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genetic algorithm in flexible work shop scheduling based on multi-objective optimization

JOURNAL OF INTERDISCIPLINARY MATHEMATICS 2018年第5期21卷 1249-1254页

作者： Wang, Yahui Fu, Liuqiang Su, Yongqiang Yang, Qian Wu, Linfeng North China Univ Water Resources & Elect Power Sch Mech Engn Zhengzhou 450045 Henan Peoples R China

Aiming at the problem that the objective function design is not reasonable in the current flexible work shop scheduling, and the error of constraint solving process is large, the application of genetic algorithm in flexible work shop scheduling multi-objective optimization was proposed in this paper. Firstly, the mechanical work shop scheduling model was built. in the model, the objective function was based on the conditions of minimum total cost of work, the shortest time of workpiece circulation in the system and the minimum penalty for the completion of the work in advance, and the constraint conditions were composed of sequential constraints, resource constraints, cost constraints and other constraints. By using the priority matrix coding method, the chromosome was encoded and the initial solution was generated, the flexible work shop scheduling function was calculated, and the optimal scheduling solution of the objective function in the model was achieved. Experiments show that the algorithm can solve the problems in flexible work shop scheduling effectively, reduce production costs and resource consumption, and improve production efficiency and reliability.

关键词： Multi-objective optimized genetic algorithm Flexible work shop Scheduling

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Mobility based genetic algorithm Hierarchical Routing Protocol in Mobile Wireless Sensor Networks

Mobility based Genetic Algorithm Hierarchical Routing Protoc...

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6th International Japan-Africa Conference on Electronics, Communications and Computations (JAC-ECC)

作者： Rady, Asmaa Sabor, Nabil Shokair, Mona El-Rabaie, El-Sayed M. Menoufia Univ Dept Elect & Elect Engn Fac Elect Engn Meouf Egypt Assiut Univ Dept Elect & Elect Engn Fac Engn Assiut 71516 Egypt

ISBN: (纸本)9781538692301

Mobile Wireless Sensor Networks (MWSN) are the overgrowth and emerging technology. Routing process in MWSN is more complicated than static one. Therefore, many routing protocols have been implemented recently for MWSN to accomplish progress in energy consumption field. This paper presents a Mobility based genetic algorithm Hierarchical routing Protocol (MGAHP) to achieve maximum lifetime of the network and improve the stable period of MWSN. The basic idea of the proposed MGAHP protocol is using genetic algorithm (GA) to find the optimum number of Cluster Heads (CHs) and their locations depending on minimizing the energy consumption of the sensor nodes. Simulation results exhibited that the proposed MGAHP protocol gives better improvement in energy efficient than LEACH-M, CBR-Mobile, and MACRO protocols.

关键词： Mobile Wireless Sensor Networks optimized genetic algorithm Mobility and Energy Efficiency

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A Parameter Optimization Method of ADRC by Adaptive Multi-population genetic algorithm

A Parameter Optimization Method of ADRC by Adaptive Multi-po...

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Chinese Automation Congress (CAC)

作者： Yu, Shimin Wang, Jianjian Zhang, Qingyong Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781728113128

In order to solve the problem that the parameters of the auto disturbance rejection controller are hard to be set, this article has presented an improved adaptive multi-population genetic algorithm for parameter tuning of ADRC. Multiple population co-evolution was used to replace the traditional single population evolution, and an adaptive parameter model is applied to the multi-population genetic algorithm. Considering the dynamic performance and control requirements of the controller, an evaluation function is established. The course control of unmanned ship is simulated as an example. The simulation results show that the proposed optimization method in this article can improve the response speed and control accuracy of the ADRC obviously, and it has certain effectiveness and practicability.

关键词： ADRC optimized genetic algorithm Parameter tuning Adaptive adjustment

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Optimization of SAGD and solvent additive SAGD applications: Comparative analysis of optimization techniques with improved algorithm configuration

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2012年 98-99卷 61-68页

作者： Al-Gosayir, Mohammad Babadagli, Tayfun Leung, Juliana Univ Alberta Edmonton AB T6G 2W2 Canada

