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11th IEEE International Conference on Computational Science and Engineering

作者： Agrawal, Kapil KanGAL Indian Inst Technol Kanpur Kanpur Genet Algorithms Lab Dept Mech Engn Kanpur Uttar Pradesh India

ISBN: (纸本)9780769531939

In the realm of Computational Finance, the performance of the optimal portfolio largely depends upon its composition and its ability to accurately predict the market movements. Recent empirical studies have shown that the underlying assumption of normality of asset returns for risk modeling is seriously flawed, in view of their asymmetric and fat-tailed behavior. This problem is further aggravated when we delve into the functioning of the Financial market and realize that the market parameters have highly nonlinear kind of inter-dependence amongst themselves. Any investment portfolio that does not account for these factors and their mutual relationship, will tend to under-perform. This work is a novel attempt, which aims at developing a framework which solves all of these problems in an integrated fashion, without overlooking any of them or preassigning lesser importance to any of these issues. The contemporary techniques often neglect one of them, resulting in an incomplete and sometimes even a misleading picture of the market scenario. In this work, copula theory effectively captures the non-linear inter-dependence. The scenarios are generated from a non-elliptical multivariate distribution constructed by a Students t-copula assuming marginal distributions as Gaussian in the center and EVT distributed in the tail. For gauging the market risk we have used CVaR (Conditional Value-at-Risk) as the risk measure. The efficient frontier thus resulted by minimizing the CVaR and maximizing the returns, gives a clear insight into how does the composition of the optimal portfolio changes with respect to change in CVaR of the portfolio. Our aim is to prove that much more reliable conclusions will certainly be drawn if a more realistic representation of data can be done using the concept of Copulas.

关键词： Portfolio Optimization multi-objective evolutionary algorithm conditional expectation Copula multivariate distribution Value-at-Risk Conditional Value-at-Risk extreme value theory NSGA-II

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A New Technique for Assessing the Diversity of Close-Pareto-Optimal Front

A New Technique for Assessing the Diversity of Close-Pareto-...

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IEEE Congress on evolutionary Computation

作者： Zeng, Sanyou Chen, Guang Wang, Rui Li, Hui Shi, Hui Ding, Lixin Kang, Lishan China Univ Geosci Dept Comp Sci Technol Res Ctr Space Sci & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China

ISBN: (纸本)9781424418220

The quality of an approximation set usually includes two aspects-- approaching distance and spreading diversity. This paper introduces a new technique for assessing the diversity of an approximation to an exact Pareto-optimal front. This diversity is assessed by using an "exposure degree" of the exact Pareto-optimal front against the approximation set. This new technique has three advantages: Firstly, The "exposure degree" combines the uniformity and the width of the spread into a direct physical sense. Secondly, it makes the approaching distance independent from the spreading diversity at the most. Thirdly, the new technique works well for problems with any number of objectives, while the widely used diversity metric proposed by Deb would work poor in problems with 3 objectives or over. Experimental computational results show that the new technique assesses the diversity well.

关键词： Quality assessment approximation set multi-objective optimization Pareto optimal front multi-objective evolutionary algorithm

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AN INVESTIGATION OF LOCAL SEARCHES IN MEMETIC algorithmS FOR multi-objective SEQUENCING PROBLEMS ON MIXED-MODEL ASSEMBLY LINES

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38th International Conference on Computers and Industrial Engineering

作者： Chutima, Parames Pinkoompee, Penpak Chulalongkorn Univ Dept Ind Engn Fac Engn Bangkok 10330 Thailand

ISBN: (纸本)9787121074370

Mixed model assembly lines are a type of production line where a variety of product models with similar product characteristics are assembled in a just-in-time (JIT) production system. In this paper, we consider two objectives;which are setup times and production rates variation. to be minimized simultaneously. The two objectives are inversely related to each other and, therefore, simultaneous optimization of both is challenging. This type of problem is also an NP-hard problem. We show in this study how the performance of such evolutionary multi-objective optimization algorithm as memetic algorithm can be improved by hybridization with local search. There are seven local search procedures applied in memetic algorithms to solve multi-objective sequencing problems on mixed-model assembly lines in UT production systems. Experimental results show that the performance of local search in memetic algorithm is significantly better than highly meta-heuristics as Strength Pareto evolutionary algorithm 2 (SPEA2) and Non-dominated Sorting Genetic algorithm II (NSGA-II) in terms of ability to find Pareto-optimal solution.

