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Reservoir operation using a robust evolutionary optimization algorithm

JOURNAL OF ENVIRONMENTAL MANAGEMENT 2017年 197卷 275-286页

作者： Al-Jawad, Jafar Y. Tanyimboh, Tiku T. Univ Strathclyde Glasgow Dept Civil & Environm Engn 75 Montrose St Glasgow G1 1XJ Lanark Scotland

In this research, a significant improvement in reservoir operation was achieved using a state-of-the-art evolutionary algorithm named Borg MOEA. A real-world multipurpose dam was used to test the algorithm's performance, and the target of the reservoir operation policy was to fulfil downstream water demands in drought condition while maintaining a sustainable quantity of water in the reservoir for the next year. The reservoir's performance was improved by increasing the maximum reservoir storage by 14.83 million m(3). Furthermore, sustainable water storage in the reservoir was achieved for the next year, for the simulated low flow condition considered, while the total annual imbalance between the monthly reservoir releases and water demands was reduced by 64.7%. The algorithm converged quickly and reliably, and consistently good results were obtained. The methodology and results will be useful to decision makers and water managers for setting the policy to manage the reservoir efficiently and sustainably. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： evolutionary optimization algorithm Reservoir operation policy Multipurpose reservoir system Reservoir drawdown limits Self-adaptive recombination Environmental water management

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Study on the application of simulated evolutionary optimization algorithm in optimizing the combination of unit commitments in power system

Study on the application of simulated evolutionary optimizat...

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3rd International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2011)

作者： Liu Qing-song Tong Jia Li Yi-feng Jiaxing Univ Jiaxing 314001 Zhejiang Peoples R China Guangdong Ocean Univ Zhanjiang Guangdong 524088 Peoples R China

ISBN: (纸本)9783037850190

This study adopted a simulated evolutionary optimization algorithm, ant colony optimization algorithm to find the optimal unit commitment operation. The concepts such as status, strategy, and path, etc. were introduced to devise the optimization of unit commitment operation by ant colony optimization algorithm mode, so that the optimal unit commitment operation could be found by ant colony optimization algorithm. To cope with different constraints by additional penalties and restrict the statuses not satisfying the constraints by tabu table, the retrieval of ant colony optimization algorithm could always be performed in feasible region and the retrieval process of the algorithm was effectively conducted. It is feasible and efficient to find the optimal unit commitment operation by ant colony optimization algorithm, which was proved by stimulation.

关键词： Power system Optimal operation Optimal unit commitment operation evolutionary optimization algorithm Ant colony optimization algorithm

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optimization Of Interior Permanent Magnet Synchronous Motor Using evolutionary optimization algorithm

Optimization Of Interior Permanent Magnet Synchronous Motor ...

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作者： Knebl, Ladislav Brno University of Technology

The development of the interior permanent magnet synchronous motor has drawn a big interest over the last decade. This is due to the use of this kind of machine in the automotive industry, thanks to the machine high efficiency and high overload capability compare to other machine types. Using artificial intelligence or evolutionary optimization algorithms is possible to optimize the motor with maximum efficiency, lowest torque ripple and highest average torque, because a huge ammount and variety of geometry combinations are tested. This paper is focused on the overview of generally used optimization algorithms and optimization is demonstrated on Self-Organizing Migrating algorithm (SOMA). Cost function and weight coefficients are also presented and used for optimization.

关键词： Maxwell SOMA optimization evolutionary optimization algorithm Finite element analysis Interior permanent magnet

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A solution potential-based adaptation reference vector evolutionary algorithm for many-objective optimization

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SWARM AND evolutionary COMPUTATION 2024年 84卷

作者： Li, Wei Chen, Yangtao Dong, Yuehua Huang, Ying JiangXi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Peoples R China Gannan Normal Univ Sch Math & Comp Sci Ganzhou 341000 Peoples R China

Decomposition-based multiobjective evolutionary algorithms are widely utilized for solving multiobjective optimization problems (MOPs). These algorithms have a higher degree of diversity due to a uniform distribution of the reference vectors in the objective space. However, these algorithms may struggle to select a suitable candidate set for solving many-objective optimization problems (MaOPs) with irregular Pareto fronts (PFs). To solve this problem, a solution potential-based adaptation reference vector evolutionary algorithm (SPARVEA) is proposed in this paper. In the algorithm, a concept called solution potential is presented to assess whether the direction of convergence of the solution to the ideal solution has potential. The solution potential is obtained with an evaluation function to calculate the potentials of the corresponding solutions. A solution potential based adaptation strategy is then designed for adapting the reference vector to better guide the direction of convergence of the solution when solving MaOPs with irregular PFs. A modified angle penalty distance (mAPD) is adjusted to improve the convergence rate of the solution set in many-objective optimization problems. The proposed algorithm SPARVEA is compared with state-of-the-art many-objective evolutionary algorithms on four test sets with irregular PFs and four practical problems. Experimental results demonstrate the superior performance of SPARVEA on many-objective optimization problems. This contribution helps to advance the development of efficient algorithms for solving many-objective optimization problems.

