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Order Dispatching via GNN-Based optimization algorithm for On-Demand Food Delivery

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第10期25卷 13147-13162页

作者： Chen, Jing-Fang Wang, Ling Liang, Yile Yu, Yang Feng, Jie Zhao, Jiuxia Ding, Xuetao Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Meituan Grp Beijing 100102 Peoples R China Jiangsu Univ Technol Sch Elect & Informat Engn Changzhou 213001 Peoples R China

As one representative of last-mile logistics in intelligent transportation systems, the on-demand food delivery (OFD) service has gained rapid market growth but also faces multiple challenges. One of the critical issues is the order dispatching problem (ODP) with an NP-hard nature, which refers to dispatching a large number of orders to riders reasonably in real time with very limited decision time. To address the ODP, this paper proposes an optimization algorithm based on graph neural networks (GNN) by combining the advantages of machine learning (ML) techniques and operational research (OR) methods: 1) The ML component learns to reduce the solution space by filtering out inappropriate riders for each order, handling the large-scale complexity of ODP. Specifically, we present a rider modeling approach by using GNN to better characterize rider information;besides, two attention mechanisms are designed to adaptively learn the matching relationship between riders and orders. 2) The OR component ensures the solution quality with a greedy and regret value-based dispatching heuristic. Extensive experiments are conducted on real-world datasets to evaluate the performance of the proposed method by comparing it with other existing models and algorithms. The results show that the design of our ML model is effective in yielding better prediction results, and the proposed GNN-based optimization algorithm can effectively and efficiently solve the ODP by improving delivery efficiency and customer satisfaction.

关键词： Graph neural network (GNN) NP-hard on-demand food delivery optimization algorithm order dispatching

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A modeling method for photovoltaic cells using explicit equations and optimization algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 59卷 23-28页

作者： Xu, Yonghai Kong, Xiangyu Zeng, Yawen Tao, Shun Xiao, Xiangning North China Power Elect Univ Dept Elect & Elect Engn State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China

A numerical method for determining the five-parameter model of photovoltaic cells is presented in the paper. Explicit equations are applied to analyze the relations between parameters which are solved by an optimization algorithm. Lambert W function is implemented to convert the I-V characteristic implicit equation to an explicit one, so the output current and voltage of photovoltaic cells can be obtained by substituting the five parameters into the explicit I-V equation. Several cells are used to verify the accuracy of the proposed method from different aspects. It is found that the proposed method gives precise results and can be applicable to various types of photovoltaic cells. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Photovoltaic cells Five-parameter model Explicit equation optimization algorithm Lambert W function

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Free-Form Filters Designed Using Binary optimization algorithm

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IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY 2023年第4期13卷 520-527页

作者： Ruan, Xuexuan Chan, Chi Hou Univ Elect Sci & Technol China UESTC Yangtze Delta Reg Inst YDRI Quzhou 324000 Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China City Univ Hong Kong State Key Lab Terahertz & Millimeter Waves Kowloon Hong Kong Peoples R China

The conventional design of filters requires numerous simulations, taking a substantial amount of time and computational resources and requiring extensive expertise. This article presents a new method to design filters using an optimization algorithm, which can use the fitness function to evaluate the filters autonomously. Experimental results from designing two common filters, including a microstrip filter and a waveguide filter, demonstrate that the proposed approach produces satisfactory results.

关键词： Microwave filters optimization Filtering algorithms Particle swarm optimization Search problems Resonator filters Phased arrays Filter free form microstrip filter optimization algorithm waveguide filter

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Identification of gene knockout strategies using a hybrid of an ant colony optimization algorithm and flux balance analysis to optimize microbial strains

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2014年第PartB期53卷 175-183页

作者： Lu, Shi Jing Salleh, Abdul Hakim Mohamed Mohamad, Mohd Saberi Denis, Safaai Omatu, Sigeru Yoshioka, Michifumi Univ Teknol Malaysia Fac Comp Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia Osaka Inst Technol Dept Elect Informat & Commun Engn Osaka 5358585 Japan Osaka Prefecture Univ Fac Engn Naka Ku Sakai Osaka 5998531 Japan

Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Baker's yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Ant colony optimization algorithm Flux balance analysis Metabolic engineering optimization algorithm Gene knockout strategy Microbial strains

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Root Based optimization algorithm for Task-Oriented Structural Design of a Multi-Axial Road Test Rig