关键词： Memetic algorithms Local Search Heuristics multi-objective evolutionary algorithm Mixed model assembly line JIT production system

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multi-objective Genetic algorithm Based on the Correlation coefficient and Its Application

Multi-Objective Genetic Algorithm Based on the Correlation c...

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20th Chinese Control and Decision Conference

作者： Li, Junfeng Dai, Wenzhan Yang, Ye Zhejiang Sci Tech Univ Dept Automat Control Hangzhou 310018 Peoples R China

ISBN: (纸本)9781424417339

In this paper, based on the correlation coefficient, a new multi-objective evolutionary algorithm is put forward. First, the best solution of every objective among the multi-objectives is obtained and they are regarded on as the referenced vector. Second, the correlation coefficient between every individual and the referenced vector is solved and the correlation coefficient 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.

关键词： The Correlation Coefficient multi-objective evolutionary algorithm Adaptive Genetic algorithm

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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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Combine LHS with MOEA to Optimize Complex Pareto Set MOPs

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3rd International Conference on Intelligence Computation and Applications

作者： Zheng, Jinhua Luo, Biao Li, Miqing Li, Jing Xiangtan Univ Res Ctr Evolutionary Computat & Intelligent Syst Xiangtan 411105 Peoples R China

ISBN: (纸本)9783540921363

The Pareto set (PS) of real multi-objective optimization problems (MOPs) are often unknown and complex, so, it is significant for multi-objective evolutionary algorithms (MOEAs) to solve complex PS MOPs (CPS_MOPs namely). In this paper, we combined Latin hypercube sampling (LHS) with MOEA, proposed a LHS based MOEA (LHS-MOEA). We Suggested two kinds of LHS-MOEA, in which LHS local search and evolutionary operator are combined to handle CPS_MOPs. Through sonic experiments, the results demonstrate that LHS-MOEA performs Much better than the traditional prevalent MOEA - NSGA-II in solvin CPS_MOPs.

关键词： multi-objective evolutionary algorithm Latin Hypercube Sampling Complex Pareto Set Local Search evolutionary Operator

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A multi-objective Approach for the Optimal Distributed Generation Allocation with Environmental Constraints

A Multi-Objective Approach for the Optimal Distributed Gener...

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10th International Conference on Probabilistic Methods Applied to Power Systems

作者： Celli, G. Mocci, S. Pilo, F. Soma, G. G. Univ Cagliari Dept Elect & Elect Engn I-09123 Cagliari Italy

ISBN: (纸本)9781934325216

The latest political initiatives on energy saving are forcing distributors to implement actions that increase the energy efficiency of their system. Among these actions distributors can drive the development of the Distributed Generation (DG) on their networks. The inclusion of the energy saving goal among the terms of the objective function makes the DG allocation problem more and more complicated, due to the non homogeneity of its terms and the presence of several technical and environmental constraints, specific for each generation technology. Hence, a multi-objective approach, based on the Non-dominated Sorting Genetic algorithm, has been adopted to solve the optimal placement of different types of generators simultaneously. The energy saving goal has been considered in the form of greenhouse gas emission reduction, characterizing each generation technology depending on its CO(2) emission per kWh produced. Load demand and DG production have been modelled probabilistically and by assuming specific daily load/generation curves.

关键词： multi-objective evolutionary algorithm Distributed Generation distribution network planning

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A multi-objective Approach to Testing Resource Allocation in Modular Software Systems

A Multi-objective Approach to Testing Resource Allocation in...

