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Well placement optimization for offshore oilfield based on Theil index and differential evolution algorithm

Journal of Petroleum Exploration and Production Technology 2018年第4期8卷 1225-1233页

作者： Chen, Hongwei Feng, Qihong Zhang, Xianmin Wang, Sen Zhou, Wensheng Liu, Chen School of Petroleum Engineering China University of Petroleum (East China) Qingdao 266580 Shandong China CNOOC Research Institute Beijing 100027 China

Proper well placement should match the heterogeneity of reservoir and development schedule, in which case the displacement is balanced. A balanced displacement process can achieve a good development performance of reservoir. Present parameters to evaluate the displacement balanced degree are not fit for high water-cut oilfields and need a specific reservoir simulator. In this work, a well placement optimization method for offshore oilfield is established. A displacement balanced degree evaluation method is proposed based on Theil index. By using minimum Theil index as the objective function, well placement optimization model for offshore oilfield is built. In this model, constraints about the available infilling scope and minimum inter-well distance are considered to find the realizable optimal location. This optimization model is solved by a derivative-free optimization algorithm, differential evolution algorithm. This method is applied and validated in a semisynthetic offshore oilfield to find optimal well locations. Results demonstrate that this proposed method can find the optimal well placement for offshore oilfield to achieve a more balanced displacement and a higher oil recovery. © 2017, The Author(s).

关键词： differential evolution algorithm Offshore oilfield Optimization model Theil index Well placement

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A Hybrid Genetic - differential evolution algorithm (HybGADE) for a Constrained Sequencing Problem

A Hybrid Genetic - Differential Evolution Algorithm (HybGADE...

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International Conference on Artificial Intelligence and Data Processing (IDAP)

作者： Abidin, Didem Manisa Celal Bayar Univ Comp Engn Dept Manisa Turkey

ISBN: (纸本)9781538668788

For researchers, evolutionary algorithms are mostly preferable because of their effectiveness in finding the optimum solutions to many problems. Among these problems, sequencing is one of the most popular. In daily life, it is a must to find the best solution to a sequencing problem in order to save time, money and labour. Education is also one of the application areas of optimization where sequencing matters. In this paper, a hybrid genetic - differential algorithm is introduced, which finds better solutions to sequencing problem in education. The correct order of educational data is crucial because it directly affects the students' performance. In this study, educational material of Database course in Ege University Tire Kutsan Vocational School Computer Programming Department is used as the data set with two different evolutionary algorithms (EA). In these data sets, there are some constraints which should be considered while sequencing. We called them "prerequisites" that tells us the rules about the order of the modules of a course. That is why, the study can be considered as an application of Precedence-Constrained Sequencing. The sequencing performances of pure genetic algorithm (GA) and hybridized differential evolution (DE) with genetic algorithms (HybGADE) are compared with a computer program implemented. It is observed that, HybGADE can be used with 99.54% of reliability where pure GA has an effectiveness of 98.53% The percentage of the students passing the class has been observed for four years. The ratio of students passing the class has increased from 39% to 65%, which can be considered as a remarkable increase.

关键词： differential evolution algorithm Hybrid algorithms Genetic algorithm Precedence-Constrained Sequencing

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Optimization Of Raw Material Inventory Costs In The Food Supply Chain Using differential evolution algorithm 3

Optimization Of Raw Material Inventory Costs In The Food Sup...

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3rd International Conference on Energy, Environmental and Information System (ICENIS) - Strengthening Planning and Implementation Energy, Environment, and Information System Toward Low Carbon Society

作者： Priymasiwi, Mara Huriga Mustafid Diponegoro Univ Magister Program Informat Syst Sch Postgrad Studies Semarang Indonesia Diponegoro Univ Fac Sci & Math Dept Math Semarang Indonesia

The management of raw material inventory is used to overcome the problems occuring especially in the food industry to achieve effectiveness, timeliness, and high service levels which are contrary to the problem of effectiveness and cost efficiency. The inventory control system is built to achieve the optimization of raw material inventory cost in the supply chain in food industry. This research represents differential evolution (DE) algorithm as optimization method by minimizing total inventory based on amount of raw material requirement, purchasing cost, saefty stock and reorder time. With the population size, the parameters of mutation control, crossover parameters and the number of iterations respectively 80, 0.8, 0.5, 200. With the amount of safety stock at the company 7213.95 obtained a total inventory cost decrease of 39.95%. Result indicate that the use of DE algorithm help providein efficient amount, time and cost.

关键词： differential evolution algorithm inventory optimization raw materials optimization food supply chain

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Modified adaptive differential evolution algorithm for test scheduling of multi-core SOC based on DVS and MVI 8

Modified adaptive differential evolution algorithm for test ...

