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International Conference on Autonomous Agents and multiagent Systems

作者： Logan Yliniemi Drew Wilson Kagan Tumer Oregon State University Austin Peay State University

ISBN: (纸本)9781450337717

Determining the contribution of an agent to a system-level objective function (credit assignment) is a key area of research in cooperative multiagent systems. multi-objective optimization is a growing area of research, though mostly focused on single agent settings. Many real-world problems are multiagent and multi-objective, (e.g., air traffic management, scheduling observations across multiple exploration robots) yet there is little work on their intersection. In this work, we leverage recent advances in single-objective multiagent learning to address multi-objective domains. We focus on the impact of difference evaluation functions (which extracts an agent's contribution to the team objective) on the Non-dominated Sorting Genetic algorithm-II (NSGA-II), a state-of-the-art multi-objective evolutionary algorithm. We derive multiple methods for incorporating difference evaluations into the NSGA-II framework, and test each in a multiagent rover exploration domain, which is a good surrogate for a wide variety of distributed scheduling and resource gathering problems. We show that how and where difference evaluations are incorporated in the NSGA-II algorithm is critical, and can either provide significant benefits or destroy system performance, depending on how it is used. Median performance of the correctly used difference evaluations dominates best-case performance of NSGA-II in a multiagent multi-objective problem.

关键词： multiagent Learning multi-objective Problem multi-objective evolutionary algorithm NSGA-II

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Prior knowledge guided active modules identification: an integrated multi-objective approach

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BMC SYSTEMS BIOLOGY 2017年第Sup2期11卷 8-8页

作者： Chen, Weiqi Liu, Jing He, Shan Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China

Background: Active module, defined as an area in biological network that shows striking changes in molecular activity or phenotypic signatures, is important to reveal dynamic and process-specific information that is correlated with cellular or disease states. Methods: A prior information guided active module identification approach is proposed to detect modules that are both active and enriched by prior knowledge. We formulate the active module identification problem as a multi-objective optimisation problem, which consists two conflicting objective functions of maximising the coverage of known biological pathways and the activity of the active module simultaneously. Network is constructed from protein-protein interaction database. A beta-uniform-mixture model is used to estimate the distribution of p-values and generate scores for activity measurement from microarray data. A multi-objective evolutionary algorithm is used to search for Pareto optimal solutions. We also incorporate a novel constraints based on algebraic connectivity to ensure the connectedness of the identified active modules. Results: Application of proposed algorithm on a small yeast molecular network shows that it can identify modules with high activities and with more cross-talk nodes between related functional groups. The Pareto solutions generated by the algorithm provides solutions with different trade-off between prior knowledge and novel information from data. The approach is then applied on microarray data from diclofenac-treated yeast cells to build network and identify modules to elucidate the molecular mechanisms of diclofenac toxicity and resistance. Gene ontology analysis is applied to the identified modules for biological interpretation. Conclusions: Integrating knowledge of functional groups into the identification of active module is an effective method and provides a flexible control of balance between pure data-driven method and prior information guidance.

关键词： Prior knowlege multi-objective evolutionary algorithm Active module identification

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The stability optimization of the international lithium trade

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RESOURCES POLICY 2021年 74卷 102336-102336页

作者： Wu, Congcong Gao, Xiangyun Xi, Xian Zhao, Yiran Li, Yu China Univ Geosci Sch Econ & Management Beijing 100083 Peoples R China Minist Nat Resources Key Lab Carrying Capac Assessment Resource & Envi Beijing 100083 Peoples R China Hebei GEO Univ Coll Informat Engn Shijiazhuang 050031 Hebei Peoples R China

The international mineral resources trade is an important way to correct the unbalanced distribution of global mineral resources. This paper proposes a bi-objective model to improve the stability of the mineral resource trade by optimizing the trade structure. The improved nondominated sorting genetic algorithm (NSGA-II) was used to solve the bi-objective optimization model, and a more robust trade structure was obtained. We applied the model to the optimization of the global lithium carbonate trade. The optimized results enabled us to identify the key trade relationships that play the most critical role in improving the stability of lithium carbonate trade. The effectiveness of the optimization model was proved by analyzing the stability of major net importing countries and the influence of major net exporting countries. Furthermore, the strongest destructive model, which was opposite to the optimal model, was used to analyze the core trade relations and core countries in the actual lithium trade. These core trade relations provide a foundation for maintaining the stability of the international trade. Our work provides new ideas for improving the stability of the global mineral trade and provides a new perspective and special basis for trading countries to choose trade partners.

