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Pareto optimisation of grillage system with multi-objectives

INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2009年第3期8卷 213-221页

作者： Kim, Yunyoung Kim, Byeong-Il Park, Joo-Shin Mokpo Natl Maritime Univ Dept Naval Architecture & Ocean Engn Mokpo Si 530701 Jeollanam Do South Korea Samsung Heavy Ind Co Ltd Marine Res Inst Jangpyung Dong 530 Geoje Si Kyeong Nam South Korea

Many real-world design problems involve simultaneous optimisation of multiple objectives. The considered grillage design is a multi-objective optimisation problem since there are two objective functions, namely: the volume design and the cost design. Thus, the solution of this optimisation problem requires specialised method suitable for multi-objective problems. In this article, a multi-objective design optimisation using real-coded genetic algorithm is proposed for the Pareto-optimality of grillage system. The objective functions in the optimisation problem measure the design sensitivities in grillage system. The non-linear constrained multi-objective optimisation is very important from the point of view of practical problem solving. Therefore, the real-coded genetic algorithm with multiple genetic operators is proposed to find the optimum grillage system without handling any of the penalty functions. Direct strength calculation defined from the class rules of DNV was applied for structural design of grillage system. The hybrid method (real-coded genetic algorithm including the non-dominated sorting and sharing approaches) performs a marvellous explorability in finding a diverse set of solutions and in converging near the true Pareto-optimal set. The results obtained are very encouraging since they show that we can produce an important portion of the Pareto-front at a very low computational time frame.

关键词： grillage system Pareto-optimal set real-coded genetic algorithm RGA

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Improvement of power system stability using genetically optimized SVC controller

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2014年第4期5卷 475-486页

作者： Hameed, Salman Garg, Pallavi Aligarh Muslim Univ ZH Coll Engn & Technol Elect Engn Dept Aligarh 202001 Uttar Pradesh India

Single machine infinite bus (SMIB) power system and multi-machine power system (MMPS) stability improvement by tuning of static var compensator (SVC) based controller parameters are investigated in the proposed method. The design problem is formulated as an optimization problem with a time-domain simulation-based objective function and real-coded genetic algorithm is used for searching optimal controller parameters. SMIB power system and MMPS models are developed using MATLAB's SIMULINK which incorporates SVC controller. A fault is created on the transmission line. The simulation results of SMIB power system and MMPS without SVC controller and with SVC controller are presented. The simulation results are analyzed which show that the power system becomes unstable on the occurrence of the fault if SVC controller is not used. This paper proves the effectiveness of the proposed design. Thus the proposed method enhances the power system stability.

关键词： Power system stability Damping of power oscillations SVC real-coded genetic algorithm Multimachine power system

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Expected value multiobjective portfolio rebalancing model with fuzzy parameters

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INSURANCE MATHEMATICS & ECONOMICS 2013年第2期52卷 190-203页

作者： Gupta, Pankaj Mittal, Garima Mehlawat, Mukesh Kumar Univ Delhi Dept Operat Res Delhi 110007 India Apeejay Sch Management Dept Decis Sci New Delhi India

In this paper we develop a multicriteria credibilistic framework for portfolio rebalancing. We use an expected value model with fuzzy parameters considering return, risk and liquidity as key financial criteria. The transaction costs are assumed to be paid on the basis of incremental discounts and are adjusted in the net return of the portfolio. A solution procedure based on fuzzy goal programming and a hybrid intelligent algorithm that combines fuzzy simulation with a real-coded genetic algorithm is presented to solve the portfolio rebalancing problem. The approach adopted here has the advantage of handling the multicriteria portfolio rebalancing problem where the fuzzy parameters are characterized by general functional forms. An empirical study is included to demonstrate the effectiveness of the solution approach and efficiency of the model in practical applications of rebalancing an existing portfolio. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Fuzzy portfolio rebalancing Transaction costs Expected value model Multiobjective programming real-coded genetic algorithm

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Modeling of resilience based on categorized recovery scenario and improving resilience with viscous damper

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JAPAN ARCHITECTURAL REVIEW 2022年第3期5卷 279-294页

作者： Akehashi, Hiroki Takewaki, Izuru Kyoto Univ Grad Sch Engn Dept Architecture & Architectural Engn Kyotodaigaku Katsura Nishikyo Kyoto 6158540 Japan

In this study, a model for the evaluation of a building's resilience and recovery time is proposed, and a new method is developed for the optimal viscous damper placement for a targeted resilience. The features of this model are as follows: 1) building components are categorized into systems in view of their functionality, 2) recovery time is regarded as a function of the damage to building components and human resources for repairing the components, 3) the model is applicable to structural design. This design method uses a type of real-coded genetic algorithm (GA). The effective use of a constraint on the sum of the added damper damping coefficients enables an efficient search for the solution.