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8th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Deng Libao Zhao Yingjie Wang Sha Harbin Inst Technol Weihai Sch Informat & Elect Engn Weihai 264209 Peoples R China Harbin Inst Technol Dept Automat Test & Control Harbin 150080 Peoples R China

ISBN: (纸本)9781538682463

As a popular kind of system chip, multi-core SOC based on DVS and MVI has the advantages of low power consumption and high performance. However, the test technology is still in the exploration stage, being the first problem restricting the development of multi-core SOC. In this paper, a new modified differential evolution algorithm (JADE-MaS) is applied to system level test optimization. JADE-MaS proposes a multiangle searching strategy and obtains good effect on enhancing population diversity. The mathematical model of TAM resource division and test scheduling include four decision variables: the test bus width, the location of IP cores, the start and end times of the test. We encode the first two variables as population individuals and use JADE-MaS to seek the optimal individual, then use a priority scheduling mechanism based on heuristic algorithm to distribute the test tasks to evaluate the individual fitness. The remaining variables can also be solved in this process. After being decoded, the individual with the shortest test time is the solution of system-level test scheduling problem. Series of experiments on ITC'02 SOC benchmarks show that JADE-MaS has searched the better scheme comparing with the GA and the PSO algorithm and effectively shortens the system-level test time.

关键词： multi-core SOC test scheduling differential evolution algorithm test time optimization

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Prediction of Particulate Matter Concentration in Chengdu Based on Improved differential evolution algorithm and BP Neural Network Model 3

Prediction of Particulate Matter Concentration in Chengdu Ba...

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3rd IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Teng, Yinyin Huang, Xiaoli Ye, Shuce Li, Yuxia Xihua Univ Sch Elect Engn & Elect Informat Chengdu Sichuan Peoples R China Fribourg Univ Dept Phys Fribourg Switzerland

ISBN: (纸本)9781538643013

Based on the establishment of improved BP neural network model, this paper has carried on the research to the PM by collecting the particle concentration and the related meteorological data for prediction. In this paper, the improved difference evolution algorithm is used to optimize the weights and thresholds of the BP neural network model (IDE-BPNN) for the traditional BP neural network model, which is too dependent on the initial value, the convergence rate is slow and easy to fall into the local minimum. The improved model is compared with the traditional BP neural network model and the other five optimized BP neural network models. The root mean square error (RMSE), the mean absolute error (MAE) and the mean absolute percentage error (MAPE) are smaller than those of other models, and the index of agreement (IA) of IDE-BPNN is the highest, the mean bias error (MBE) of IDE-BPNN tends to be zero, and the IDE-BPNN model performs better.

关键词： BP neural network particle concentration meteorological data differential evolution algorithm

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An Improved differential evolution algorithm with Local Search for Capacitated Vehicle Routing Problem 10

An Improved Differential Evolution Algorithm with Local Sear...

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10th International Conference on Advanced Computational Intelligence (ICACI)

作者： Song, Lulu Dong, Yun Northeastern Univ Inst Ind & Syst Engn Shenyang Liaoning Peoples R China

ISBN: (纸本)9781538643624

This paper proposes an improved differential evolution (DE) algorithm with local search to solve the capacitated vehicle routing problem (CVRP). CVRP is a kind of typical vehicle routing problem with capacity constraint of the vehicle. The proposed DE employs three different mutation operations at three iteration phases, respectively, which are try to avoid premature, accelerate the convergence, and enhance the robustness of algorithm simultaneously. While local search can help to improve the solution quality. The proposed algorithm and several similar type algorithms are compared based on CVRP benchmark tests. Experimental results show that the proposed algorithm achieves obvious advantages relative to the competitors.

关键词： capacitated vehicle routing problem CVRP differential evolution algorithm local search

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Control strategy based on differential evolution algorithm for planar second-order nonholonomic manipulator 37

Control strategy based on differential evolution algorithm f...

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37th Chinese Control Conference (CCC)

作者： Wang, Yawu Lai, Xuzhi Zhang, Pan China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9789881563958

This paper presents a two-stage position control strategy based on the differential evolution (DE) algorithm for a planar second-order nonholonomic manipulator, which has one passive joint and this passive joint is not the first joint (planar A(m)PA(n) manipulator for short, where m >= 1, n >= 0). An offline DE algorithm is used to calculate all link target angles corresponding to the target position. According to these target angles, a Lyapunov function is constructed to design the controllers A for the control stage 1, which are used to control all active links to their target angles. According to the constraint equation of the planar A(m)PA(n) manipulator, an oscillatory trajectory is planned for the first active link based on the online DE algorithm. When the first active link tracks the oscillatory trajectory, it will back to its target angle eventually. Meantime, the passive link will be jointly controlled to its target angle eventually. Then, the other Lyapunov function is constructed to design the controllers B for the control stage 2, which are used to control the first active link tracks the target trajectory and control the remaining m + n - 1 active links maintain in their target angles. Simulation results of a planar AAPA manipulator demonstrate the effectiveness of the proposed control strategy.

