检索结果-内蒙古大学图书馆

hybrid Multi-Objective optimization Approach With Pareto Local Search for Collaborative Truck-Drone Routing Problems Considering Flexible Time Windows

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 13011-13025页

作者： Luo, Qizhang Wu, Guohua Ji, Bin Wang, Ling Suganthan, Ponnuthurai Nagaratnam Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

The collaboration of drones and trucks for last-mile delivery has attracted much attention. In this paper, we address a collaborative routing problem of the truck-drone system, in which a truck collaborates with multiple drones to perform parcel deliveries and each customer can be served earlier and later than the required time with a given tolerance. To meet the practical demands of logistics companies, we build a multi-objective optimization model that minimizes total distribution cost and maximizes overall customer satisfaction simultaneously. We propose a hybrid multi-objective genetic optimization approach incorporated with a Pareto local search algorithm to solve the problem. Particularly, we develop a greedy-based heuristic method to create initial solutions and introduce a problem-specific solution representation, genetic operations, as well as six heuristic neighborhood strategies for the hybrid algorithm. Besides, an adaptive strategy is adopted to further balance the convergence and the diversity of the hybrid algorithm. The performance of the proposed algorithm is evaluated by using a set of benchmark instances. The experimental results show that the proposed algorithm outperforms three competitors. Furthermore, we investigate the sensitivity of the proposed model and hybrid algorithm based on a real-world case in Changsha city, China.

关键词： Drones Routing Collaboration optimization Costs Search problems Customer satisfaction Last-mile delivery truck-drone collaborative routing problem flexible time windows multi-objective optimization hybrid optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved parallel chaos optimization algorithm

引用

APPLIED MATHEMATICS AND COMPUTATION 2012年第8期219卷 3590-3599页

作者： Yuan, Xiaofang Yang, Yimin Wang, Hui Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

Chaos optimization algorithm (COA), which has the features of easy implementation, short execution time and robust mechanisms of escaping from the local optimum, is a promising tool for the engineering applications. The design of approaches to improve the convergence of the COA is a challenging issue. Improved mutative-scale parallel chaotic optimization algorithm (MPCOA) are proposed in this paper, and three ways of improvements for MPCOA are investigated in detail: MPCOA combined with simplex search method, MPCOA based on competitive/cooperative inter-communication, MPCOA combined with harmony search algorithm. Several simulation results are used to show the effective performance of these chaos optimization algorithms. (C) 2012 Elsevier Inc. All rights reserved.

关键词： hybrid optimization algorithm Chaos optimization algorithm (COA) Harmony search algorithm Simplex search method

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid method combining electromagnetism-like mechanism and firefly algorithms for constrained design optimization of discrete truss structures

引用

COMPUTERS & STRUCTURES 2019年第Feb.期212卷 20-42页

作者： Duc Thang Le Dac-Khuong Bui Tuan Duc Ngo Quoc-Hung Nguyen Nguyen-Xuan, H. Vietnamese German Univ Dept Computat Engn Binh Duong Vietnam Univ Melbourne Dept Infrastruct Engn Melbourne Vic 3010 Australia Ho Chi Minh City Univ Technol HUTECH CIRTeh Inst Ho Chi Minh City Vietnam Sejong Univ Dept Architectural Engn 209 Neungdong Ro Seoul 05006 South Korea

A new optimization method called Electromagnetism-like Firefly algorithm (EFA), which is a novel hybrid between the Electromagnetism-like algorithm (EM) and the Firefly algorithm (FA) for discrete structural optimization is proposed. The EFA inherits the advantages of both the FA and the EM. This proposed optimization algorithm is then presented to improve both solution accuracy and convergence speed, as well as to treat constrained optimization problems with discrete design variables. In EFA, modified formulas of interactive forces are used to increase the diversification of the population, and the constraint violations are embedded into the charges of all electromagnetic fireflies to avoid becoming trapped in unfeasible domains. A mechanism called "current-to-best" electromagnetic movement is incorporated with traditional interactive movements to balance the exploration and the exploitation abilities of the EFA. In the local search phase, a newly bi-directional searching procedure is performed on the best firefly to intensify effectively its local optimum. In order to guarantee the convergence capability of the EFA, a harmonized selection mechanism combined with the traditional and elitist selections is proposed and applied if the algorithm cannot find a better optimal solution during a number of predetermined optimization loops. In addition, in this study, the FA and EM with some improvements in several phases are also proposed to solve the constrained optimization problems. Finally, a rounding technique is applied to the proposed EFA, FA and EM for solving discrete optimization problems. The improved performance of the EFA in comparison with the FA, EM as well as other optimization algorithms in the literature is demonstrated by six popular truss optimization problems with discrete variables. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： hybrid optimization algorithm Electromagnetism-like firefly algorithm (EFA) Firefly algorithm (FA) Electromagnetism-like mechanism algorithm (EM) Discrete constrained optimization Truss structures