关键词： facility multi-objective optimization real-coded genetic algorithm recovery time resilience viscous damper

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A multiobjective portfolio rebalancing model incorporating transaction costs based on incremental discounts

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OPTIMIZATION 2014年第10期63卷 1595-1613页

作者： Mittal, Garima Mehlawat, Mukesh Kumar Univ Delhi Dept Operat Res Delhi 110007 India

In this paper, we propose a multiobjective model of portfolio rebalancing problem considering return, risk and liquidity as key financial criteria. Further, a more realistic situation of financial market is considered where the portfolio, at the end of a typical time period, will be modified by buying and/or selling asset(s) in response to changing conditions. We assume that the transaction costs are paid on the basis of incremental discounts and are adjusted in the net return of the portfolio. A real-coded genetic algorithm (RGGA) is developed to solve the portfolio rebalancing problem and build an optimal portfolio. An empirical study is included to illustrate the behaviour of the proposed model using data of some randomly selected assets listed on the National Stock Exchange (NSE), Mumbai, India.

关键词： portfolio selection multiobjective optimization real-coded genetic algorithm transaction costs

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Fitting of self-exciting threshold autoregressive moving average nonlinear time-series model through genetic algorithm and development of out-of-sample forecasts

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STATISTICS 2014年第5期48卷 1166-1184页

作者： Ghosh, Himadri Prajneshu Samanta, Sandipan Indian Agr Stat Res Inst Div Biometr & Stat Modelling New Delhi 110012 India

Self-exciting threshold autoregressive moving average (SETARMA) nonlinear time-series model is considered here. Sufficient conditions for invertibility and stationarity are derived. Parameter estimation algorithm is developed by employing real-coded genetic algorithm stochastic optimization procedure. A significant feature of the work done is that optimal out-of-sample forecasts up to three-step ahead and their forecast error variances are derived analytically. Relevant computer programs are written in statistical analysis system (SAS) and C. As an illustration, annual mackerel catch time-series data are considered. Forecast performance of the fitted model for hold-out data is evaluated by using Naive and Monte Carlo approaches. It is found that optimal out-of-sample forecast values are quite close to actual values and estimated variances are quite close to theoretical values. Superiority of the SETARMA model over the SETAR model for equal predictive ability through Diebold-Mariano test is also established.

关键词： SETARMA nonlinear time-series model real-coded genetic algorithm mackerel catch data mean square forecasting error optimal out-of-sample forecast

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Artificial intelligence power controller of fuel cell based DC nanogrid

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RENEWABLE ENERGY FOCUS 2020年 34卷 120-128页

作者： Kumar, Saurabh Krishnasamy, Vijayakumar Neeli, Satyanarayana Kaur, Rajvir MNIT EED Jaipur Rajasthan India IITDM ECED Kancheepuram India SUTD ISTD Singapore Singapore

The expansion of DC nanogrid is projected in rural and remote areas for electrification. Conventional way of powering the rural areas is to extend the central grid to that area. However, extending the network of central grid is a cumbersome process and it involves huge capital cost. Further, with the development of power electronics and evolution of renewable resource, a standalone nanogrid is a simple and feasible solution to power the rural areas. Therefore, a clean and sustainable fuel cell based nanogrid is proposed as a step towards rural electrification. The output voltage of the fuel cell is varying in nature which could damage the voltage sensitive electronic equipment of the house hold. Therefore, boost converter based interfacing unit with an intelligent controller is proposed in this paper to regulate the voltage. The intelligent controller improves the dynamic response of the system and regulates the voltage at the DC distribution bus irrespective of source side or load side disturbances. The proposed intelligent proportional integral and derivative controller parameters are tuned using a binary genetic algorithm and real-coded genetic algorithm by formulating an optimal control problem. The proposed controller is model independent, simple in implementation and gives an excellent dynamic response.A laboratory scaled hardware prototype is developed to validate the performance of proposed controller.

