints. An empirical study based on 40 stocks of SSE A shares from Cathay Pacific database was conducted to illustrate the practicability of the proposed models. The results show that better stocks and better transaction decisions can be found and selected by the portfolio management method based on fundamental and technical analysis, which could be obtained a portfolio with better result than the market index. The effectiveness and practicality of this investment model is improved more with more fundamental indicators introduced, especially cash flow growth, capital expenditure growth rates and payout ratio.

关键词： multi-objective evolutionary algorithm fundamental analysis technical analysis stock investment

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Enhancing Search-Based Product Line Design with Crossover Operators

Enhancing Search-Based Product Line Design with Crossover Op...

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Genetic and evolutionary Computation Conference (GECCO)

作者： da Silva, Diego Fernandes Okada, Luiz Fernando Colanzi, Thelma Elita Assuncao, Wesley K. G. Univ Estadual Maringa Maringa Parana Brazil Univ Tecnol Fed Parana Toledo Parana Brazil

ISBN: (纸本)9781450371285

The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line. PLA designing has been formulated as a multi-objective optimization problem and successfully solved by a state-of-the-art search-based approach. However, the majority of empirical studies optimize PLA designs without applying one of the fundamental genetic operators: the crossover. An operator for PLA design, named Feature-driven Crossover, was proposed in a previous study. In spite of the promising results, this operator occasionally generated incomplete solutions. To overcome these limitations, this paper aims to enhance the search-based PLA design optimization by improving the Feature-driven Crossover and introducing a novel crossover operator specific for PLA design. The proposed operators were evaluated in two well-studied PLA designs, using three experimental configurations of NSGA-II in comparison with a baseline that uses only mutation operators. Empirical results show the usefulness and efficiency of the presented operators on reaching consistent solutions. We also observed that the two operators complement each other, leading to PLA design solutions with better feature modularization than the baseline experiment.

关键词： multi-objective evolutionary algorithm software product line software architecture recombination operators

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An algorithm for traffic grooming in WDM optical mesh networks with multiple objectives 12

An algorithm for traffic grooming in WDM optical mesh networ...

引用

12th International Conference on Computer Communications and Networks (ICCCN 2003)

作者： Prathombutr, P Stach, J Park, E Univ Missouri Sch Interdisciplinary Comp & Engn Kansas City MO 64110 USA

ISBN: (纸本)0780379454

This paper studies a traffic grooming in wavelength-division multiplexing (WDM) mesh networks for the SONET/SDH streams requested between node pairs. The traffic could be groomed at the access node before converting to an optical signal carried in the all-optical network. We design a virtual topology with a given physical topology to satisfy multiple objectives and constraints. The grooming problem of a static demand is considered as an optimization problem. The traditional algorithms found in the literatures mostly focus on a single objective either to maximize the performance or to minimize the cost. We propose a multi-objective evolutionary algorithm to solve a grooming problem that optimizes multiple objectives all together at the same time. In this paper we consider the optimization of three objectives: maximize the traffic throughput, minimize the number of transceivers, and minimize the average propagation delay or average hop counts. The simulation results show that our approach is superior to an existing heuristic approaches in an acceptable running time.

关键词： multi-objective evolutionary algorithm grooming WDM mesh optical networks Pareto optimal

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multi-objective Ant Colony algorithm Based on Pheromone Weight 16

Multi-objective Ant Colony Algorithm Based on Pheromone Weig...

引用

16th International Conference on Computational Intelligence and Security (CIS)

作者： Yang, Lei Jia, Xiaotian Liu, Ganming South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China

ISBN: (纸本)9781665404457

This paper proposed multi-objective ant colony algorithm based on pheromone weight, which is used to solve multi-objective optimization problems. The algorithm introduces the weight of distance-related in the initialization of pheromones, which is beneficial to the ant speed up the path selection, improving the efficiency of ant search. At the same time, the adaptive variation operator that dynamically adjusts the number of ant neighbors with the number of iterations and the weight Tchebycheff aggregation method are also introduced, which are beneficial to improve the convergence speed and the quality of the algorithm. The algorithm has been compared with other related algorithms using Hypervolume and other indicators in the standard dual Traveling Salesman Problem (TSP), and has been proven that the improved algorithm has better results.

关键词： multi-objective evolutionary algorithm Ant colony optimization Hypervolume index

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multi-objective Magnitude-Based Pruning for Latency-Aware Deep Neural Network Compression 16th

Multi-objective Magnitude-Based Pruning for Latency-Aware De...

