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32nd Chinese Control And Decision Conference (CCDC)

作者： Li, Fei Shen, Hao Wang, Yudong Dai, Mingcheng Park, Ju H. Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243002 Peoples R China Yeungnam Univ Dept Elect Engn 280 Daehak Ro Kyongsan 712749 South Korea

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158556

Due to the high experimental cost in function evaluation procedure, commonly used evolutionary algorithms are difficult to solve expensive multi-objective optimization problems. In this paper, we propose a new modified inverted generational distance based evolutionary algorithm to address this issue. The proposed method has introduced a Kriging model to replace the real model. Considering the better characteristics of the IGD(+) indicator, we select the performance indicator to balance the convergence and diversity of the population. In addition, we have embedded the mean value and mean squre error into the IGD(+) indicator. Meanwhile, two archives management strategy is introduced to store the non-dominated individuals and update the surrogate model. The proposed algorithm has been conducted on some benchmark test problems compared with four related algorithms. The experimental results have validated that the proposed algorithm is suitable for solving the expensive multi-objective optimization problems.

关键词： The IGD(+) indicator Kriging evolutionary multi-objective optimization algorithm two archives management strategy

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The IGD+ indicator based evolutionary algorithm for expensive multi-objective optimization problems

The IGD+ indicator based evolutionary algorithm for expensiv...

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第32届中国控制与决策会议

作者： Fei Li Hao Shen Yudong Wang Mingcheng Dai Ju H.Park School of Electrical and Information Engineering Anhui Unviersity of Technology Department of Electrical Engineering Yeungnam University

Due to the high experimental cost in function evaluation procedure,commonly used evolutionary algorithms are difficult to solve expensive multi-objective optimization *** this paper,we propose a new modified inverted generational distance based evolutionary algorithm to address this *** proposed method has introduced a Kriging model to replace the real *** the better characteristics of the IGD indicator,we select the performance indicator to balance the convergence and diversity of the *** addition,we have embedded the mean value and mean squre error into the IGD ***,two archives management strategy is introduced to store the nondominated individuals and update the surrogate *** proposed algorithm has been conducted on some benchmark test problems compared with four related *** experimental results have validated that the proposed algorithm is suitable for solving the expensive multi-objective optimization problems.

关键词： The IGD+ indicator Kriging evolutionary multi-objective optimization algorithm two archives management strategy

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Heterogeneous ensemble short-term integrated load forecasting considering differentiated cycle-trend properties

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 156卷

作者： Huang, Nantian Wang, Yaoyao Li, Bingling Cai, Guowei Zhang, Liang Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin 132012 Jilin Province Peoples R China

Aiming at the Integrated Energy System (IES), there are key problems such as the potential change pattern of seasonal differentiation of multiple loads, the lack of mining complex coupling characteristics, the limited generalization ability of forecasting models and the weak interpretability. A heterogeneous integrated short-term multiple load forecasting method considering differentiated cycle-trend characteristics is proposed. Firstly, multiple Seasonal Trend decomposition using Loess (MSTL) is used to decompose the multivariate loads into multi-timescale cycle and trend series, and to construct multi-timescale cycle-trend feature sets, which breaks through the limitations of the traditional single-cycle decomposition in characterizing inter-seasonal coupling characteristics. Secondly, the evolutionary multi-objective optimization mechanism based on the dual indexes of diversity and accuracy is introduced into the Stacking integration framework for the first time, and the seasonal adaptive optimal model combination selection of electric/cooling/heating loads is realized. Finally, a two-layer interpretable analysis paradigm based on Shapley Additive Explanations (SHAP) values is established to improve the interpretability of the proposed multivariate load forecasting method by performing input feature attribution analysis from both global and individual dimensions. The experimental analysis is carried out on the example of Tempe Campus IES of Arizona State University in the U.S.A. The results show that the average percentage errors of electricity, cooling, and heating forecasts of the proposed method reach 1.783 %, 1.012 %, and 2.225 %, respectively, demonstrating superior forecasting accuracy compared to existing methods.

