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作者： Xu, Yunfei Wang, Xianjun Yu, Huaizhi Shandong Guonong Logistics Technology Co. Ltd. Jinan China Shandong Institute of Commerce and Technology Jinan China

With the economic development and the improvement of cold chain logistics service technology, more and more fresh food enterprises are committed to expanding their retail scope, seizing market share and improving their profits. This paper studies the pricing decision optimization algorithm of fresh agricultural product supply chain under the background of artificial intelligence, aiming to improve the efficiency and competitiveness of fresh agricultural product supply chain. Aiming at the difficulties and challenges in the pricing decision of fresh agricultural products supply chain, an optimization algorithm is proposed. The algorithm considers the impact of delivery time on the pricing and service decisions of fresh agricultural products, aims to improve the revenue of supply chain members, constructs a dual-channel game model, analyzes and evaluates multiple links in the fresh agricultural product supply chain, optimizes the pricing strategy, and provides appropriate management suggestions for the relevant decisions of fresh supply chain members. This paper discusses and analyzes the relevant theories and methods of pricing decision-making in the supply chain of fresh agricultural products, selects the actual fresh agricultural products market as the experimental platform, and verifies the effectiveness and feasibility of the algorithm through experiments. The results show that the pricing decision optimization algorithm of fresh agricultural products supply chain based on artificial intelligence technology has high accuracy and efficiency, which can provide useful help for the management and optimization of fresh agricultural product supply chain. The results of this study are of great significance for the management and optimization of fresh agricultural supply chain, and the algorithm can be further studied and applied in the future to improve the efficiency and competitiveness of fresh agricultural product supply chain. © The Author(s), under exclusive licens

关键词： Artificial intelligence Game theory Model building optimization algorithm Supply chain

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Prairie Dog optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2022年第22期34卷 20017-20065页

作者： Ezugwu, Absalom E. Agushaka, Jeffrey O. Abualigah, Laith Mirjalili, Seyedali Gandomi, Amir H. Univ KwaZulu Natal Sch Math Stat & Comp Sci King Edward Rd ZA-3201 Pietermaritzburg Kwazulu Natal South Africa Fed Univ Lafia Dept Comp Sci Lafia 950101 Lafianasarawa S Nigeria Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Torrens Univ Ctr Artificial Intelligence Res & Optimizat Adelaide SA Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW Australia

This study proposes a new nature-inspired metaheuristic that mimics the behaviour of the prairie dogs in their natural habitat called the prairie dog optimization (PDO). The proposed algorithm uses four prairie dog activities to achieve the two common optimization phases, exploration and exploitation. The prairie dogs' foraging and burrow build activities are used to provide exploratory behaviour for PDO. The prairie dogs build their burrows around an abundant food source. As the food source gets depleted, they search for a new food source and build new burrows around it, exploring the whole colony or problem space to discover new food sources or solutions. The specific response of the prairie dogs to two unique communication or alert sound is used to accomplish exploitation. The prairie dogs have signals or sounds for different scenarios ranging from predator threats to food availability. Their communication skills play a significant role in satisfying the prairie dogs' nutritional needs and anti-predation abilities. These two specific behaviours result in the prairie dogs converging to a specific location or a promising location in the case of PDO implementation, where further search (exploitation) is carried out to find better or near-optimal solutions. The performance of PDO in carrying out optimization is tested on a set of twenty-two classical benchmark functions and ten CEC 2020 test functions. The experimental results demonstrate that PDO benefits from a good balance of exploration and exploitation. Compared with the results of other well-known population-based metaheuristic algorithms available in the literature, the PDO shows stronger performance and higher capabilities than the other algorithms. Furthermore, twelve benchmark engineering design problems are used to test the performance of PDO, and the results indicate that the proposed PDO is effective in estimating optimal solutions for real-world optimization problems with unknown global optima. The PDO

关键词： Nature-inspired Prairie dogs optimization algorithm Anti-predation Burrow-building optimization Swarm intelligence

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Highly Efficient and Flexible optimization algorithm for Magnetic Components in Power Electronics with Comprehensive Thermal and Power Loss Analysis 25

Highly Efficient and Flexible Optimization Algorithm for Mag...

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25th European Conference on Power Electronics and Applications (EPE ECCE Europe)

作者： Fiesser, Sven Schwalbe, Ulf Ilmenau Univ Technol Ehrenbergstr 29 Ilmenau Germany Fulda Univ Appl Sci Leipziger Str 123 Fulda Germany

This paper presents an algorithm for the optimization of magnetic components in power electronics which includes a comprehensive analysis of the power losses and the temperature rise using dimensionless numbers for na... 详细信息

ISBN: (纸本)9789075815412

关键词： Magnetics optimization algorithm Thermal design Power losses

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An Improved Snake optimization algorithm and Its Application in Manipulator Trajectory Planning 35

An Improved Snake Optimization Algorithm and Its Application...

