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Gene Ranking: A Novel Approach Using multi-objective genetic algorithm 7

Gene Ranking: A Novel Approach Using Multi-Objective Genetic...

7th International Conference on Reliability, Infocom Technologies and Optimization - Trends and Future Directions (ICRITO)

作者： Das, Priyojit Saha, Sujay Ghosh, Anupam Dey, Kashi Nath Natl Inst Technol Calicut Dept Comp Sci & Engn Kozhikode 673601 Kerala India Heritage Inst Technol Dept Comp Sci & Engn Kolkata 700107 W Bengal India Netaji Subhas Engn Coll Dept Comp Sci & Engn Kolkata 700152 W Bengal India Univ Calcutta Dept Comp Sci & Engn Kolkata 700009 W Bengal India

ISBN: (纸本)9781538646922

In a Gene Co-expression Network, the same or closely related genes are clustered into co-expressed groups. It is necessary to investigate the role that these genes play as far as some complex diseases like cancer are concerned in those networks. Ranking those genes actually discover the significant candidate genes for various types of cancers. There are several gene ranking algorithms proposed so far that produces the top ranked genes according to their importance with respect to a particular cancer disease. In this work, we apply multi-objective genetic algorithm, multi-objective Network GA, on a gene coexpression network to find the top ranked cancer mediating genes. The algorithm is applied to publicly available real-life cancer datasets taken from NCBI (National Centre for Biotechnology Information) biological online repository. The performance of the algorithm is justified by classification using SVM classifier with linear kernel and it is compared with state-of-the-art methods on the basis of percentage of accuracy, precision, recall, and F1-Score.

关键词： Microarray Gene Expression Data genetic algorithm multi-objective genetic algorithm PageRank HITS

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An Approach for Optimizing Group Stock Portfolio Using multi-objective genetic algorithm 5

An Approach for Optimizing Group Stock Portfolio Using Multi...

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5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC)

作者： Chen, Chun-Hao Chiang, Bing-Yang Hong, Tzung-Pei Tamkang Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Sun Yat Sen Univ Comp Sci & Engn Kaohsiung Taiwan Natl Kaohsiung Univ Comp Sci & Informat Engn Kaohsiung Taiwan

ISBN: (纸本)9781728102078

Stock portfolio optimization is always an attractive research topic because of the variety of financial markers. In the past decade, lots of approaches have been proposed to deal with the problem. Previously, an algorithm was also designed for the group stock portfolio optimization problem. In that approach, portfolio satisfaction and group balance are utilized to evaluate the goodness of possible solutions. However, there is trade-off between those factors. In this paper, we thus design an approach for finding group stock group portfolios using the multi-objective genetic algorithm. Two objective functions, Sharpe ratio and group balance, are employed to derive possible Pareto solutions. Experiments on a real dataset were conducted to verity the effectiveness of the proposed approach.

关键词： Portfolio optimization group stock portfolio genetic algorithm grouping genetic algorithm multi-objective genetic algorithm

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Application of multi-objective genetic algorithm Based Simulation for Cost-Effective Building Energy Efficiency Design and Thermal Comfort Improvement

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FRONTIERS IN ENERGY RESEARCH 2018年第APR期6卷

作者： Lin, Yaolin Yang, Wei Wuhan Univ Technol Sch Civil Engn & Architecture Wuhan Peoples R China Victoria Univ Coll Engn & Sci Melbourne Vic Australia

Building design following the energy efficiency standards may not achieve the optimal performance in terms of investment cost, energy consumption and thermal comfort. In this paper, an improved multi-objective genetic algorithm (NSGA-II) is combined with building simulation to assist building design optimization for five selected cities located in the hot summer and cold winter region in China. The trade-offs between the annual energy consumption (AEC) and initial construction cost, as well as between life cycle cost (LCC) and number of thermal discomfort hours, were explored. Sensitivity analysis of various design parameters on building energy consumption is performed. The optimizations predicted AEC reduction of 29.08% on average, as compared to a reference building designed following the standard, and 38.6% with 3.18% more cost on the initial investment. New values for a number of building design parameters are recommended for the revision of relevant building energy efficiency standard.

