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Traffic accident severity prediction using a novel multi-objective genetic algorithm

INTERNATIONAL JOURNAL OF CRASHWORTHINESS 2017年第4期22卷 425-440页

作者： Hashmienejad, Seyed Hessam-Allah Hasheminejad, Seyed Mohammad Hossein Razi Univ Dept Civil Engn Kermanshah Iran Alzahra Univ Dept Comp Engn Tehran Iran

Prediction of traffic accident severity is a motor vehicle traffic challenge due to its impact on saving human lives. There are several researches in the literature to predict traffic accident severity based on artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs) and other classification methods. In fact, the main disadvantage of ANNs and SVMs is lack of interpretation for human and the main disadvantage of classical DTs such as C4.5, ID3 and CART is their low accuracy. To address these drawbacks, in this paper we propose a novel rule-based method to predict traffic accident severity according to user's preferences instead of conventional DTs. In the proposed method, we customised a multi-objective genetic algorithm, i.e. Non-Dominated Sorting genetic algorithm (NSGA-II), to optimise and identify rules according to Support, Confidence and Comprehensibility metrics. The goal of the proposed method is providing facilities to make use of the knowledge of users, including traffic police, roads and transportation engineers and trade-off among all the conflicting objectives. The proposed method is evaluated by a traffic accident data set including 14211 accidents in rural and urban roads in Tehran Province of Iran for a period of 5years (2008-2013). The evaluation results revealed that the proposed method outperforms the classification methods such as ANN, SVM, and conventional DTs according to classification metrics like accuracy (88.2%), and performance metrics of rules like support and confidence (0.79 and 0.74, respectively).

关键词： Traffic accident severity prediction multi-objective genetic algorithm decision trees Non-Dominated Sorting genetic algorithm

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Configuration optimization of shell-and-tube heat exchangers with helical baffles using multi-objective genetic algorithm based on fluid-structure interaction

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2017年 85卷 62-69页

作者： Wang, Simin Xiao, Juan Wang, Jiarui Jian, Guanping Wen, Jian Zhang, Zaoxiao Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Peoples R China

The configuration parameters of helical angle and overlapped degree of shell-and-tube heat exchangers with helical baffles have been discussed for the thermal-structural comprehensive performance. Based on fluid structure interaction theory, a method on configuration optimization of shell-and-tube heat exchangers with helical baffles is introduced using second-order polynomial regression response surface combined with multi objective genetic algorithm. The results show that the heat transfer coefficient per unit pressure drop of shell and -tube heat exchangers with helical baffles increases firstly and then decreases with the increase of helical angle, and decreases with the increase of overlapped degree under certain shell-inlet velocity. And the performance of flow and heat transfer is more sensitive to helical angle compared with overlapped degree. The maximum shear stress increases with helical angle, but it is almost unaffected by overlapped degree for mechanical properties of helical baffles. The objectives of optimization are the heat transfer coefficient per unit pressure drop maximizing and maximum shear stress minimizing with scope of allowable stress, and three optimal structures are obtained. The optimal results indicate that the heat transfer coefficient per unit pressure drop increases averagely by 14.1%, the maximum shear stress decreases averagely by 4.1%, which provides theoretical guidance for industrial design of shell-and-tube heat exchangers with helical baffles.

关键词： Shell-and-tube heat exchangers with helical baffles Fluid-structure interaction multi-objective genetic algorithm Configuration optimization Mechanical properties

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Dynamic configuration of QC allocating problem based on multi-objective genetic algorithm

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JOURNAL OF INTELLIGENT MANUFACTURING 2017年第3期28卷 847-855页

作者： Liang, ChengJi Li, MiaoMiao Lu, Bo Gu, Tianyi Jo, Jungbok Ding, Yi Shanghai Maritime Univ Logist Res Ctr Shanghai Peoples R China Dalian Univ Inst E Commerce & Modern Logist Dalian Peoples R China UCAS Sch Management Beijing Peoples R China Shanghai Int Port Grp Co Ltd Shanghai Peoples R China Dongseo Univ Div Comp & Informat Engn Busan South Korea

Solving the problem of allocating and scheduling quay cranes (QCs) is very important to ensure favorable port service. This work proposes a bi-criteria mixed integer programming model of the continual and dynamic arrival of several vessels at a port. A multi-objective genetic algorithm is applied to solve the problem in three cases. The results thus obtained confirm the feasibility and effectiveness of the model and GA. Additionally, the multi-objective solution considering both the total duration for which vessels stay in the port and QCs move is the best, as determined by comparing with considering only the total time for which vessels stay in the port or QCs move, as it considers, and it balances these two objectives.

关键词： Port facilities multi-objective genetic algorithm Quay crane scheduling Pareto optimal solution

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A Novel Linear Time Invariant Systems Order Reduction Approach Based on a Cooperative multi-objective genetic algorithm 8th

A Novel Linear Time Invariant Systems Order Reduction Approa...