关键词： evolutionary optimization algorithm Adaptation reference vector Many-objective optimization Irregular Pareto fronts Angle penalty distance

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Applying evolutionary optimization algorithms for improving fuzzy C-mean clustering performance to predict the deformation modulus of rock mass

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INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES 2019年 113卷 172-182页

作者： Majdi, Abbas Beiki, Morteza Univ Tehran Univ Coll Engn Sch Min Engn Tehran Iran Ferdowsi Univ Mashhad Fac Sci Dept Geol Mashhad Iran

This paper focuses on the capability of the evolutionary computation methods namely, genetic algorithm (GA) and particle swarm optimization (PSO) in design and optimizing the fuzzy c-means clustering (FCM) structure and their applications to predict the deformation modulus of rock masses. Accordingly, evolutionary algorithms are used to tune the pre-determined FCM clustering-based model to make better the accuracy of modulus estimation. A new empirical equation with the aid of multiple regression analysis is also suggested and on the basis of it a prediction chart is presented for determination of the rock mass deformation modulus. Finally, a comprehensive credibility assessment of the prediction performances of some existing empirical equations is done and the results are compared with that obtained by the evolutionary algorithms based FCM clustering models. It is concluded that the new proposed approaches provide more accurate results compared with the existing empirical equations.

关键词： Deformation modulus of rock mass Regression analysis Fuzzy C-means clustering (FCM) evolutionary optimization algorithm Genetic algorithm (GA) Particle swarm optimization (PSO)

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Multi-objective flexible job shop scheduling based on feature information optimization algorithm

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COMPUTERS & OPERATIONS RESEARCH 2025年 179卷

作者： Guo, Zeyin Wei, Lixin Zhang, Jinlu Hu, Ziyu Sun, Hao Li, Xin Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China

Multi-objective optimization methods are increasingly used in job shop scheduling optimization strategies. However, in the design process of multi-objective optimization strategies, a neighborhood search is performed on all solutions in the optimization algorithm, resulting in a time-consuming search. In the algorithm selection process, feature information carried by individuals is often ignored, leading to a lack of targeted guidance ability in the algorithm. To address the limitations of the existing methods, a multi-objective flexible job shop scheduling method based on a feature information optimization algorithm (FIOA) was proposed. First, a framework of multiple group optimization algorithms was applied to construct diverse groups. Subsequently, a representative individual selection strategy was applied to mine individual offspring information and accelerate population convergence. To balance the exploration ability and computational resources of the FIOA, multiple neighborhood search rules were used to improve the utilization rate of individual offspring. In this study, the parameter configuration of the proposed algorithm was calibrated using the Taguchi method. To evaluate the effectiveness and superiority of the FIOA, each improvement of the FIOA algorithm was evaluated. In addition, it was compared with state-of-the-art algorithms in benchmark tests, and the results showed that the FIOA outperformed the other algorithms in solving flexible job shop scheduling.

关键词： Flexible job shop scheduling problem Multi-objective optimization Feature information selection evolutionary optimization algorithm Variable neighborhood search

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Genetic evolutionary structural optimization

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JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH 2008年第3期64卷 305-311页

作者： Liu, Xia Yi, Wei-Jian Li, Q. S. Shen, Pu-Sheng Hunan Univ Coll Civil Engn Changsha 410082 Hunan Peoples R China City Univ Hong Kong Dept Bldg & Construct Kowloon Hong Kong Peoples R China

evolutionary structural optimization (ESO) is based on a simple idea that an optimal structure (with maximum stiffness but minimum weight) can be achieved by gradually removing ineffectively used materials from design domain. In general, the results from ESO are likely to be local optimums other than the global optimum desired. In this paper, the genetic algorithm (GA) is integrated with ESO to form a new algorithm called Genetic evolutionary Structural optimization (GESO), which takes the advantage of the excellent behavior of the GA in searching for global optimums. For the developed GESO method, each element in finite element analysis is an individual and has its own fitness value according to the magnitude of its sensitivity number. Then, all elements in an initial domain constitute a whole population in GA. After a number of generations, undeleted elements will converge to the optimal result that will be more likely to be a global optimum than that of ESO. To avoid missing the optimum layout of a structure in the evolution, an interim thickness is introduced into GESO and its validity is demonstrated by an example. A stiffness optimization with weight constraints and a weight optimization with displacement constraints are studied as numerical examples to investigate the effectiveness of GESO by comparison with the performance of ESO. It is shown through the examples that the developed GESO method has powerful capacity in searching for global optimal results and requires less computational effort than ESO and other existing methods. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： structural optimization genetic algorithm evolutionary optimization algorithm

