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Credit portfolio optimization: A multi-objective genetic algorithm approach

BORSA ISTANBUL REVIEW 2022年第1期22卷 69-76页

作者： Wang, Zhi Zhang, Xuan Zhang, ZheKai Sheng, Dachen Beijing Univ Technol Int Business Sch Gengdan Inst Beijing Peoples R China Nanjing Audit Univ Inst Econ & Finance Nanjing Peoples R China

The algorithm for optimization of a credit portfolio has not been fully demonstrated. This paper fills the gap in the literature by presenting a general approach for optimizing a credit portfolio by minimizing the default risk of the entire portfolio. Default risk is measured with quadratic weighting and a matrix containing information about the default intensity of two stocks and the correlation in default between them. The default correlation and the default intensity are represented with a novel bivariate intensity model. A multi-objective genetic algorithm is introduced to optimize a credit portfolio with the purpose of overcoming limitations in the analytical method and improving the efficiency of optimization. The algorithm can be applied to a portfolio's credit risk management, which is particularly crucial for investors and regulars in emerging markets. Copyright (C) 2021, Borsa Istanbul Anonim Sirketi. Production and hosting by Elsevier B.V.

关键词： Credit portfolio optimization Heuristic optimization multi-objective genetic algorithm

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A Novel Linear Time Invariant Systems Order Reduction Approach Based on a Cooperative multi-objective genetic algorithm 8th

A Novel Linear Time Invariant Systems Order Reduction Approa...

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8th International Conference on Swarm Intelligence (ICSI)

作者： Ryzhikov, Ivan Brester, Christina Semenkin, Eugene Siberian State Aerosp Univ Krasnoyarsk Russia

ISBN: (纸本)9783319618333;9783319618326

Cooperative multi-objective optimization tool is proposed for solving the order reduction problem for linear time invariant systems. Normally, the adequacy of an order reduction problem solution is estimated using two different criteria, but only one of them identifies the model. In this study, it was suggested to identify the parameters using both of the criteria, and since the criteria are complex and multi-extremum there is a need for a powerful optimization algorithm to be used. The proposed approach is based on the cooperation of heterogeneous algorithms implemented in the islands scheme and it has proved its efficiency in solving various multi-objective optimization problems. It allows us to receive a set of lower order models, which are non-dominated solutions for the given criteria and an estimation of the Pareto set. The results of this study are compared to the results of solving the same problems using various approaches and heuristic optimization tools and it is demonstrated that the set of solutions not only outperforms these approaches by the main criterion, but also provides good solutions with another criterion and a combination of them using the same computational resources.

关键词： Cooperation meta-heuristics multi-objective optimization multi-objective genetic algorithm Linear time invariant system Parameters identification

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A growing complex network design method with an adaptive multi-objective genetic algorithm and an inner link restructuring method

A growing complex network design method with an adaptive Mul...

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Annual Conference of the Society-of-Instrument-and-Control-Engineers (SICE)

作者： Mizuno, Haruki Okamoto, Takashi Koakutsu, Seiichi Hirata, Hironori Chiba Univ Grad Sch Engn Chiba Japan

ISBN: (纸本)9784907764401

Many systems that can be modeled using network structures appear in various fields such as informatics, social science, economics, ecology, biology, and engineering. If these systems can be modeled as complex network systems, a complex network design method that finds a desired network structure can become one of strong tools in large-scale system designs. Conventional complex network design methods generally tackle static networks. On the other hand, a growing complex network design method has been proposed. In this method, the network is grown with determining the link structure of the newly-added node in order to obtain a desired network. In this design method, it is expected that characteristics of the optimization problem solved in the algorithm vary along with growth of the network. However, parameters of the optimization method are fixed;hence, its search process may be inefficient. In the design method, connection targets of a new node are only determined, and the structure of the existing network is not changed. Therefore, if the initial network has a large number of nodes, the desired network may not be obtained by using the design method. In this study, we propose to introduce an inner link restructuring method and an adaptive MOGA, that is, an adaptive SPEA2 into the growing complex network design method to deal with foregoing issues. Then, we confirm the effectiveness of the proposed method through numerical experiments.

关键词： Complex Network multi-objective Optimization Growing Network Network Design multi-objective genetic algorithm

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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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Construction of interpretable and precise fuzzy models using fuzzy clustering and multi-objective genetic algorithm

Construction of interpretable and precise fuzzy models using...