来源：评论

学校读者我要写书评

暂无评论

Solution of economic dispatch problem for wind-thermal power systems by a modified hybrid optimization method

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

引用

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2019年第4期34卷 1871-1895页

作者： Tefek, Mehmet Fatih Uguz, Harun Kirsehir Ahi Evran Univ Kaman Vocat Sch Dept Comp Technol TR-40300 Kaman Kirsehir Turkey Konya Tech Univ Fac Engn & Nat Sci Dept Comp Engn TR-42250 Konya Turkey

Economic Dispatch Problem (EDP) is a complex, constrained and non-linear optimization problem. In the EDP, it is aimed to minimize the system fuel cost between minimum and maximum limits of the active power buses. In this study, a modified hybrid Gravitational Search- Teaching-Learning Based optimization algorithm (MHGT), a quick, efficient and reliable method is proposed by combining standard Gravitational Search algorithm (GSA) and Teaching-Learning Based optimization (TLBO). The proposed MHGT method was developed by modifying the global search superiority in GSA and powerful local search specialty in TLBO for the solution of constrained optimization problem. The MHGT was tested experimentally by well-known and mostly used ten benchmark function in the literature. The proposed method was first implemented on a 6 bus wind-thermal power system for 400, 450 and 500 MW powers. Then, it was implemented on Turkey 19 bus wind-thermal power system according to different ratios of the installed power as 25, 27.5 and 30 percent to solve the EDP problem. The obtained results were compared with the results of other studies. From the results, it is seen that the proposed MHGT method finds the solution in a short execution time and less fuel cost with more reliably and more efficiently in terms of both fuel cost and execution time.

关键词： hybrid optimization algorithm gravitational search algorithm teaching-learning based algorithm economic dispatch problem wind-thermal power system

来源：评论

学校读者我要写书评

暂无评论

Research on Vehicle Routing Planning Based on Adaptive Ant Colony and Particle Swarm optimization algorithm

引用

INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS RESEARCH 2021年第1期19卷 83-91页

作者： Jiang, Chunyan Fu, Jingfang Liu, Weiyan Jiangsu Open Univ Room 1203399 Jiangdong North Rd Nanjing 210036 Jiangsu Peoples R China City Vocat Coll Jiangsu Nanjing 210036 Peoples R China

Aiming at vehicle routing problem and combining the advantages of ant colony and particle swarm optimization, an intelligent optimization algorithm of adaptive ant colony and particle swarm optimization is proposed. Through the simulation of ant colony and bird swarm intelligence mechanism, the particle swarm algorithm and the ant colony algorithm heuristic strategy are combined, and different search strategies are used in different stages of the algorithm. The adaptive adjustment is adopted, and the feedback information is obtained by dynamic interaction with the environment, thus speeding up the convergence speed, improving the learning ability, avoiding the local optimum, getting the best solution and improving the efficiency. The simulation experiment shows that the algorithm has fast convergence speed, strong optimization ability, and can obtain better optimization results. It has some advantages in solving vehicle routing problem.

关键词： Adaptive Ant colony Particle swarm optimization hybrid optimization algorithm Vehicle routing problem

来源：评论

学校读者我要写书评

暂无评论

A multi-objective optimization prediction approach for water resources based on swarm intelligence

引用

EARTH SCIENCE INFORMATICS 2021年第1期14卷 457-468页

作者： Zhang, Feng Zhang, Yongheng Yulin Univ Sch Informat Engn Yulin 719000 Shaanxi Peoples R China

The purpose of this study was to investigate improve the utilization rate of water resources and using multi-objective optimization algorithms to prediction water demand for the next 30 years, then the swarm intelligence approach was used to analysis for the development and utilization of water resources. The results obtained in this study include built a group optimization intelligent algorithm and the multi-objective optimization configuration prediction model for water resources is realized, for the disadvantages of PSO and GA algorithm, a hybrid optimization algorithm based on PSO and GA is proposed. The results indicated that the proposed algorithm can predict the water demand in the next 30 years, and can provide a certain reference for the formulation to effective regulation economic, social and ecological water consumption. Simultaneously, the PSO and GA hybrid optimization algorithm can achieve more than a simple algorithm using PSO or GA optimization results better.

关键词： hybrid optimization algorithm Prediction model Water resources Data analysis Multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Improved Genetic algorithm and Its Application in Parameter optimization for Certain Aeroengine Compressor Guide Vane Regulator

Improved Genetic Algorithm and Its Application in Parameter ...