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IEEE ACCESS 2020年 8卷 168061-168078页

作者： Wang, Xiao Cong, Dacheng Yang, Zhidong Han, Junwei Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China

A novel root based optimization algorithm (ROA), which mimics the proliferation of plant roots, is proposed to evaluate the performances of a multi-axial road test rig over a prescribed workspace. Plant roots have developed complex behavior patterns to search for nutrients in the soil and even exhibit swarm intelligence. The growing roots can adapt their actions such as elongation, branching, and tropistic movement according to the environment. During the process of foraging for water and nutrients, the adaptive growth behavior of roots can be simulated to tackle the optimization problem. The efficiency of the proposed ROA has been validated by comparing it with well-known intelligent algorithms using classical and IEEE CEC 2019 benchmark functions. Computational results indicate that ROA outperforms other algorithms to search a global optimum on several benchmarks. Furthermore, the results are tested with nonparametric statistical methods, e.g., Wilcoxon rank-sum test. Besides, ROA is applied to the optimal dimensioning synthesis of the multi-axial road test rig (MRTR) based on the orthogonal 6-RSS parallel manipulator to promote the device's performance. The inverse kinematics of the multi-axial road test rig based on the asymmetrical orthogonal 6-RSS parallel mechanism is analyzed in detail. Applying the achieved dimensionless Jacobian, the performance criteria involving the global isotropy index, the force/velocity transmissibility index, the decoupling index are established. By simultaneously taking account the proposed criteria of performance, structural optimization is carried out resorting to ROA. Finally, a set of optimized dimensional parameters is obtained. The practical application further illustrates that ROA has the potential to deal with complex optimization problems.

关键词： optimization Roads Particle swarm optimization Indexes Benchmark testing Kinematics Space exploration Root adaptive growth optimization algorithm benchmark test orthogonal 6-RSS parallel manipulator task-oriented performance criteria optimal structural design

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Research on crow swarm intelligent search optimization algorithm based on surrogate model

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2020年第10期34卷 4043-4049页

作者： Xu, Huanwei Liu, Liangwen Zhang, Miao Univ Elect Sci & Technol China Chengdu 611731 Peoples R China

A large amount of calculation exists in a complex engineering optimization problem. The swarm intelligence algorithm can improve calculation efficiency and accuracy of complex engineering optimization. In the existing research, the surrogate model and the swarm intelligence algorithm are only two independent tools to solve the optimization problem. In this paper, we propose the surrogate-assisted crow swarm intelligent search optimization algorithm (SACSA) by combining the characteristics of swarm intelligence algorithm and surrogate model. The proposed algorithm utilizes the initial samples to construct the surrogate model, and then the improved crow search algorithm (CSA) is applied to obtain optimal solution. Finally, the proposed algorithm is compared with EGO, MSSR, ARSM-ISES, AMGO and SEUMRE, MPS, HAM algorithms. The comparison results show that the proposed algorithm can find a global optimal solution with fewer samples and is beneficial to improving the efficiency and accuracy of calculation.

关键词： Crow search algorithm Kriging optimization algorithm Surrogate model

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On a novel multi-swarm fruit fly optimization algorithm and its application

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APPLIED MATHEMATICS AND COMPUTATION 2014年 233卷 260-271页

作者： Yuan, Xiaofang Dai, Xiangshan Zhao, Jingyi He, Qian Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China

Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Recently, a kind of Drosophila (fruit fly) inspired optimization algorithm, called fruit fly optimization algorithm (FOA), has been developed. This paper presents a variation on original FOA technique, named multi-swarm fruit fly optimization algorithm (MFOA), employing multi-swarm behavior to significantly improve the performance. In the MFOA approach, several sub-swarms moving independently in the search space with the aim of simultaneously exploring global optimal at the same time, and local behavior between sub-swarms are also considered. In addition, several other improvements for original FOA technique is also considered, such as: shrunk exploring radius using osphresis, and a new distance function. Application of the proposed MFOA approach on several benchmark functions and parameter identification of synchronous generator shows an effective improvement in its performance over original FOA technique. (C) 2014 Elsevier Inc. All rights reserved.