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5th International Conference on Machine Learning and Cybernetics

作者： Xing, Zong-Yi Hou, Yuan-Long Zhang, Yong Jia, Li-Min Gao, Qiang Nanjing Univ Sci & Technol Sch Mech Engn Jiangsu 210094 Peoples R China Beijing Jiaotong Univ Sch Transportat Beijing 100044 Peoples R China

ISBN: (纸本)1424400600

An approach to construct interpretable and precise fuzzy models from data is proposed. Interpretability, which is one of the most important features of fuzzy models, is analyzed first. Then a modified fuzzy clustering algorithm, combined with the least square method, is used to identify the initial fuzzy model. Third, the multi-objective genetic algorithm and interpretability-driven simplification techniques are proposed to evolve the initial fuzzy model to optimize its structure and parameters iteratively, thus interpretability and precision of the fuzzy model are improved. Finally, the proposed approach is applied to the Mackey-Glass tine series, and the results show its validity.

关键词： fuzzy modeling fuzzy clustering multi-objective genetic algorithm TS fuzzy model interpretability

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Optimization of EDM Process Parameters on Aluminum Alloy 6082 by Using multi-objective genetic algorithm 1st

Optimization of EDM Process Parameters on Aluminum Alloy 608...

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1st International Conference on Innovative Product Design and Intelligent Manufacturing System, ICIPDIMS 2019

作者： Md Riyaz, Sk. Srinivas, P. Ramu, I. Mechanical Department Vishnu Institute of Technology Bhimavaram534202 India

ISBN: (纸本)9789811526954

Electric discharge machining is one of the most popular machines which are capable of machining geometrically complex and hard materials, that are precise and difficult to machine such as heat-treated tools, superalloys, heat-resistant steels, carbides, etc;these types of materials are being widely used in aerospace, automobiles, marine industries, etc. This paper aims to investigate the optimal set of process parameters of die sinker EDM on aluminum alloy 6082 with copper tube electrode by varying the input parameters such as pulse-on time (Ton), pulse-off time (Toff), current (I), and voltage (v). An L16 orthogonal array has been designed using the Taguchi method for input parameters to conduct experiments for getting the output variables such as metal removal rate (MRR) and tool wear rate (TWR). Based on the analysis, the experimental results have been carried out using ANOVA method. To find out the optimal set of values, multi-objective genetic algorithm (MOGA) is used to predict the experimental results. Upon comparing the experimental values and predicted values, an optimal value is obtained which is useful to the manufactures to get the high-performance rate of EDM to machine aluminum alloy 6082. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Electric discharge machining multi-objective genetic algorithm Taguchi method

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The Evaluation and Testing System of Automobile Performance Based on multi-objective genetic algorithm

The Evaluation and Testing System of Automobile Performance ...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Wen, Yanling Tan, Kecheng Liuzhou Railway Vocational Technical College Guangxi Liuzhou China

ISBN: (纸本)9798350315455

In the face of the rapid growth of China's car ownership, the variety of vehicle types, and the increasingly complex structure and use conditions of modern cars, the majority of car manufacturers urgently need to further control the performance of cars in the process of car design and production, and effectively measure the performance of cars and analyse and process the results so that they have high practicality and economic benefits. The purpose of this paper is to study the vehicle performance evaluation and testing system based on multi-objective genetic algorithm. The indicators for the evaluation of the vehicle performance are determined, including vehicle dynamics, fuel economy, braking, handling stability and environmental friendliness. The fuzzy comprehensive evaluation method is used to establish the subordinate degree function of each factor, so as to obtain the scores of the vehicle performance evaluation, select the appropriate input and output parameters, and establish the evaluation model using the neural network of multi-objective genetic algorithm. As a result, the overall vehicle performance scores after the two sets of real-world tests were 79 and 80 respectively. © 2023 IEEE.