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35th Chinese Control and Decision Conference (CCDC)

作者： Li, Wei Zhao, Jiangbo Wang, JunZhen Beijing Inst Technol Sch Automat Beijing Peoples R China

ISBN: (纸本)9798350334722

Manipulator is a kind of widely used mechanical equipment, which has important applications in industry, agriculture, medical and other fields. Manipulator trajectory planning has always been a hot topic in manipulator research. In this paper, an improved snake optimization algorithm is proposed for robot manipulator trajectory planning in complex scenes. Firstly, the mathematical model of trajectory planning problem is constructed and a new cost function with constraint penalty term is proposed. Secondly, the updating mechanism of SO algorithm is improved and a new chaotic local search method is proposed to enhance the search ability and convergence speed of the algorithm. Finally, simulation experiments are carried out in multiple complex environments and compared with traditional algorithms. Experimental results show that ISO algorithm has better performance and stability in trajectory planning.

关键词： snake optimization robot manipulator trajectory planning obstacle avoidance optimization algorithm

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Probabilistic optimal planning of dispatchable distributed generator units in distribution systems using a multi-objective velocity-based butterfly optimization algorithm

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RENEWABLE ENERGY FOCUS 2022年 43卷 191-209页

作者： Kumar, T. Vinod Injeti, Satish Kumar Natl Inst Technol Warangal Dept Elect Engn Warangal 506004 Telengana India

Nowadays, because of the enormous increase in load demand, the electrical distribution system faces problems like poor system efficiency due to high I2R losses and poor voltage profile. Therefore, distribu-tion system operators are looking for various alternatives for enhancing system efficiency & voltage pro-file. Distributed generation (DGs) technology has recently been the focus of several researchers due to its enormous technological advantages in mitigating the above problems. In this article, an approach is pre-sented for the optimal integration of dispatchable distributed generations (DDG): PV-BESS (Photovoltaic System-Battery energy storage system), WT-Biomass (Wind Turbine) units in the distribution system in the presence of optimal network reconfiguration. Distribution generations like PV & WT are non-dispatchable in nature due to the intermittency nature of solar radiance and wind speed. The PV unit is supported by BESS, while the WT unit is supported by Biomass to make the PV and WT units dispatch -able. Therefore, the paper's main intent is to determine the best locations & best sizes of PV-BES, Wind -Biomass units in the distribution system in the presence of network reconfiguration considering the time-varying 24-hour load pattern, probabilistic nature of solar irradiance & wind speed. To reduce sys-tem energy loss, voltage deviation index, and annual economic loss, a multi-objective pareto-based veloc-ity butterfly optimization algorithm (MOVBOA) is used. IEEE 33,69 & 118 bus test systems are being used to implement the proposed approach. The MOVBOA algorithm gives better results for solving the prob-lem than the multi-objective Butterfly optimization algorithm (MOBOA) & Non-dominated sorting genetic algorithm (NSGA-II).(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Dispatchable distribution generations (DDGs) Photo-Voltaic (PV) Wind Turbine (WT) Battery energy storage system (BESS) Biomass optimization algorithm

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Routing optimization of Computer Network Based on Rough Set Theory and Application of optimization algorithm 1st

Routing Optimization of Computer Network Based on Rough Set ...

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World Conference on Intelligent and 3-D Technologies (WCI3DT)

作者： Cai, E. Yunnan Power Grid Co Ltd Informat Ctr Kunming 650011 Yunnan Peoples R China

ISBN: (纸本)9789811971860;9789811971846;9789811971839

The main function of computer network routing is to realize the data connection of Internet ports and provide information transmission function for different networks. In recent years, with the continuous development of multimedia technology, related data services are also constantly strengthened, and computer network applications have become an indispensable part of people's lives. The increase in the number of computer network users and large-scale network coverage also brings higher requirements and challenges to the development of computer networks in China. Rough set theory is an effective tool to solve the system decision-making problem with uncertain and incomplete information;so in this paper, we will be able to apply this theory to build the decision reasoning system of computer routing optimization and optimization algorithm. Based on rough set theory, this paper studies the optimization of computer network routing and the application of optimization algorithms. While optimizing the computer network routing, it can further improve the effective utilization of computer network resources and improve the service quality of computer network for users. In this paper, taking network routing and sample types as the starting point, the optimization of computer network routing and its optimization algorithm are proposed for how to strengthen the service function of computer network routing and further improve the stability and completeness of computer network information transmission.

关键词： Rough set theory Computer network Routing optimization optimization algorithm

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An enhanced donkey and smuggler optimization algorithm for choosing the precise job applicant

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Iran Journal of Computer Science 2023年第3期6卷 233-243页

作者： Hasan, Nazir M. Rashid, Tarik A. Alsadoon, Abeer Qosaeri, Ahmed S. Abualigah, Laith Yaseen, Zaher Mundher Computer Science and Engineering Department University of Kurdistan Hawler KR Erbil Iraq Charles Sturt University Sydney Australia Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia

Throughout the last few decades, Nature-Inspired algorithms (NIA) have become very popular in solving real-world problems by getting inspiration from nature. This work suggests the Modified Donkey and Smuggler optimization (MDSO) algorithm for solving the selection problem to choose suitable job applicants for a specific position. The original Donkey and Smuggler optimization algorithm (DSO) has two modes: smuggler and donkey mode (non-adaptive and adaptive). In the smuggler mode, the algorithm tries to find the best solutions, which is the path to send the donkey to the destination;once the best path is found, the smuggler will send the donkey through the selected path. The donkey's actions will start when the smuggler mode's best solution is no longer the best one. Certain modifications have been made in the smuggler mode, including replacing the original fitness with a new fitness equation since that method can work more accurately. The Human Resource (HR) department in Korek-telecom has been used as a resource to achieve real-world data to test the original and Modified DSO. Using MDSO, not only will organizations be able to choose suitable job applicants more accurately but also will manage to do so at a faster pace as well. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Donkey and smuggler algorithms Human resource management MDSO Nature-inspired algorithms optimization algorithm Swarm intelligence

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Comparative study of microgrid optimal scheduling under multi-optimization algorithm fusion 10

Comparative study of microgrid optimal scheduling under mult...

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10th International Forum on Electrical Engineering and Automation, IFEEA 2023

作者： Duan, Hongyi Li, Qingyang Li, Yuchen Ji, Tianjiao Zhang, Jianan Xie, Yuming Xi'an Jiaotong University Faculty of Electronic and Information Engineering Xi'an China Shanghai University of Finance and Economics University School of Mathematica Shanghai China National University of Defense Technology Faculty of Electronic Science Changsha China

ISBN: (纸本)9798350309379

As global attention on renewable and clean energy grows, the research and implementation of microgrids become paramount. This paper delves into the methodology of exploring the relationship between the operational and environmental costs of microgrids through multi-objective optimization models. By integrating various optimization algorithms like Genetic algorithm, Simulated Annealing, Ant Colony optimization, and Particle Swarm optimization, we propose an integrated approach for microgrid optimization. Simulation results depict that these algorithms provide different dispatch results under economic and environmental dispatch, revealing distinct roles of diesel generators and micro gas turbines in microgrids. Overall, this study offers in-depth insights and practical guidance for microgrid design and operation. © 2023 IEEE.

关键词： Microgrid Multi-objective Dispatchs multi-target optimization algorithm

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SSALEO optimization algorithm for Feature Selection for Diabetic Retinopathy 24

SSALEO Optimization Algorithm for Feature Selection for Diab...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Salem, Mohammed A. Alijla, Basem O. Islamic University of Gaza Palestine

ISBN: (纸本)9798350384307

Diabetic Retinopathy is a severe health problem in the modern world that affects people of all ages and can result in blindness. Many of the affected persons won't lose their eyesight if Diabetic Retinopathy can be identified in its early stages. Therefore, several studies employ several classification algorithms in order to detect DR disease in its early stages. In this study, we use the SSALEO optimization algorithm as a Multi-Objective Feature Selection algorithm in order to obtain higher accuracy with the minimum number of most informative features (i.e., feature reduct). The APTOS-2019 Gaussian-Filtered dataset was used which contains 3662 images. In the proposed model, features were extracted from the input images using a novel feature extraction technique called 'FE-CM'. Feature Selection was performed using SSALEO optimization algorithms as a wrapper-based feature selection using the KNN classifier. the performance of SSALEO was compared using five well-known optimization algorithms: PSO, GWO, MFO, WOA, and BAT. The findings confirm the capability of the SSALEO algorithm as a Feature Selection algorithm in order to help ophthalmologists to detect the disease accurately. © 2023 IEEE.

关键词： Diabetic Retinopathy Feature Selection optimization algorithm SSALEO

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Relaxed static output feedback control for discrete-time Takagi-Sugeno fuzzy systems: A switching sequence convex optimization algorithm

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INFORMATION SCIENCES 2024年 678卷

作者： Gao, Jingjing Xie, Xiangpeng Xia, Jianwei Liaocheng Univ Sch Math Sci Liaocheng 252059 Shandong Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210023 Peoples R China

In this paper, a new switching sequence convex optimization (SSCO) algorithm is proposed for solving non-convex optimization problem with complex time-varying relaxation matrix structures that arises during output feedback design. Firstly, the introduced time-varying relaxation matrix combines the membership functions and the designed switching mechanism to adjust the positive and negative terms of the inequality constraints. As a result, relaxed controller design conditions with complex matrix structures are established. The proposed SSCO algorithm employs switching optimization variables and inner approximation strategy, which is able to compute non-convex optimization problems with complex matrix structures more flexibly and converge quickly. It is worth noting that the implementation of the SSCO algorithm requires a set of strictly feasible initial solutions. Therefore, an initialization iterative algorithm is proposed, which overcomes the difficulties of transforming the solving problem into a typical non-convex optimization problem and linearizing multiple different concave parts, by which a set of optimized feasible solutions are obtained. Finally, simulation examples are used to demonstrate the superiority of the design scheme proposed in this paper.

关键词： Takagi-Sugeno fuzzy systems Static output feedback Time-varying relaxation matrix technique optimization algorithm

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