关键词： Trade stability Complex network Trade structural optimization multi-objective evolutionary algorithm Lithium

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PyBrOpS: a Python package for breeding program simulation and optimization for multi-objective breeding

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G3-GENES GENOMES GENETICS 2024年第10期14卷 jkae199页

作者： Shrote, Robert Z. Thompson, Addie M. Michigan State Univ Dept Plant Soil & Microbial Sci 1066 Bogue St E Lansing MI 48824 USA Michigan State Univ Plant Resilience Inst E Lansing MI 48824 USA

Plant breeding is a complex endeavor that is almost always multi-objective in nature. In recent years, stochastic breeding simulations have been used by breeders to assess the merits of alternative breeding strategies and assist in decision-making. In addition to simulations, visualization of a Pareto frontier for multiple competing breeding objectives can assist breeders in decision-making. This paper introduces Python Breeding Optimizer and Simulator (PyBrOpS), a Python package capable of performing multi-objective optimization of breeding objectives and stochastic simulations of breeding pipelines. PyBrOpS is unique among other simulation platforms in that it can perform multi-objective optimizations and incorporate these results into breeding simulations. PyBrOpS is built to be highly modular and has a script-based philosophy, making it highly extensible and customizable. In this paper, we describe some of the main features of PyBrOpS and demonstrate its ability to map Pareto frontiers for breeding possibilities and perform multi-objective selection in a simulated breeding pipeline.

关键词： stochastic simulation Python breeding programs multi-objective optimization Pareto frontier multi-objective evolutionary algorithm

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multi-objective Flexible Job Shop Scheduling Problem Based on Non-Dominated Genetic algorithm

Multi-Objective Flexible Job Shop Scheduling Problem Based o...

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Artificial Intelligence and Electromechanical Automation (AIEA), International Conference on

作者： Yang Yacong Wang Aimin Ge Yan School of Mechanical Engineering Department Institute of Technology Beijing China

ISBN: (数字)9781728182889

ISBN: (纸本)9781728182896

Aiming at the limitation of flexible job shop scheduling (JSP), a multi-objective optimization model of JSP is constructed, which takes the maximum completion time and delivery time as the objective function. Considering the actual inventory size of job shop, a non dominated genetic algorithm is proposed. Combined with the actual inventory and demand, the program is developed to verify the feasibility and effectiveness of the algorithm. The results show that the algorithm can meet the delivery time and the maximum completion time at the same time, considering the size of inventory and achieve satisfactory results.

关键词： component Flexible job shop scheduling multi-objective evolutionary algorithm Non dominated sorting genetic algorithm Job shop scheduling multi-objective evolutionary algorithm Completion time delivery date JSP Genetic algorithm (GA) inventories FLEXIBLE

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Design and Optimisation of a 20kW Horizontal Axis Wind Turbine using HARP_Opt*

Design and Optimisation of a 20kW Horizontal Axis Wind Turbi...

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IEEE International Conference on Power and Energy Systems (ICPES)

作者： Krishneel A Singh Mohammed R Ahmed School of Engineering and Physics The University of the South Pacific Suva Fiji

ISBN: (数字)9781728126586

ISBN: (纸本)9781728126593

The design of a 20 kW horizontal axis small wind turbine was carried out using the analysed wind data and the Weibull parameters of a site in Vanuatu. The airfoils were generated for the 20 kW SWT and optimized using XFOIL. The initial airfoil was parameterized using a 7 th order Bezier curve. Three airfoils with thicknesses of 21%, 14% and 9% were generated for the root region, mid region and the tip region respectively. The performance characteristics were studied and Harp_Opt was used to design and optimize the 20 kW wind turbine. A cut-in wind speed of 2 m/s and a rated wind speed of 10 m/s were shown by the power curve. The annual energy production (AEP) of the designed turbine was calculated to be 66.75 MWh/year with a capacity factor of around 38.1%. Small wind turbines will be ideal for the Pacific Island Countries (PICs) since they can operate at low wind speeds and Reynolds numbers less than 500,000. Using larger commercial turbines are not ideal for the PICs due to frequent cyclones and logistics issues thus a SWT is designed in the present work.

