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improved genetic algorithms for optimal power flow under both normal and contingent operation states

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 1997年第5期19卷 287-292页

作者： Lai, LL Ma, JT Energy Systems Group Dept of Electrical Electronic and Information Engineering City University London EC1V 0HB UK Dept of Electrical Engineering Tokyo Metropolitan University 1-1 Minami Osawa Hachioji-shi Tokyo 192-03 Japan

This paper presents an improved genetic algorithm (IGA) to solve the problem of optimal power flow. The GA, with the dynamical hierarchy of the coding system developed in this paper, has the ability to code a large number of control variables in a practical system within a reasonable length string. It is, therefore, able to regulate the active power outputs of generators, bus voltages, shunt capacitors/ reactors and transformer tap-settings to minimize the fuel costs. Two cases in the IEEE 30-bus system for both normal and contingent operation states have been studied. In the contingent state, the circuit outage is simulated in one branch which causes a power flow violation in the other branch. The IGA always finds the best results and eliminates operational and insecure violations. (C) 1997 Elsevier Science Ltd.

关键词： optimal power flow economic dispatch artificial intelligence improved genetic algorithms

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improved genetic algorithms for optimization of inventory allocation in LED chip manufacturing plants

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JOURNAL OF INTERDISCIPLINARY MATHEMATICS 2017年第3期20卷 727-738页

作者： Wu, Horng-Huei Jiang, Xiao-Yun Chung Hua Univ Dept Business Adm Hsinchu 300 Taiwan Xiamen Univ Technol Dept Informat Management Xiamen 361000 Peoples R China

Light-emitting diode (LED) chip manufacturing (LED-CM) is an essential part of the LED supply chain. The specifications of LED chips ordered usually involve multiple feasible bins. A bin is determined by a definitive level of lightness and wavelength. Efficient allocation of inventory bins to orders increases throughput and the number of fulfilled orders while decreasing the inventory. As such, LED-CM plants face the task of allocating the inventory of feasible bins to various orders. Therefore, improved genetic algorithms for optimizing the allocation of inventory bins are proposed in this paper. This approach can be used to reduce the complexity of this task and obtain an effective solution. An illustrative LED-CM example is provided to demonstrate and evaluate the application the proposed approach and its effectiveness.

关键词： LED-CM plant Allocation of Bin inventory Optimization improved genetic algorithms

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The improved genetic algorithms for Multiple Maximum Scatter Traveling Salesperson Problems 11th

The Improved Genetic Algorithms for Multiple Maximum Scatter...

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11th China Wireless Sensor Network Conference (CWSN)

作者： Dong, Wenyong Dong, Xueshi Wang, Yufeng Wuhan Univ Comp Sch Wuhan Hubei Peoples R China

ISBN: (纸本)9789811081231;9789811081224

Maximum scatter traveling salesperson problem (MSTSP), as a variant of traveling salesman problem (TSP), has been successfully applied to the practical cases in manufacturing and medical imaging. However, it cannot model the application problems where there are multiple objectives or individuals. This paper proposes a new model named multiple maximum scatter traveling salesperson problems (MMSTSP) for modeling such problems. The paper applies three improved genetic algorithms (GAs) to solve MMSTSP, where three methods are used to improve GA, the first one is greedy initialization for optimization, the second one is climbing-hill algorithm, and the last one is simulated annealing algorithm. Furthermore, many real-world problems can be modeled by MMSTSP, and the scale of constructed model is usually up to large scale, it is necessary to study large scale MMSTSP problem. Therefore, the paper uses the improved GAs to solve the small scale to large scale MMSTSP. By extensive experiments and analysis, it shows that the improved algorithms are effective, and can demonstrate different characteristics in solving the problem.

关键词： improved genetic algorithms Maximum scatter traveling salesperson problem Greedy algorithm Hill-climbing Simulated annealing

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Picking Route Optimization of Automated Warehouse Based on improved genetic algorithms

Picking Route Optimization of Automated Warehouse Based on I...

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2nd International Conference on Information Technology and Management Innovation (ICITMI 2013)

作者： Wang, Peiguang Qi, Xingmin Zong, Xiaoping Zhu, Lingling Hebei Univ Coll Elect & Informat Engn Hebei Baoding Peoples R China

ISBN: (纸本)9783037858646

In order to improve the efficiency of automated warehouse, the order-picking task of the fixed shelve was researched and analysed. The picking mathematical model of automated warehouse was established and attributed to the classical traveling salesman problem (TSP) model. At the same time, using an improved genetic algorithms(improved GAs) solved the optimization problem. Firstly, the initial population of the algorithm was optimized, and then a 'reverse evolution operator' was introduced in the improved genetic algorithms because of the lack of local optimization ability of genetic algorithm. Results of experiment verify that the method can acquire satisfying the demands of the route picking and optimization of speed.