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8th International Conference on Swarm Intelligence (ICSI)

作者： Ryzhikov, Ivan Brester, Christina Semenkin, Eugene Siberian State Aerosp Univ Krasnoyarsk Russia

ISBN: (纸本)9783319618333;9783319618326

Cooperative multi-objective optimization tool is proposed for solving the order reduction problem for linear time invariant systems. Normally, the adequacy of an order reduction problem solution is estimated using two different criteria, but only one of them identifies the model. In this study, it was suggested to identify the parameters using both of the criteria, and since the criteria are complex and multi-extremum there is a need for a powerful optimization algorithm to be used. The proposed approach is based on the cooperation of heterogeneous algorithms implemented in the islands scheme and it has proved its efficiency in solving various multi-objective optimization problems. It allows us to receive a set of lower order models, which are non-dominated solutions for the given criteria and an estimation of the Pareto set. The results of this study are compared to the results of solving the same problems using various approaches and heuristic optimization tools and it is demonstrated that the set of solutions not only outperforms these approaches by the main criterion, but also provides good solutions with another criterion and a combination of them using the same computational resources.

关键词： Cooperation meta-heuristics multi-objective optimization multi-objective genetic algorithm Linear time invariant system Parameters identification

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Parallel multi-objective genetic algorithm for Short-Term Economic Environmental Hydrothermal Scheduling

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ENERGIES 2017年第2期10卷 163页

作者： Feng, Zhong-Kai Niu, Wen-Jing Zhou, Jian-Zhong Cheng, Chun-Tian Qin, Hui Jiang, Zhi-Qiang Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Dalian Univ Technol Inst Hydropower Syst & Hydroinformat Dalian 116024 Peoples R China

With the increasingly serious energy crisis and environmental pollution, the short-term economic environmental hydrothermal scheduling (SEEHTS) problem is becoming more and more important in modern electrical power systems. In order to handle the SEEHTS problem efficiently, the parallel multi-objective genetic algorithm (PMOGA) is proposed in the paper. Based on the Fork/Join parallel framework, PMOGA divides the whole population of individuals into several subpopulations which will evolve in different cores simultaneously. In this way, PMOGA can avoid the wastage of computational resources and increase the population diversity. Moreover, the constraint handling technique is used to handle the complex constraints in SEEHTS, and a selection strategy based on constraint violation is also employed to ensure the convergence speed and solution feasibility. The results from a hydrothermal system in different cases indicate that PMOGA can make the utmost of system resources to significantly improve the computing efficiency and solution quality. Moreover, PMOGA has competitive performance in SEEHTS when compared with several other methods reported in the previous literature, providing a new approach for the operation of hydrothermal systems.

关键词： parallel computing economic environmental hydrothermal scheduling multi-objective optimization multi-objective genetic algorithm constraint handling method

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Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm

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JOURNAL OF INTELLIGENT MANUFACTURING 2016年第3期27卷 549-559页

作者： Pashazadeh, Hamed Gheisari, Yousof Hamedi, Mohsen Islamic Azad Univ East Azarbaijan Sci & Res Branch Young Researchers & Elites Club Tabriz Iran Islamic Azad Univ Bushehr Branch Dept Math Bushehr Iran Univ Tehran Sch Mech Engn Coll Engn Tehran Iran

Resistance spot welding (RSW) is a highly used joining procedure in automotive industry. In RSW, after a number of welds the welding electrode starts to wear and its diameter changes. This causes the weld nugget diameter abnormal variations and consequently reduces the weld strength. Therefore the tip of the electrode should be dressed in RSW. Selecting the optimum time for the welding electrode tip dressing operations is very important. In this research three welding parameters including the welding time, the welding current, and the welding pressure were identified as the main effective parameters on the weld nugget dimensions including the weld nugget diameter and height using full factorial design of experiments. Then using hybrid combination of the artificial neural networks and multi-objective genetic algorithm, the optimized values of the aforementioned parameters were specified. Finally experiments were fulfilled to estimate the admissible number of the weld spots which should be done before the electrode tip dressing operation.

关键词： Resistance spot welding Electrode tip dressing Design of experiments Artificial neural networks multi-objective genetic algorithm

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Finding Robust Adaptation Gene Regulatory Networks Using multi-objective genetic algorithm

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016年第3期13卷 571-577页

作者： Ren, Hai-Peng Huang, Xiao-Na Hao, Jia-Xuan Xian Univ Technol Dept Informat & Control Engn Xian 710048 Peoples R China Shaanxi Key Lab Complex Syst Control & Intelligen Xian Peoples R China HeXi Univ Sch Phys & Electromech Engn Zhangye 734000 Gansu Peoples R China

Robust adaptation plays a key role in gene regulatory networks, and it is thought to be an important attribute for the organic or cells to survive in fluctuating conditions. In this paper, a simplified three-node enzyme network is modeled by the Michaelis-Menten rate equations for all possible topologies, and a family of topologies and the corresponding parameter sets of the network with satisfactory adaptation are obtained using the multi-objective genetic algorithm. The proposed approach improves the computation efficiency significantly as compared to the time consuming exhaustive searching method. This approach provides a systemic way for searching the feasible topologies and the corresponding parameter sets to make the gene regulatory networks have robust adaptation. The proposed methodology, owing to its universality and simplicity, can be used to address more complex issues in biological networks.