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Fast-charging station Leipzig - the floating roof on a computer- generated endless support

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STAHLBAU 2023年第7期92卷 407-417页

作者： Eisenbach, Philipp Bollinger, Klaus Heimrath, Moritz Prengel, Jon BG Ingenieure Bollinger & Grohmann GmbH Westhafenpl 1 D-60327 Frankfurt Germany Bollinger & Grohmann ZT GmbH Franz Josefs Kai 31-1-4 A-1010 Vienna Austria Raumwerk Gesell Architektur & Stadtplanung Gutleutstr 163-167 D-60327 Frankfurt Germany

For a fast-charging station at the production site of a sports car manufacturer, a roof was to be developed that would transport the image of the brand in addition to I function as weather protection. The existing technical infrastructure, i. e., power lines that could not be built over and movement and maintenance spaces for the charging columns, only allowed for a few and, above all, irregular support points and support positions. The design process results in a seemingly floating roof whose cladding is modelled on an aerofoil supported on a single endless column. The infinite support Spline, iconographically analogous to an initially simple appearing bent-up paper clip, ensures both the clamping of the cantilevered roof and the global reinforcement through the spatial support positions and inclinations. The geometry was developed using an evolutionary optimisation algorithm with the most outstanding efficiency while complying with the given geometric restrictions. Within the parametric structural model, in addition to the control and analysis of the static performance, the geometric implementation of the milled circular arcs at the head and foot points was also carried out. The entire process, from the development and variant study to the analysis and documentation to the production, thus took place in a closed digital process chain. Therefore, it was possible to achieve the design goal of contrasting a flat roof and a filigree support structure.

关键词： steel roof design process evolutionary optimization algorithm geometry development milled steel components digital process digitalisation conception and design special structures

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Effect of caustic addition in epoxy polymerization process: A single and multi-objective evolutionary approach

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MACROMOLECULAR THEORY AND SIMULATIONS 2004年第2期13卷 152-161页

作者： Raha, S Majumdar, S Mitra, K Tata Consultancy Serv Mfg Practice Pune 411013 Maharashtra India Tata Res Dev & Design Ctr Pune 411013 Maharashtra India

An epoxy polymerization process is studied in semi-batch mode. Caustic has a very critical influence on epoxy polymerization process, which is modeled as a set of highly nonlinear coupled ODE's (ordinary differential equations). Owing to the highly complicated, nonlinear domain of analysis, "Differential Evolution (DE)" and "Genetic algorithm (GA)" are used as optimization tools to identify the (M) over bar (n)-PDI Pareto set and study the best operating strategy with respect to NaOH addition. The moment of various oligomeric components during the semi-batch polymerization is also presented for a better understanding of the process. This study demonstrates the potential of evolutionary optimization algorithm to identify various operating philosophies of a semi-batch epoxy reactor for a targeted product quality.

关键词： epoxy polymerization evolutionary optimization algorithm molecular weight distribution/molar mass distribution oligomers reactive processing

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Integrating an Ensemble Surrogate Model's Estimation into Test Data Generation

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2022年第4期48卷 1336-1350页

作者： Sun, Baicai Gong, Dunwei Tian, Tian Yao, Xiangjuan China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Shandong Peoples R China Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250101 Shandong Peoples R China China Univ Min & Technol Sch Math Xuzhou 221116 Jiangsu Peoples R China

For the path coverage testing of a Message-Passing Interface (MPI) program, test data generation based on an evolutionary optimization algorithm (EOA) has been widely known. However, during the use of the above technique, it is necessary to evaluate the fitness of each evolutionary individual by executing the program, which is generally computationally expensive. In order to reduce the computational cost, this article proposes a method of integrating an ensemble surrogate model's estimation into the process of generating test data. The proposed method first produces a number of test inputs using an EOA, and forms a training set together with their real fitness. Then, this article trains an ensemble surrogate model (ESM) based on the training set, which is employed to estimate the fitness of each individual. Finally, a small number of individuals with good estimations are selected to further execute the program, so as to have their real fitness for the subsequent evolution. This article applies the proposed method to seven benchmark MPI programs, which is compared with several state-of-the-art approaches. The experimental results show that the proposed method can generate test data with significantly low computational cost.

关键词： Testing Estimation Data models optimization Computational efficiency Training Sun MPI program path coverage testing evolutionary optimization algorithm ensemble surrogate model test data generation

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