关键词： multivariate load forecasting evolutionary multi-objective optimization algorithm Stacking ensemble learning Heterogeneous ensemble Interpretability

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A dynamic multi-objective optimization model with interactivity and uncertainty for real-time reservoir flood control operation

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APPLIED MATHEMATICAL MODELLING 2019年 74卷 606-620页

作者： Liu, Xiaolin Luo, Jungang Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg Xian 710048 Shaanxi Peoples R China

Despite the successes of both multi-objective optimization and uncertainty handling techniques in reservoir flood control operation, no work has been done yet on developing and investigating dynamic multi-objective optimization models for this problem. In this work, a dynamic multi-objective optimization model with interactivity and uncertainty was developed for the real-time reservoir flood control operation. Accordingly, a dynamic multi-objective optimization algorithmic framework with two newly designed change reaction strategies was proposed for solving the proposed dynamic model. Following the proposed algorithmic framework, any evolutionary multi-objective optimization algorithm can be converted into a dynamic optimizer. After investigating the difficulty variation of the proposed dynamic model, the effectiveness and robustness of the proposed algorithmic framework have been validated based on experiential studies on two typical floods of Ankang reservoir. (C) 2019 Published by Elsevier Inc.

关键词： Reservoir flood control operation Dynamic multi-objective optimization model Interactivity evolutionary multi-objective optimization algorithm

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Solving High objective Problems in Fixed Interactions with the Decision Maker

Solving High Objective Problems in Fixed Interactions with t...

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IEEE Congress on evolutionary Computation (CEC)

作者： Sinha, Ankur Pandey, Anmol Deb, Kalyanmoy Aalto Univ Sch Econ Dept Informat & Serv Econ Helsinki 00076 Finland

ISBN: (纸本)9781467315098

In recent advancements towards handling high objective optimization problems, it is proposed to progressively integrate the decision maker with the execution of an evolutionary multi-objective optimization algorithm. Preferences from the decision maker are accepted at the intermediate steps of the algorithm and a progress towards the most preferred point is made. In this paper, we extend the work on 'progressively interactive evolutionary multi-objective optimization using value function' ( PI-EMO-VF) by allowing the optimization to be performed in a fixed number of interactions with the decision maker. In the PI-EMO-VF procedure, information is accepted from the decision maker, which is utilized by the evolutionary algorithm to perform a focused search in the region of interest. However, it is not possible to restrict the number of interactions required to handle an optimization problem. This paper contributes towards, solving the optimization problem in a pre-decided number of decision maker calls. Once the available budget of decision maker calls are known, it is optimally utilized to get close to the most preferred point on the Pareto-frontier. The paper evaluates the performance of the modified PI-EMO-VF algorithm on two, three and five objective test problems. A comparative study is performed against the previous proposal for the PI-EMO-VF procedure.

关键词： evolutionary multi-objective optimization algorithm multiple criteria decision-making interactive multi-objective optimization algorithm value function decision maker calls preference based multi-objective optimization

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Progressively Interactive evolutionary multi-objective optimization Method Using Generalized Polynomial Value Functions

Progressively Interactive Evolutionary Multi-Objective Optim...

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2010 IEEE World Congress on Computational Intelligence

作者： Sinha, Ankur Deb, Kalyanmoy Korhonen, Pekka Wallenius, Jyrki Aalto Univ Sch Econ Dept Business Technol FIN-00101 Helsinki Finland

ISBN: (纸本)9781424481262

This paper advances and evaluates a recently proposed progressively interactive evolutionary multi-objective optimization algorithm. The algorithm uses preference information from the decision maker during the intermediate generations of the EMO and produces the most preferred solution on the Pareto-optimal front. The progress towards the Pareto-optimal front is made by approximating decision maker's value function. In this paper, a generalized polynomial value function has been proposed and the procedure to fit the value function to the decision maker's preference information has been described. The generality of the procedure of fitting a value function to the decision maker's preferences has been shown by using other existing value functions from the literature. The proposed generic polynomial value function has been incorporated in the PI-EMO-VF algorithm to efficiently approximate the decision maker's value function. The paper then evaluates the performance of the PI-EMO-VF algorithm on three and five objective test problems with constraints. It also evaluates the efficacy of the procedure in producing the most preferred solution when the decision maker is unable to provide perfect information, i.e., the decision maker finds certain pairs of solutions in the objective space to be incomparable. Results have been presented for three and five objective constrained test problems using the procedure.

关键词： evolutionary multi-objective optimization algorithm multiple criteria decision-making interactive multi-objective optimization algorithm value/utility function preference based multi-objective optimization

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