关键词： Gene regulatory networks Michaelis-Menten rate equations robust adaptation multi-objective genetic algorithm Pareto ranking

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Application of camera calibrating model to space manipulator with multi-objective genetic algorithm

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Journal of Central South University 2016年第8期23卷 1937-1943页

作者：王中宇江文松王岩庆 Key Laboratory of Precision Opto-Mechatronics Technology of Ministry of Education (Beihang University) Beijing 100191 China Academy of Opto-Electronics Chinese Academy of Sciences Beijing 100094 China

The multi-objective genetic algorithm(MOGA) is proposed to calibrate the non-linear camera model of a space manipulator to improve its locational accuracy. This algorithm can optimize the camera model by dynamic balancing its model weight and multi-parametric distributions to the required accuracy. A novel measuring instrument of space manipulator is designed to orbital simulative motion and locational accuracy test. The camera system of space manipulator, calibrated by MOGA algorithm, is used to locational accuracy test in this measuring instrument. The experimental result shows that the absolute errors are [0.07, 1.75] mm for MOGA calibrating model, [2.88, 5.95] mm for MN method, and [1.19, 4.83] mm for LM method. Besides, the composite errors both of LM method and MN method are approximately seven times higher that of MOGA calibrating model. It is suggested that the MOGA calibrating model is superior both to LM method and MN method.

关键词： space manipulator camera calibration multi-objective genetic algorithm orbital simulation and measurement

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An improved multi-objective genetic algorithm for heterogeneous coverage RFID network planning

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第8期54卷 2227-2240页

作者： Tang, Lin Zheng, Li Cao, Hui Huang, Ningjian Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Gen Motors R&D Mfg Syst Res Lab Warren MI USA

Recent research has demonstrated the potential benefits of radio frequency identification (RFID) technology in the supply chain and production management via its item-level visibility. However, the RFID coverage performance is largely impacted by the surrounding environment and potential collisions between the RFID devices. Thus, through RFID network planning (RNP) to achieve the desired coverage within the budget becomes a key factor for success. In this study, we establish a novel and generic multi-objective RNP model by simultaneously optimising two conflicted objectives with satisfying the heterogeneous coverage requirements. Then, we design an improved multi-objective genetic algorithm (IMOGA) integrating a divide-and-conquer greedy heuristic algorithm to solve the model. We further construct a number of computational cases abstracted from an automobile mixed-model assembly line to illustrate how the proposed model and algorithm are applied in a real RNP application. The results show that the proposed IMOGA achieves highly competitive solutions compared with Pareto optimal solutions and the solutions given by four recently developed well-known multi-objective evolutionary and swarm-based optimisers (SPEA2, NSGA-II, MOPSO and (MOPSO)-O-2) in terms of solution quality and computational robustness.

关键词： heterogeneous coverage RFID network planning multi-objective genetic algorithm mixed-model assembly line

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Shape optimization of free-form buildings based on solar radiation gain and space efficiency using a multi-objective genetic algorithm in the severe cold zones of China

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SOLAR ENERGY 2016年 132卷 38-50页

作者： Zhang, Longwei Zhang, Lingling Wang, Yuetao Chinese Acad Sci Inst Appl Ecol Shenyang 110164 Peoples R China Shenyang Jianzhu Univ Sch Architecture & Urban Design Shenyang 110168 Liaoning Provin Peoples R China Shandong Jianzhu Univ Coll Architecture & Urban Planning Jinan 250101 Shandong Peoples R China

In the severe cold zones of China, solar radiation is one of the most important issues in architectural design. The design seeks to make buildings receive more direct sunlight within the limits of the user's comfort and simultaneously save energy and space. So far in China, the design of solar radiation has usually been qualitative, not quantitative, and it is often implemented by architects with experience or those following convention. This rough and rigid design approach is not accurate or efficient, particularly in the design of free-form buildings, which comprise a class of irregular-form buildings popular in current architectural design. Moreover, solar radiation is not the only thing that needs to be considered;shape coefficient and space efficiency should also be considered in free-form building design. This study proposes a method for a free-form building that receives more solar radiation though shape optimization and takes into account the other two objectives mentioned above. This paper provides a method with a "Modeling-simulation-Optimization" framework. In the process of applying this method, parametric modeling with Rhinoceros and Grasshopper is used to build up the free-form building model, and the shape optimization of the building is processed by using the multi-objective genetic algorithm to make sure the three objectives-i.e., to maximize solar radiation gain, to maximize space efficiency, and to minimize the shape coefficient-are all achieved. Finally, a Pareto frontier is generated to show the optimal solutions and to assist designers in making final decisions. The case study shows that compared with the cube-shaped reference building, the total solar radiation gain of the optimized free-form shape building is 30-53% higher, and the shape coefficient value is reduced by 15-20%, with a decrease of less than 5% of the space efficiency values. The proposed method, according to the basic process of architecture design, uses a performance-driven ap

关键词： Free-form building Solar radiation gain Shape optimization multi-objective genetic algorithm Severe cold zone

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