关键词： building design optimization energy efficiency design standard life cycle cost thermal comfort multi-objective genetic algorithm

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Aiming strategy optimization for uniform flux distribution in the receiver of a linear Fresnel solar reflector using a multi-objective genetic algorithm

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APPLIED ENERGY 2017年 205卷 1394-1407页

作者： Qiu, Yu Li, Ming-Jia Wang, Kun Liu, Zhan-Bin Xue, Xiao-Dai Xi An Jiao Tong Univ Key Lab Thermofluid Sci & Engn Minist Educ Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China Tsinghua Univ State Key Lab Control & Simulat Power Syst & Gene Dept Elect Engn Beijing 100084 Peoples R China Qinghai Univ New Energy Photovolta Ind Res Ctr Xining 810016 Qinghai Peoples R China

Non-uniform solar flux may lead to negative effects in the receiver of the linear Fresnel reflector (LFR), including the failure of the receiver and the fluctuating operation. For reducing these effects, an aiming strategy optimization approach is presented by combining a multi-objective genetic algorithm (GA) and Monte Carlo ray tracing to homogenize the flux distribution in current work. Both the flux non-uniformity index and the optical loss 07100 are used as the objective functions. Based on the approach, first, the flux distributions in the multi Tube Cavity Receiver (MTCR) and the Single-Tube Receiver with a Secondary Collector (STRSC) are optimized at a typical condition. Optimal results indicate that the GA optimization strategy (S2) can reach a compromise between the flux non-uniformity and the optical loss in both MTCR and STRSC systems. Furthermore, the optimal strategy obtained at a specific transversal incidence angle can be applied in a relatively large range around it. Moreover, parameter study indicates that the aiming line number (n(aim)) has little impact on the efficiencies of the two systems. naim has almost no effect on the flux non-uniformity in the MTCR, but the effect is visible in the STRSC. Finally, the application of S2 under a real-time condition indicates that fluxes in the two receivers can be homogenized efficaciously in the whole time range with a small drop of 0.2-3.8 percentage points in efficiency compared with those of traditional one-line aiming strategy (S1). It is also found that the flux non-uniformity indexes of the MTCR are greatly reduced from 0.77-1.09 to 0.02-0.06 when S1 is replaced by S2, and those of the STRSC are steeply reduced from 0.59-0.70 to 0.9-0.37. It is concluded that the present approach is effective and suitable for homogenizing the fluxes in the receivers of LFRs.

关键词： Linear Fresnel solar reflector Aiming strategy optimization Flux distribution non-uniformity Optical loss Monte Carlo ray tracing multi-objective genetic algorithm

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An effective multi-objective genetic algorithm based on immune principle and external archive for multi-objective integrated process planning and scheduling

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2017年第9-12期91卷 3145-3158页

作者： Luo, Guofu Wen, Xiaoyu Li, Hao Ming, Wuyi Xie, Guizhong Zhengzhou Univ Light Ind Mech & Elect Engn Inst 5 Dongfeng Rd Zhengzhou 450002 Henan Peoples R China

Process planning and scheduling are two major sub-systems in a modern manufacturing system. In traditional manufacturing system, they were regarded as the separate tasks to perform sequentially. However, considering their complementarity, integrating process planning and scheduling can further improve the performance of a manufacturing system. Meanwhile, the multiple objectives are needed to be considered during the realistic decision-making process in a manufacturing system. Based on the above requirements from the real manufacturing system, developing effective methods to deal with the multi-objective integrated process planning and scheduling (MOIPPS) problem becomes more and more important. Therefore, this research proposes a multi-objective genetic algorithm based on immune principle and external archive (MOGA-IE) to solve the MOIPPS problem. In MOGA-IE, the fast non-dominated sorting approach used in NSGA-II is utilized as the fitness assignment scheme and the immune principle is exploited to maintain the diversity of the population and prevent the premature condition. Moreover, the external archive is employed to store and maintain the Pareto solutions during the evolutionary process. Effective genetic operators are also designed for MOIPPS. To test the performance of the proposed algorithm, three different scale instances have been employed. And the proposed method is also compared with other previous algorithms in literature. The results show that the proposed algorithm has achieved good improvement and outperforms the other algorithms.

关键词： Integrated process planning and scheduling multi-objective genetic algorithm Immune principle External archive

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Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

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INTERNATIONAL JOURNAL OF CONTROL 2017年第4期90卷 661-683页

作者： Montazeri, A. West, C. Monk, S. D. Taylor, C. J. Univ Lancaster Dept Engn Gillow Ave Lancaster England

This paper concerns the problem of dynamic modelling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual-manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model-orientated research using the same machine, the paper develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimise the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivisation of an output error single-performance index. The developed algorithm utilises a multi-objective genetic algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of 'true' parameters) and experimental data. Both simulation and experimental results show that multi-objectivisation has improved convergence of the estimated parameters compared to the single-objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem.

关键词： Parameter estimation system identification nonlinear model multi-objective genetic algorithm hydraulic manipulator

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Time Series Forecasting Using a Two-Level multi-objective genetic algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans

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algorithmS 2018年第8期11卷 123-123页

作者： Al-Douri, Yamur K. Hamodi, Hussan Lundberg, Jan Lulea Univ Technol Div Operat & Maintenance Engn SE-97187 Lulea Sweden Univ Mosul Coll Engn Dept Mech Engn AZ-6321 Mosul Iraq

The aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 is for the process of forecasting time series cost data, while level 2 evaluates the forecasting. Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model. Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with using the ARIMA model only. The results show the drawbacks of time series forecasting using only the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In level 2, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.