关键词： Horizontal axis wind turbine small wind turbine multi objective genetic algorithm HARP_Opt optimization horizontal axis wind turbine Wind Energy Conversion Systems wind speed PICs Weibull parameters multi-objective evolutionary algorithm Reynolds number Standard Widget Toolkit Airfoils Wind data

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

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

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第37届中国控制会议

作者： Xiaolian Meng Xiuzhen Chen School of Economics and Management Nanjing University of Science and Technology

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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A Fuzzy multi-objective Portfolio Selection Model with Transaction Cost and Liquidity

A Fuzzy Multi-objective Portfolio Selection Model with Trans...

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International Conference on Computational Intelligence and Security

作者： Wei Yue Yuping Wang School of Computer Science and Technology Xidian University Xi'an Shannxi China

In this paper, a new proportion entropy function is proposed as an objective function to obtain a well-diversified portfolio. Secondly, a new fuzzy multi-objective portfolio selection model based on the proposed entropy function is presented. By using the model, we can find tradeoffs between risk, return and the diversification degree of portfolio. Thirdly, a new multi-objective evolutionary algorithm is designed to solve the proposed model. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed model and the corresponding algorithm.

关键词： Entropy multi-objective evolutionary algorithm Portfolio selection Possibilistic moments multi-objective evolutionary algorithm entropy entropy function liquidity Fuzzy tradeoffs objective function

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Robot Path Planning Based on an Improved multi-objective PSO Method

Robot Path Planning Based on an Improved Multi-Objective PSO...

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2016 International Conference on Computer Engineering,Information Science & Application Technology(ICCIA 2016)

作者： Li Di Zheng Zheng Meng Xia Min Xia Economics and Technology Research Institute of State Grid Henan Electric Power Company Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology

ISBN: (纸本)9781510830363

Robot path planning is an important content in the field of robot research. Robot Path Planning is a typical multi-objective optimization problem. The path length, the degree of path smoothness and the degree of security are the optimization objectives in this work. And an improved multi-objective PSO method is used for optimization. In this method, in order to make the particle population multi-objective particle swarm optimization algorithm can quickly converge to the Pareto optimal boundary, an environment selection and a matching selection strategy are proposed. At each iteration of the algorithm, in order to improve the population information exchange and reduce the randomness, the environmental selection and matching selection strategy of SPEA2 are used for multi-objective PSO method, and the particle population can faster convergence to the Pareto optimal boundary. The simulation results verify the method, and the result of proposed method is better than that of multi-objective PSO method, and the simulations indicates that the proposed model is practical for robot path planning.

关键词： multi-objective evolutionary algorithm SPEA2 Robot path planning PSO

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multi-objective Optimal Tuning of multi-loop Control Systems by using evolutionary Computation

Multi-objective Optimal Tuning of Multi-loop Control Systems...

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Asian Control Conference

作者： Andrea Richter Adrian Gambier Westinghouse Electric Germany Heidelberg University

ISBN: (纸本)9781612844879

A framework for the joint parameters tuning of several controllers in coupled multi-loop control systems is presented. It is based on the design of a cooperative dynamic game, which is solved by using a multi-objective evolutionary algorithm, combined with a game theoretic solution concept. For each controller, an objective function is defined and parametrically optimized. Several performance indices e.g. ISE, ITSE and ISTSE are used as objective functions and the optimization results are compared by means of a simulation example.

关键词： evolutionary algorithm multiple loop objective function performance indicators control systems solution concept multi-objective evolutionary algorithm Controllers tuning control ring Hunting Game theory Game animals

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