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IEEE Congress on evolutionary Computation

作者： Wang, Zai Tang, Ke Yao, Xin Univ Sci & Technol China Dept Comp Sci & Technol Nat Inspired Computat & Applicat Lab Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9781424418220

Nowadays, as the software systems become increasingly large and complex, the problem of allocating the limited testing-resource during the testing phase has become more and more difficult. In this paper, we propose to solve the testing-resource allocation problem (TRAP) using multi-objective evolutionary algorithms. Specifically, we formulate TRAP as two multi-objective problems. First, we consider the reliability of the system and the testing cost as two objectives. In the second formulation, the total testing-resource consumed is also taken into account as the third goal. Two multi-objective evolutionary algorithms, Non-dominated Sorting Genetic algorithm II (NSGA2) and multi-objective Differential Evolution algorithms (MODE), are applied to solve the TRAP in the two scenarios. This is the first time that the TRAP is explicitly formulated and solved by multi-objective evolutionary approaches. Advantages of our approaches over the state-of-the-art single-objective approaches are demonstrated on two parallel-series modular software models.

关键词： Software Reliability multi-objective evolutionary algorithm Parallel-Series Modular Software System

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multi-objective optimization of a dimpled channel for heat transfer augmentation

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HEAT AND MASS TRANSFER 2008年第2期45卷 207-217页

作者： Samad, Abdus Lee, Ki-Don Kim, Kwang-Yong Inha Univ Dept Mech Engn Inchon 402751 South Korea

Staggered arrays of dimples printed on opposite surfaces of a cooling channel is formulated numerically and optimized with hybrid multi-objective evolutionary algorithm and Pareto optimal front. As Pareto optimal front produces a set of optimal solutions, the trends of objective functions with design variables are predicted by hybrid multi-objective evolutionary algorithm. The problem is defined by three non-dimensional geometric design variables composed of dimpled channel height, dimple print diameter, dimple spacing, and dimple depth, to maximize heat transfer rate compromising with pressure drop. Twenty designs generated by Latin hypercube sampling were evaluated by Reynolds-averaged Navier-Stokes solver and the evaluated objectives were used to construct Pareto optimal front through hybrid multi-objective evolutionary algorithm. The optimum designs were grouped by k-means clustering technique and some of the clustered points were evaluated by flow analysis. With increase in dimple depth, heat transfer rate increases and at the same time pressure drop also increases, while opposite behavior is obtained for the dimple spacing. The heat transfer performance is related to the vertical motion of the flow and the reattachment length in the dimple.

关键词： FLOW STRUCTURE NUMERICAL PREDICTIONS SHAPE OPTIMIZATION SURFACES FRICTION NUMBER DEPTH WALL multi-objective evolutionary algorithm shape optimization wall Heat exchange intensification dimple design variable Optimal design friction

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Single-objective front optimization: application to rf circuit design 08

Single-objective front optimization: application to rf circu...

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Proceedings of the 10th annual conference on Genetic and evolutionary computation

作者： E. J. Solteiro Pires Luís Mendes P. B. de Moura Oliveira J. A. Tenreiro Machado N. M. Fonseca Ferreira João Vaz Maria Rosário UTAD University Vila Real Portugal Inst. Politécnico de Leiria Leiria Portugal Inst. Superior de Engenharia do Porto Porto Portugal Inst. Superior de Engenharia de Coimbra Coimbra Portugal Inst. Superior Técnico Lisboa Portugal

ISBN: (纸本)9781605581309

This paper proposes a new algorithm which promotes well distributed non-dominated fronts in the parameters space when a single-objective function is optimized. This lgorithm is based on µ-dominance concept and maxmin sorting scheme. Besides that, the paper also presents the results of the algorithm when it is used in the automated synthesis of optimum performance CMOS radio-frequency and microwave binary-weighted differential switched capacitor arrays (RFDSCAs). The genetic synthesis tool optimizes a fitness function which is based on the performance parameter of the RFDSCAs. To validate the proposed design methodology, a CMOS RFDSCA is synthesized, using a 0.25 ¼m BiCMOS technology.

关键词： multi-objective evolutionary algorithm single-objective front radio frequency circuit design

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