关键词： Automated warehouse Stacker Picking Route Optimization improved genetic algorithms

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Digital Microfluidic Droplet Path Planning Based on improved genetic Algorithm

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INFORMATION 2025年第2期16卷

作者： Luo, Zhijie Long, Wufa Chen, Rui Wu, Jianhao Huang, Aiqing Zheng, Jianhua Zhongkai Univ Agr & Engn Coll Informat Sci & Technol Guangzhou 510225 Peoples R China Zhongkai Univ Agr & Engn Smart Agr Engn Technol Res Ctr Guangzhou 510225 Peoples R China Zhongkai Univ Agr & Engn Guangzhou Key Lab Agr Prod Qual & Safety Traceabil Guangzhou 510225 Peoples R China

In practical applications of droplet actuation using digital microfluidic (DMF) systems based on electrowetting-on-dielectric (EWOD), various electrode failures can still arise due to diverse operational conditions. To improve droplet transport efficiency, this study proposes a heuristic-elite genetic algorithm (HEGA) for droplet path planning. We introduce a heuristic method and a bidirectional elite fragment recombination method to address the challenge of poor initialization quality in genetic algorithms, particularly in complex environments. These approaches aim to enhance the global search capability and accelerate the algorithm's convergence. Simulations were performed using MATLAB, and the results indicate that compared to the basic ant colony algorithm, the proposed method reduces the average number of turning points by approximately 17.23% and the average search time by about 92.60%. In multi-droplet transport applications, the algorithm generates optimal paths for test droplets while maintaining fast convergence. Additionally, it effectively prevents droplets from accidentally contacting or merging in non-synthesis areas, ensuring improved testing outcomes for the chip.

关键词： digital microfluidic system heuristic-elite improved genetic algorithms path planning droplets collision avoidance strategy chip testing

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Research on vehicle scheduling for forest fires in the northern Greater Khingan Mountains

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SCIENTIFIC REPORTS 2025年第1期15卷 1-15页

作者： Zhang, Jie He, Junnan Ren, Shihao Zhou, Pei Guo, Jun Song, Mingyue Inner Mongolia Agr Univ Coll Energy & Transportat Engn Hohhot 010010 Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430070 Peoples R China Inner Mongolia Agr Univ Coll Forestry Hohhot 010010 Peoples R China

In the face of forest fire emergencies, fast and efficient dispatching of rescue vehicles is an important means of mitigating the damage caused by forest fires, and is an effective method of avoiding secondary damage caused by forest fires, minimizing the damage caused by forest fires to the ecosystem, and mitigating the losses caused by economic development. this paper takes the actual problem as the starting point, constructs a reasonable mathematical model of the problem, for the special characteristics of the emergency rescue vehicle scheduling problem of forest fires, taking into account the actual road conditions in the northern pristine forest area, through the analysis of the cost of paths between the forest area and the highway, to obtain the least obstructed rescue paths, to narrow the gap between the theoretical model and the problem of the actual. Improvement of ordinary genetic algorithm, design of double population strategy selection operation, the introduction of chaotic search initialization population, to improve the algorithm's solution efficiency and accuracy, through the northern pristine forest area of Daxing'anling real forest fire cases and generation of large-scale random fire point simulation experimental test to verify the effectiveness of the algorithm, to ensure that the effectiveness and reasonableness of the solution to the problem of forest fire emergency rescue vehicle scheduling program. It enriches the solution method of forest fire emergency rescue vehicle dispatching problem in Great Khingan area, which is of great significance to improve the emergency rescue capability in case of sudden forest fire. Through simulation experiments, the proposed improved genetic Algorithm (IGA) achieved an average rescue time reduction of 8.5% compared to conventional genetic Algorithm (GA) and 3.5% compared to improved Artificial Bee Colony (IABC) algorithm, with an average solution time of 9.4 ms.

关键词： Emergency relief improved genetic algorithms Vehicle scheduling Chaos Search

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Constructing small sample datasets with game mixed sampling and improved genetic algorithm

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JOURNAL OF SUPERCOMPUTING 2024年第14期80卷 20891-20922页

作者： Zhu, Bailin Wang, Hongliang Fan, Mi Liaoning Petrochem Univ Sch Artificial Intelligence & Software 1West Sect Dandong Rd Fushun 113001 Liaoning Peoples R China Liaoning Petrochem Univ Sch Foreign Languages 1West Sect Dandong Rd Fushun 113001 Liaoning Peoples R China

The issue of categorizing imbalanced data is becoming increasingly prevalent. While existing methodologies have demonstrated notable advancements in handling imbalanced data, the challenges of extensive data size and low-quality data in the dataset persist. We propose an innovative hybrid approach that combines game mixed sampling and improved genetic algorithms to address the issues of excessive dataset size and low data quality in imbalanced data classification problems. Specifically, in the game hybrid sampling module, we identify the optimal hybrid sampling method and sampling ratio for the current dataset through the game idea, intending to obtain a diverse dataset to ensure comprehensive coverage of the dataset. Additionally, in the module on improving genetic algorithms, we integrate the classifier group, encode the performance metrics of the sampled data, and perform a comprehensive evaluation of the fitness of each data point. Preserve population data of many excellent individuals through selection operations. The population data is subjected to crossover and mutation operations to explore the search space, and the minimum stable population size is determined by sliding standard deviation. In the real world, where credit card fraud data are highly imbalanced, our combined approach achieves small dataset sizes and high evaluation indices, outperforms existing methods, and demonstrates the effectiveness of game mixed sampling and improved genetic algorithms.