关键词： fuzzy comprehensive evaluation inspection system multi-objective genetic algorithm performance evaluation whole vehicle performance

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A population randomization-based multi-objective genetic algorithm for gesture adaptation in human-robot interaction

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Science China(Information Sciences) 2021年第1期64卷 188-200页

作者： Luefeng CHEN Wanjuan SU Min LI Min WU Witold PEDRYCZ Kaoru HIROTA School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and *** view of these problems, we propose a population randomization-based multi-objective genetic *** gesture signal is processed with a slight change by imitating the biological evolution mechanisms. In the proposed algorithm, a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population. The weights of the objective function and the elite retention strategy are adopted, and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation. To verify the effectiveness of the algorithm, preliminary application experiments are performed on the gesture adaptation of a robotic arm. The results are compared with the original signal, and the comparison shows that by using the proposed method, the energy consumption is reduced, and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm. These obtained results meet the communication requirements for human-robot interactions such as handshakes. Moreover, the proposed method has better performance, uses less energy, and has a smaller tracking error than the particle swarm optimization, the single-objective genetic algorithm, and the traditional multi-objective genetic algorithm. A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time.

关键词： multi-objective genetic algorithm gesture adaptation human-robot interaction

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Optimization of a high-speed direct-injection diesel engine at low-load operation using computational fluid dynamics with detailed chemistry and a multi-objective genetic algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2010年第D4期224卷 547-563页

作者： Ge, H-W Shi, Y. Reitz, R. D. Willems, W. Univ Wisconsin Engn Res Ctr Madison WI 53706 USA Ford Res Ctr Aachen Germany

A passenger car high-speed direct-injection diesel engine operating at low-load conditions in the modulated kinetic combustion mode was optimized using a multidimensional computational fluid dynamics code and a multi-objective genetic algorithm. Spray targeting, piston bowl geometry, and swirl ratio were optimized. Since the combustion is mainly kinetics controlled, detailed chemistry was considered through a recently developed adaptive multi-grid chemistry (AMC) model. The numerical results from the AMC model, including the pressure and pollutant emissions, were first validated on the baseline engine for a parametric sweep by comparison with the results from the standard KIVA-CHEMKIN model. The AMC model was found to give consistent results with the KIVA-CHEMKIN model, with a computational cost that is less than half that of the KIVA-CHEMKIN model. Optimal designs from the optimization were also validated using the full KIVA-CHEMKIN model and were found to reduce the fuel consumption and/or pollutant emissions. Start-of-injection timing was found to be the primary parameter influencing the fuel consumption and soot emissions for the engine operating in the low-load condition. Later injection benefits the fuel consumption and soot reduction. However, further retardation of the injection timing leads to reduced combustion efficiency and even misfire, and results in higher unburned hydrocarbon emissions. Different piston bowl shapes have different responses to bulk flow motions and the resulting geometry-generated turbulence will affect soot formation and oxidation.

关键词： high-speed direct-injection diesel engine multi-objective genetic algorithm detailed chemistry KIVA-CHEMKIN model adaptive multi-grid chemistry model

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A multi-objective genetic algorithm for intelligent software project scheduling and team staffing

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2013年第1期7卷 59-80页

作者： Stylianou, Constantinos Andreou, Andreas S. Univ Cyprus Dept Comp Sci Nicosia Cyprus Cyprus Univ Technol Dept Comp Engn & Informat 31 Archbishop Kyprianou AvePOB 50329 CY-3036 Lemesos Cyprus

Allocation of human resources is considered one of the most important activities carried out by software project managers, since human resources are essentially the only type of resource utilized in software development. Part of human resource allocation involves the scheduling of tasks and the staffing of teams with suitable developers, which for project managers are activities that are often very difficult to carry out due to the large number of possible permutations and factors influencing selection. In addition, no standardized technique is available for software project managers that can be adopted to carry out these activities. Consequently, proper human resource allocation is now gradually being regarded as a critical factor that can influence software project success and can directly contribute to providing customers with software products on time, within budget and with the adequate level of quality. The aim of the research work, therefore, is to form an approach to help software project managers undertake the responsibility of scheduling projects and forming teams in the best possible way given a set of tasks and developers. The approach employs a multi-objective genetic algorithm to optimize various aspects of scheduling and staffing in the form of objective functions with respect to project duration and developer skills and at the same time handling constraints concerning task dependencies and assignment conflicts. The approach was assessed using a set of scenarios of varying project size and complexity that depict possible real-world software project instances. The results obtained show that the proposed approach is capable of providing feasible project schedules and team assignments for software projects with differing sizes and complexities, whereas its ability to provide optimal solutions is limited by the complexity of software projects. Software project managers do not always have the same goals and criteria when planning for projects. Therefore, t

关键词： Software project management project scheduling team staffing multi-objective genetic algorithm

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