引用

IEEE International Conference on Electronics, Communications and Control (ICECC)

作者： Peng, Kai Fan, Ding Fu, Jiangfeng Zhang, Lei NW Polytech Univ Sch Power & Energy Xian 710072 Peoples R China

ISBN: (纸本)9781457703218

An improved genetic algorithm (Fuzzy Adaptive Simulated Annealing Genetic algorithm with Gradient direction, GFASAGA) will be proposed in this paper, whose global superlinear convergence properties was analyzed by means by Markov chain etc. Certain fuzzy aeroengine compressor guide vane controller parameters of the regulator were optimized by GFASAGA, standard genetic algorithm (SGA) and customized hybrid optimization algorithm in iSIGHT comparatively, then simulation results show that: the improved genetic algorithm is of good characteristics, such as global search, evolutionary rapidity and so on;the ultimate guide vane regulator formed by semi-physical simulation is provided with good static and dynamic characteristics.

关键词： compressor guide vane regulator fuzzy control genetic algorithm hybrid optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

hybrid optimization Design of a High-Speed Permanent Magnet Machine Used for EV Traction 9

Hybrid Optimization Design of a High-Speed Permanent Magnet ...

引用

9th International Conference and Exposition on Electrical and Power Engineering (EPE)

作者： Fodorean, Daniel Idoumghar, Lhassane Tech Univ Cluj Napoca Cluj Napoca Romania Univ Haute Alsace Mulhouse France

ISBN: (纸本)9781509061280

The paper is dealing with the optimum design of a high-speed permanent magnet synchronous machine (HS-PMSM), to be used for the traction of an electric vehicle. A hybrid optimization algorithm, based on particle swarm and simulated annealing approaches is employed for the optimization of the studied machine. The finite element method approach is used to numerically validate the optimum design of the HSPMSM.

关键词： hybrid optimization algorithm high-speed permanent magnet machine EV application

来源：评论

学校读者我要写书评

暂无评论

Multi-state Markov process Based Maintenance optimization for Power Distribution Transformer

Multi-state Markov process Based Maintenance Optimization fo...

引用

International Conference on Power System Technology (PowerCon)

作者： Liang Hai-feng Zhang Jing Gao Ya-jing North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Baoding 071003 Peoples R China

ISBN: (纸本)9781479950324

Pointing at optimization of the distribution transformer's maintenance based on risk, firstly this paper proposed an equipment failure rate model based on Multi-state Markov process that is suitable for Condition Based Maintenance. And then an age reduction factor is used to modify the failure rate model. A Distribution Transformer Maintenance optimization Model whose objective function is the minimum of the risk of grid operation is established basically. The model is established from the perspective of maintenance accounting the maintenance risk and failure risk. And a hybrid optimization algorithm is proposed to solve the optimized problem. Finally using a case validates the effectiveness of this proposed method.

关键词： Maintenance optimization Markova Process Age reduction factor hybrid optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Dynamic reactive power optimization method for active distribution network with new energy and electric vehicle charging stations connected to the grid 2

Dynamic reactive power optimization method for active distri...

引用

IEEE 2nd International Conference on Power Science and Technology (ICPST)

作者： Rao, Zhen Li, Rui Zhou, Shouhu He, Wei Yuan, Xuan Li, Jia State Grid Jiangxi Elect Power Res Inst Nanchang Jiangxi Peoples R China State Grid Jiangxi Elect Power Co Nanchang Jiangxi Peoples R China Chongqing Univ Technol Chongqing Peoples R China Nanchang Univ Sch Informat Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9798350349047;9798350349030

Taking into account new energy output, EV charging load and residential power load, a clustering-local relaxation-correction dynamic reactive power optimization spatio-temporal decoupling strategy is proposed. Firstly, K-medoids clustering algorithm is used to divide the time period. Then, different types of variables are optimized in the same period. Finally, the number of input groups of shunt capacitor banks (SCBS) in several periods is fixed, and the secondary static reactive power optimization is carried out by using continuous variables. According to the characteristics of the model, a hybrid optimization algorithm with cross-feedback mechanism is used to solve different types of variables, and an improved artificial hummingbird algorithm based on Tent chaos mapping and adaptive variation is proposed to improve the solving efficiency. The simulation results show that the proposed decoupling strategy can not only guarantee the dynamic constraints of discrete variables, but also obtain satisfactory optimization results, and the hybrid algorithm can effectively solve the nonlinear optimization problems of mixed integers.

关键词： Active distribution network new energy electric vehicles dynamic reactive power optimization k-medoids clustering hybrid optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：