关键词： ARIMA model data forecasting multi-objective genetic algorithm regression model

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Optimization of autoregressive, exogenous inputs-based typhoon inundation forecasting models using a multi-objective genetic algorithm

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ENGINEERING OPTIMIZATION 2017年第7期49卷 1211-1225页

作者： Ouyang, Huei-Tau Natl Ilan Univ Dept Civil Engn Yilan Taiwan

Three types of model for forecasting inundation levels during typhoons were optimized: the linear autoregressive model with exogenous inputs (LARX), the nonlinear autoregressive model with exogenous inputs with wavelet function (NLARX-W) and the nonlinear autoregressive model with exogenous inputs with sigmoid function (NLARX-S). The forecast performance was evaluated by three indices: coefficient of efficiency, error in peak water level and relative time shift. Historical typhoon data were used to establish water-level forecasting models that satisfy all three objectives. A multi-objective genetic algorithm was employed to search for the Pareto-optimal model set that satisfies all three objectives and select the ideal models for the three indices. Findings showed that the optimized nonlinear models (NLARX-W and NLARX-S) outperformed the linear model (LARX). Among the nonlinear models, the optimized NLARX-W model achieved a more balanced performance on the three indices than the NLARX-S models and is recommended for inundation forecasting during typhoons.

关键词： Inundation typhoon ARX multi-objective genetic algorithm Pareto-optimal set

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Optimization investigation on configuration parameters of spiral-wound heat exchanger using genetic Aggregation response surface and multi-objective genetic algorithm

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APPLIED THERMAL ENGINEERING 2017年 119卷 603-609页

作者： Wang, Simin Jian, Guanping Xiao, Juan Wen, Jian Zhang, Zaoxiao Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Shanxi Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Peoples R China

Based on the method combining genetic Aggregation response surface and multi-objective genetic algorithm, the effects of configuration parameters of spiral-wound heat exchanger (SWHE) on flow and heat transfer characteristics were numerically studied. The results show that the shell-side pressure drop of the spiral-wound heat exchanger decreases with the increase of layer pitch, winding angle and tube pitch, respectively. The shell-side heat transfer coefficient of the spiral-wound heat exchanger decreases with the increase of layer pitch and increases with the external diameter of tube. The shell-side heat transfer coefficient increases firstly with the increase of the winding angle and then decreases. The sensitivity analysis also shows that the shell-side flow and heat transfer characteristics are mainly affected by the winding angle. Under the working condition, the pressure drop and heat transfer coefficient are both negatively correlated with the layer pitch. And the winding angle is negatively correlated with the pressure drop, but positively correlated with the heat transfer coefficient. Three optimal configurations were obtained by the multi-Object genetic algorithm based on genetic Aggregation response surface. Compared with the original configuration, the average heat transfer coefficient of improved ones is enhanced by 2.93%, while the average pressure drop is reduced by 40.27%. The results are of great significance for the design of spiral-wound heat exchanger. (C) 2017 Published by Elsevier Ltd.

关键词： Spiral-wound heat exchanger Structural parameters genetic Aggregation multi-objective genetic algorithm

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CFD modeling and optimization of a latent heat storage unit for running a solar assisted single effect Li-Br absorption chiller using multi-objective genetic algorithm

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SUSTAINABLE CITIES AND SOCIETY 2017年 34卷 321-334页

作者： Ghorbani, Babak Kowsary, Farshad Ebrahimi, Sasan Vijayaraghavan, Krishna Simon Fraser Univ Sch Mechatron Syst Engn 250-13450 102 Ave Surrey BC V3T 0A3 Canada Univ Tehran Mech Engn Dept Tehran Iran

In this paper, the feasibility of a solar absorption refrigeration system to be powered by a latent heat storage (LHS) unit is investigated for a representative building. A single effect absorption chiller, utilizing Li-Br and water as working fluids is thermodynamically simulated. Then, the simulation of the latent heat storage unit is performed by applying finite difference method and the results were validated by the researches in the literature. Then, the geometry of a phase change material (PCM) based LHS system was optimized using multi-objective genetic algorithm for simultaneously minimizing the charging time, and maximizing the discharging time. Since the paper considers conflicting objectives, a Pareto front is presented that can be used for obtaining the optimum geometry according to the environmental conditions and working hours of the absorption system. As an illustrative example, the designed heat storage system was shown to be able to drive the 72 kW generator of an absorption system, for at least 10 h of operation in the discharging mode with the absence of sunlight. Therefore, it is possible to run absorption chillers under low-load operation conditions using the solar energy if the appropriate storage unit, such as what is introduced here, is used.

关键词： Single effect absorption system Li-Br and water Latent heat storage multi-objective genetic algorithm Optimization

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