关键词： optimization algorithm Fruit fly optimization algorithm (FOA) Multi-swarm Swarm behavior Cooperative swarms

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Optimized Wavelet-Based Satellite Image De-Noising With Multi-Population Differential Evolution-Assisted Harris Hawks optimization algorithm

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IEEE ACCESS 2020年 8卷 133076-133085页

作者： Golilarz, Noorbakhsh Amiri Mirmozaffari, Mirpouya Gashteroodkhani, Tayyebeh Asgari Ali, Liaqat Dolatsara, Hamidreza Ahady Boskabadi, Azam Yazdi, Mohammad Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Texas Arlington Dept Ind Mfg & Syst Engn Arlington TX 76019 USA Univ Guilan Dept Elect Engn Rasht *** Iran Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Clark Univ Sch Management Worcester MA 01610 USA Washington State Univ Carson Coll Business Dept Finance & Management Sci Pullman WA 99163 USA Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal

In this research, we propose to utilize the newly introduced Multi-population differential evolution-assisted Harris Hawks optimization algorithm (CMDHHO) in the optimization process for satellite image denoising in the wavelet domain. This optimization algorithm is the improved version of the previous HHO algorithm which consists of chaos, multi-population, and differential evolution strategies. In this study, we applied several optimization algorithms in the optimization procedure and we compared the de-noising results with CMDHHO based noise suppression as well as with the Thresholding Neural Network (TNN) approaches. It is observed that applying the CMDHHO algorithm provides us with better qualitative and quantitative results comparing with other optimized and TNN based noise removal techniques. In addition to the quality and quantity improvement, this method is computationally efficient and improves the processing time. Based on the experimental analysis, optimized based noise suppression performs better than TNN based image de-noising. Peak Signal to Noise Ratio (PSNR) and Mean Structural Similarity Index (MSSIM) are used to evaluate and measure the performance of different de-noising methods. Experimental results indicate the superiority of the proposed CMDHHO based satellite image de-noising over other available approaches in the literature.

关键词： optimization Image denoising Wavelet transforms Signal processing algorithms Noise reduction Wavelet domain Thresholding (Imaging) CMDHHO optimization algorithm satellite image de-noising TNN wavelet domain

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Synergistic Swarm optimization algorithm

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2557-2604页

作者： Sharaf Alzoubi Laith Abualigah Mohamed Sharaf Mohammad Sh.Daoud Nima Khodadadi Heming Jia Faculty of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Al Al-Bayt UniversityMafraq25113Jordan Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Department of Industrial Engineering College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia College of Engineering Al Ain UniversityAbu Dhabi112612United Arab Emirates Department of Civil and Architectural Engineering University of MiamiCoral Gables1251USA School of Information Engineering Sanming UniversitySanming365004China

This research paper presents a novel optimization method called the Synergistic Swarm optimization algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search *** cooperation enhances the exploitation of promising regions in the search space while maintaining exploration ***,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and *** leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization *** effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design *** experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and *** codes of SSOA are available at:https://***/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.

关键词： Synergistic swarm optimization algorithm optimization algorithm metaheuristic engineering problems benchmark functions

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A new approach for solving the flow-shop scheduling problem using a parallel optimization algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第12期12卷 10723-10732页

作者： Nazif, Habibeh Payame Noor Univ PNU Dept Math POB 19395-4697 Tehran Iran

In any organization and business, efficient scheduling cause increased efficiency, reducing the time required to complete jobs and increasing an organization's profitability in a competitive environment. Also, the flow-shop scheduling problem is a vital type of scheduling problem with many real-world applications. Flow-shop scheduling has numerous exciting applications in various manufacturing and industrial domains. During the past eras, the growing interests in the arrangement of flow shops with diverse objective functions (for example, minimizing the makespan and flow-time) were observed. The permutation flow-shop is formulated as mixed-integer programming, and it is an NP-Hard problem. Therefore, in this paper, a novel method is provided to decrease the makespan and completion time. Since parallel algorithms use some computing elements to accelerate the search and present a new exploration pattern that is frequently suitable to enhance the quality of the results, in this research, a parallel ant colony optimization algorithm is employed to solve the mentioned problem. The Matlab simulation setting in Net Beans IDE 8.0.2 and Java to simulate the introduced method is applied. According to the obtained results, the suggested procedure has more efficiency than the previous methods. The Matlab simulator outcomes have indicated that the average response time has been improved compared to the PSO-SA and HBC algorithms. Also, the makespan is improved in comparison to GA and MOACSA.

关键词： Flow‐ shop scheduling Parallel ACO algorithm optimization algorithm

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