Heavy oil and bitumen recovery cost are excessive mainly due to high energy requirement to generate heat and its environmental impacts. Steam Assisted Gravity Drainage (SAGD) is an example of this case. The determination of optimal operating conditions, such as injection rates and well locations, based on reservoir and fluid characteristics is essential in the design of field applications. Many Steam Assisted Gravity Drainage (SAGD) optimization studies published in the literature combined numerical simulation with graphical or analytical techniques for design and performance evaluation. There have been limited efforts that integrated the simulation exercise with global optimization algorithms. Some studies focused on optimization of cumulative steam-to-oil ratio (cSOR) in SAGD by altering steam injection rates, while others focused on optimization of cumulative net energy-to-oil ratio (cEOR) in solvent-additive SAGD by altering injection pressures and fraction of solvent in the injection stream. Typical scoring functions were the net present value per hectare of land (NPV/ha) by controlling steam and solvent rates. Several studies also considered total project net present value calculation by changing total project area, capital cost intensities, solvent prices, discount rate, and risk factors to determine the well spacing and drilling schedule. Optimization techniques commonly used in those studies were scattered search, simulated annealing, and genetic algorithm (GA). In continuation of these efforts, we focused on optimizing the SAGD process and its extension to solvent-additive SAGD and several optimization techniques including simulated annealing and genetic algorithm were tested and compared. Additional procedures were incorporated to improve the implementation configuration and initial population or seed. The objective function was defined to obtain the lowest cumulative steam-oil ratio (cSOR) and highest recovery factor. It was used later as a scoring funct

关键词： optimization of heavy-oil recovery SAGD solvent injection genetic algorithm simulated annealing optimized genetic algorithm

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A general inverse control model of a magneto-rheological damper based on neural network

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JOURNAL OF VIBRATION AND CONTROL 2022年第7-8期28卷 952-963页

作者： Yan, Yaya Dong, Longlei Han, Yi Li, Weishuo Xi An Jiao Tong Univ 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

Because of the nonlinear hysteresis characteristics of the magneto-rheological damper, the damper's inverse model has disadvantages of low fitting accuracy and poor practicality. Therefore, in this study, an optimized genetic algorithm has been proposed to optimize the back propagation neural network's initial weights and threshold. Compared with other damper controllers, the proposed inverse model improves the control current's prediction accuracy and tracks the desired damping force in real time. Moreover, the proposed inverse model and designed fuzzy controller are applied to the 1/4 vehicle suspension system simulation. The obtained results show that the optimized neural network model can be applied to a practical control. The root mean square value of body acceleration of semi-active suspension is lower than that of passive suspension under different road excitation. This method provides a foundation for the accurate modeling and semi-active control of the magneto-rheological damper.

关键词： Magneto-rheological damper back propagation algorithm optimized genetic algorithm inverse model semi-active control

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Machine Learning Perspective Gene Optimization for Efficient Induction Machine Design

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2018年第3期13卷 1202-1211页

作者： Selvam, Ponmurugan Panneer Narayanan, Rengarajan Sri Krishna Coll Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India Nandha Engn Coll Dept Elect & Elect Engn Erode Tamil Nadu India

In this paper, induction machine operation efficiency and torque is improved using Machine Learning based Gene Optimization (ML-GO) Technique is introduced. optimized genetic algorithm (OGA) is used to select the optimal induction machine data. In OGA, selection, crossover and mutation process is carried out to find the optimal electrical machine data for induction machine design. Initially, many number of induction machine data are given as input for OGA. Then, fitness value is calculated for all induction machine data to find whether the criterion is satisfied or not through fitness function (i.e., objective function such as starting to full load torque ratio, rotor current, power factor and maximum flux density of stator and rotor teeth). When the criterion is not satisfied, annealed selection approach in OGA is used to move the selection criteria from exploration to exploitation to attain the optimal solution (i.e., efficient machine data). After the selection process, two point crossovers is carried out to select two crossover points within a chromosomes (i.e., design variables) and then swaps two parent's chromosomes for producing two new offspring. Finally, Adaptive Levy Mutation is used in OGA to select any value in random manner and gets mutated to obtain the optimal value. This process gets iterated till finding the optimal value for induction machine design. Experimental evaluation of ML-GO technique is carried out with performance metrics such as torque, rotor current, induction machine operation efficiency and rotor power factor compared to the state-of-the-art works.

关键词： Induction motor annealed selection Adaptive Levy Mutation two point crossovers fitness function optimized genetic algorithm

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A Parameter Optimization Method of ADRC by Adaptive Multi-population genetic algorithm

A Parameter Optimization Method of ADRC by Adaptive Multi-po...

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Chinese Automation Congress

作者： Shimin Yu Jianjian Wang Qingyong Zhang School of Automation Wuhan University of Technology Wuhan China

关键词： ADRC optimized genetic algorithm Parameter tuning Adaptive adjustment

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