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16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Hong, Wenjing Yang, Peng Wang, Yiwen Tang, Ke Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Univ Sci & Technol China Dept Management Sci Hefei 230027 Peoples R China Guangdong Hong Kong Macao Greater Bay Area Ctr Br Guangzhou 510515 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Dept Chem & Biol Engn Hong Kong Peoples R China

ISBN: (纸本)9783030581114;9783030581121

Layer-wise magnitude-based pruning is a popular method for Deep Neural Network (DNN) compression. It has the potential to reduce the latency for an inference made by a DNN by pruning connects in the network, which prompts the application of DNNs to tasks with real-time operation requirements, such as self-driving vehicles, video detection and tracking. However, previous methods mainly use the compression rate as a proxy for the latency, without explicitly accounting for latency in the training of the compressed network. This paper presents a new layer-wise magnitude-based pruning method, namely multi-objective Magnitude-based Latency-Aware Pruning (MMLAP). MMLAP captures latency directly and incorporates a novel multi-objective evolutionary algorithm to optimize both accuracy of a DNN and its latency efficiency when designing compressed networks, i.e., when tuning hyper-parameters of LMP. Empirical studies show the competitiveness of MMLAP compared to well-established LMP methods and show the value of multi-objective optimization in yielding Pareto-optimal compressed networks in terms of accuracy and latency.

关键词： Compression Magnitude-based pruning Latency-aware multi-objective optimization multi-objective evolutionary algorithm

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Robustness Analysis of a MOEA-based Elicitation Method for Outranking Model Parameters

Robustness Analysis of a MOEA-based Elicitation Method for O...

引用

10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： Covantes, Edgar Fernandez, Eduardo Navarro, Jorge Autonomous Univ Sinaloa Fac Informat Sci Culiacan Sinaloa Mexico

ISBN: (纸本)9781479914609

To set the parameter values for the outranking model is usually a demanding task for the decision-maker (DM) because it is necessary to provide a large number of parameters (thresholds and weights). The use of indirect methods (preference-disaggregation analysis (PDA)) to infer the set of parameters from a battery of decision examples allows the DM to avoid the task of specifying these values. In this paper a robustness analysis has been made to a PDA method based on an evolutionary approach. In this case we use THESEUS method as an artificial DM and its assignment rule for evaluating each individual of a multi-objective evolutionary algorithm (MOEA) population. The non-dominated solutions are acceptable parameter settlements.

关键词： Robustness analysis preference-disaggregation analysis THESEUS method multi-objective evolutionary algorithm

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Proposal of a Realistic Many-objective Test Suite 16th

Proposal of a Realistic Many-Objective Test Suite

引用

16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Chen, Weiyu Ishibuchi, Hisao Shang, Ke Southern Univ Sci & Technol SUSTech Dept Comp Sci & Engn Univ Key Lab Evolving Intelligent Syst Guangdong Shenzhen Key Lab Computat Intelligence Shenzhen Guangdong Peoples R China

ISBN: (纸本)9783030581114;9783030581121

Real-world many-objective optimization problems are not always available as easy-to-use test problems. For example, their true Pareto fronts are usually unknown, and they are not scalable with respect to the number of objectives or decision variables. Thus, the performance of existing multi-objective evolutionary algorithms is always evaluated using artificial test problems. However, there exist some differences between frequently-used many-objective test problems and real-world problems. In this paper, first we clearly point out one difference with respect to the effect of changing the value of each decision variable on the location of the objective vector in the objective space. Next, to make artificial test problems more realistic, we introduce a coefficient matrix to their problem structure. Then, we demonstrate that a different coefficient matrix leads to a different difficulty of a test problem. Finally, based on these observations, we propose a realistic many-objective test suite using various coefficient matrices.

关键词： Many-objective optimization multi-objective evolutionary algorithm Many-objective test suite Coefficient matrix

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Analysis of the Pareto Front of a multi-objective Optimization Problem for a Fossil Fuel Power Plant

Analysis of the Pareto Front of a Multi-objective Optimizati...

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Van Sickel, Joel H. Venkatesh, Paramasivam Lee, Kwang Y. Penn State Univ Dept Elect Engn University Pk PA 16802 USA Baylor Univ Dept Elect & Comp Engn Waco TX 76798 USA

ISBN: (纸本)9781424419050

This paper examines the Pareto front of a simple fossil fuel power plant using a common third-order model. This front is first examined analytically. Then the power plant model is transferred over to a steady-state model using a static neural network and the front is estimated using various geometric and heuristic approaches. This paper is the first of the two stages to eliminate the need for a human to construct a single objective cost function from multiple objectives for use in the power plant optimization. To generate the Pareto front of the power plant, three different optimization techniques are explored, normal-boundary intersection utilizing differential equation, multi-objective particle swarm optimization, and multi-objective evolutionary algorithm optimization.

关键词： multi-objective evolutionary algorithm multi-objective particle swarm optimization normal-boundary intersection Pareto front Pareto optimization power plant reference governor

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