关键词： Boost converter Binary genetic algorithm Fuel cell Nanogrid real-coded genetic algorithm

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Learning Fuzzy Cognitive Maps using Structure Optimization genetic algorithm

Learning Fuzzy Cognitive Maps using Structure Optimization G...

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3rd International Conference on Innovative Network Systems and Applications (iNetSApp) held in conjunction with Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Poczeta, Katarzyna Yastrebov, Alexander Papageorgiou, Elpiniki I. Kielce Univ Technol Al Tysiaclecia Panstwa Polskiego 7 PL-25314 Kielce Poland CERTH Ctr Res & Technol Hellas Thermi 57001 Greece Technol Educ Inst TEI Cent Greece Lamia 35100 Greece

ISBN: (纸本)9788360810668

Fuzzy cognitive map (FCM) is a soft computing methodology that allows to describe the analyzed problem as a set of nodes (concepts) and connections (links) between them. In this paper a new Structure Optimization genetic algorithm (SOGA) for FCMs learning is presented for modeling complex decision support systems. The proposed approach allows to automatic construct and optimize the FCM model on the basis of historical multivariate time series. The SOGA defines a new learning error function with an additional penalty for highly complexity of FCM understood as a large number of concepts and a large number of connections between them. The aim of this study is the analysis of usefulness of the Structure Optimization genetic algorithm for fuzzy cognitive maps learning. Comparative analysis of the SOGA with other well-known FCM learning algorithms (real-coded genetic algorithm and Multi-Step Gradient Method) was performed on the example of prediction of rented bikes count. Simulations were done with the ISEMK (Intelligent Expert System based on Cognitive Maps) software tool. The obtained results show that the use of SOGA allows to significantly reduce the structure of the FCM model by selecting the most important concepts and connections between them.

关键词： Fuzzy Cognitive Maps Structure Optimization genetic algorithm real-coded genetic algorithm Multi-Step Gradient Method

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Black-Box Optimization Benchmarking for Noiseless Function Testbed Using A Direction-Based RCGA 12

Black-Box Optimization Benchmarking for Noiseless Function T...

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14th International Conference on genetic and Evolutionary Computation Conference (GECCO)

作者： Chuang, Yao-Chen Chen, Chyi-Tsong Feng Chia Univ Dept Chem Engn 100 Wenhwa Rd Taichung 40724 Taiwan

ISBN: (纸本)9781450311786

This paper benchmarks a novel and efficient real-coded genetic algorithm (RCGA) enhanced from our previous work [1] on the noisefree BBOB 2012 testbed. The enhanced algorithm termed as direction-based RCGA (DBRCGA) uses relative fitness information to direct the crossover toward a direction that significantly improves the objective fitness. As a base of performance evaluation and comparisons, the maximum number of function evaluations (#FEs) for each test run is set to 10(5) times to the problem dimension. Extensive benchmarking test results reveal that all functions can be solved by DBRCGA in the low search dimensions. Although the DBRCGA shows the difficulty in getting a solution with the desired accuracy 10(-8) for high conditioning and multi-modal functions within the specified maximum #FEs, the DBRCGA presents good performance in separable function and functions with low or moderate conditioning.

关键词： Benchmarking Black-box optimization Evolutionary algorithms real-coded genetic algorithm

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Artificial Immune Network Model Based Controller for Vibration Suppression of flexible structures

Artificial Immune Network Model Based Controller for Vibrati...

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2010 IEEE World Congress on Computational Intelligence

作者： Salleh, Salihatun Md Tokhi, M. Osman Toha, Siti Fauziah Univ Sheffield Dept Automat Control & Syst Engn Mappin St Sheffield S1 3JD S Yorkshire England

ISBN: (纸本)9781424481262

This paper presents a model based controller with immune inspired algorithm to suppress flexible plate vibration. Artificial immune network (AiNet) algorithm with real-valued random initialisation of antibodies is considered to optimise the system model for use in active vibration controller development in a single-input and multi-output (SIMO) configuration. The AiNet model based controller thus developed shows the capability of the approach with relatively better accuracy in comparison to using real-coded genetic algorithm. The simulation results also reveal improvement in vibration reduction and less computational time.

关键词： AiNet algorithm SIMO configuration active vibration controller antibody artificial immune network model based controller artificial immune systems flexible plate vibration flexible structure flexible structures genetic algorithms immune inspired algorithm plates (structures) real-coded genetic algorithm real-valued random initialisation single-input and multi-output configuration vibration control vibration reduction vibration suppression

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