关键词： Imbalanced dataset Game mixed sampling improved genetic algorithms Performance metric coding Small dataset size High evaluation index

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Optimal Sensors Placement for Health Monitoring System of Long-span Steel Truss Railway Cable-stayed Bridge 5

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5th International Symposium on Environmental Vibration (ISEV2011)

作者： Shan, Deshan Wan, Zhenhua Li, Qiao Southwest Jiaotong Univ Sch Civil Engn Bridge Engn Dept Chengdu 610031 Peoples R China

ISBN: (纸本)9787030323736

The optimal placement method of sensorsis carried out based on improved adaptive genetic algorithm for solving the optimal placementproblem of sensorsin the health monitoring systemof long-span railway bridge. Dual structure coding method is introduced to improve the individual encoding method in the genetic algorithm. Adaptive partial matching crossover andinversus mutation methodsareadopted in the optimal preservation strategy, andthe crossover probability and mutation probability are changed automatically according to the fitness value for obtaining the global optimal solution of the sensor placement. Therefore, some defects in other genetic algorithm applied in the optimal placement of sensors for major bridge structure, such as slow convergence speed and easily falling into local optimum etc., are overcome, and the convergence is speed up. Then the optimal sensors placement of the health monitoring system for one certain long-span railway steel truss cable-stayed bridge is taken as the example to verify the proposed improved genetic algorithm. The result shows that the proposed method has better global optimization, computational efficiency and reliability in compare with the Simple genetic Algorithm and General genetic Algorithm, and can be applied to the actual railway cable-stayed bridge health monitoring system for the sensors optimal placement.

关键词： steel truss girder railway cable-stayed bridges health monitoring optimal placement sensors improved genetic algorithms

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Optimal placement of FBG sensors for reconstruction of flexible plate structures using modal approach 34

Optimal placement of FBG sensors for reconstruction of flexi...

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34th Chinese Control Conference, CCC 2015

作者： Lu, Geng Xiaojin, Zhu Hesheng, Zhang Zhiyuan, Gao Kaining, Liu School of Mechatronics Engineering and Automation Shanghai University Shanghai China Shanghai Institute of Aerospace Electronic Technology Shanghai China

ISBN: (纸本)9789881563897

An optimization method based on mode approach and improved genetic algorithms is developed for optimal FBG sensors placement in health monitoring and reconstruction of flexible thin-plate structure. The optimization objective is to obtain a kind of placement of FBG sensors which has the best reconstruction effects. The structure is modeled using Finite Element methods and modal analysis is carried out to create a modal space which provides data for both shape-reconstruction and error-calculation. The optimization is performed by improved genetic algorithm that uses the mean square error of reconstruction for the comparison of different optimal solution candidates. FBG sensors were arranged on the structural surface and experiment with an optimized sensor placement were performed to verify the effectiveness of the method and the simulation results. © 2015 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： FBG sensors flexible plate structures improved genetic algorithms Modal approach shape reconstruction

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Dynamic characteristics analysis and optimization for lateral plates of the vibration screen

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JOURNAL OF VIBROENGINEERING 2015年第4期17卷 1593-1604页

作者： Zhou, Ning Hebei Univ Key Lab Digital Med Engn Hebei Prov Coll Elect & Informat Engn Baoding 071002 Peoples R China

To solve the problem of damage due to large dynamic stress of the lateral plates during working process of the vibration screen, it is necessary to calculate and analyze natural modes and distribution of dynamic stress of lateral plates, which is shown in results that the lateral plate structure shall be optimized. In this paper, with the total weight of the lateral plates for the banana-shaped vibration screen as the optimization objective, frequency constraints as the status variables, optimization for multi-frequency constraints is conducted based on the improved genetic algorithm. Next, a mathematical model of structure parameter optimization for the lateral plates of the vibration screen under frequency constraints is established to carry out optimization design in order to obtain a structure with smaller dynamic stress and lower weight. Sensitivity analysis is added into the improved genetic algorithm, and the optimization efficiency is increased simultaneously. The structure frequency is optimized by means of the improved genetic algorithm. Then, a modal experiment is carried out to the entire vibration screen so as to verify reliability of the finite element model, and the natural characteristics of the vibration screen before and after optimization are analyzed, and the top 6 orders natural frequency and vibration modes of the entire vibration screen are calculated, so as to indicate that optimized vibration screen is improved in terms of material saving, stiffness and stability. In addition, noise is directly related to vibration. As a result, the change of the vibration screen should be also analyzed. Noise of the vibration screen is also tested by sound array technology. Results showed that the radiated noise is reduced after optimization, and optimization in this paper is feasible.

关键词： dynamic stress lateral plates frequency constraints experiment design improved genetic algorithms sensitivity analysis

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