关键词： Planar second-order nonholonomic manipulator differential evolution algorithm Lyapunov function Online intelligent trajectory planning

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Dynamic Selection of Parameters in differential evolution algorithm 8

Dynamic Selection of Parameters in Differential Evolution Al...

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8th IEEE International Conference on Cloud Computing, Data Science and Engineering (Confluence) / Global Technology, Innovation and Enterpreneurship Summit

作者： Singh, Avjeet Kumar, Anoj Motilal Nehru Natl Inst Technol Allahabad Dept Comp Sci & Engn Allahabad 211004 Uttar Pradesh India

ISBN: (纸本)9781538617199

The differential evolution (DE) algorithm is one of the most effective nature-inspired algorithm that is utilized to solve the complex problems. The computation cost in a DE algorithms increases as the function evaluation is computed for the higher number of generations. In this paper, a dynamic method for selection of parameters has been introduced for basic differential evolution algorithm. The proposed method enhances the convergence speed and performance of the basic differential evolution algorithm. The proposed method is compared with other DE variants such as DE-F, JADEb, DE, DEAE, DEb, DE-PAL, CMAES, MVDE. The results show the effectiveness of proposed method over other methods.

关键词： differential evolution algorithm Endocrine COCO Platform

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Optimization of Flow Shop Scheduling Control Strategy Based on Improved differential evolution algorithm 33

Optimization of Flow Shop Scheduling Control Strategy Based ...

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33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Cheng, Ying Wang, Hongxin Weng, Zhiyuan Fang, Jie Weng, Zhigang West Anhui Univ Sch Mech & Vehicle Engn Lu An 237012 Peoples R China West Anhui Univ Intelligent Lighting & Display Technol Engn Ctr Sch Elect & Photoelect Engn Lu An 237012 Peoples R China

ISBN: (纸本)9781538672556

The scheduling problem of flow shop involves the benefit of production enterprise. In order to solve the existing problems in the flow shop scheduling problem, a modified job shop scheduling problem optimization algorithm based on differential evolution algorithm is proposed. First of all, the mathematical model of flow shop scheduling problem is established, and the shortest processing time is taken as the evaluation index. Then the mathematical model is solved and differential evolution algorithm is adopted. The differential evolution algorithm has been improved accordingly to speed up the solution of the mathematical model. Finally, the performance of the algorithm is tested and analyzed. The result shows that the algorithm can get better flow shop scheduling problem. Compared with other algorithms, the improved differential evolution algorithm has better evaluation index, and has higher practical application value.

关键词： flow shop scheduling mathematical model differential evolution algorithm simulation test

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An Image Enhancement Optimization Method Based on differential evolution algorithm and Cuckoo Search Through Serial Coupled Mode

An Image Enhancement Optimization Method Based on Differenti...

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IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems

作者： Zhiwei Ye Ye Cao Aixin Zhang Can Jin Lie Ma Xiang Hu Jiwei Hu Hubei University of Technology Wuhan Jizhi Technology co. LTD Wuhan Fiberhome Technical Services Co. Ltd.

ISBN: (纸本)9781728140704

Image enhancement based on Beta function is a widely used method for it is able to fit multiple transformation curves, which is a significant step for image analysis. The key step for the method is to find the appropriate parameters to determine the grayscale transformation function. However, it needs a lot of time to seek applicable parameters when enumeration is used and random optimization algorithms often have failures within a limited time and are prone to fall into the local optimum. In order to solve the problems a serial coupled mode of stochastic optimization algorithms is investigated in the paper. According to the model, the differential evolution algorithm and cuckoo search algorithm are tried in image enhancement through serial coupling mode and compared with the traditional optimization algorithm. The experimental results reveals that the proposed approach is feasible and the performance is more balanced, which has a good performance on the image enhancement.

关键词： Image enhancement Hybrid algorithm differential evolution algorithm Cuckoo search algorithm image enhancement differential evolution algorithms Optimization algorithms SEARCHING algorithm evolutionary algorithm Cuculidae Serial Publications BETA FUNCTIONS local optimum

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