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Research on Rural Spatial Distribution in China Based on Urban-Rural Integrated Planning Management

Research on Rural Spatial Distribution in China Based on Urb...

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international conference on Computer Sciences and convergence Information technology (ICCIT)

作者： Tao Yu Jing-xiang Zhang Li-jing Sun Wen-yun Huang Department of Urban and Regional Planning Nanjing University Nanjing China Nanjing Municipal Finance Bureau Nanjing China Yichang Municipal Planning Bureau Yichang China

Along with the rapid development of urbanization and industrialization in China, the vast rural areas in China, especially in developed regions, is confronted with new development bottleneck and confusion. Under such circumstance, universal attention has been paid to urban-rural integrated planning as an effective way to promote positive interaction between urban and rural areas as well as a new perspective to study rural spatial distribution in a new area in China. By analyzing the concept and connotation of urban-rural integrated planning in combination with the case study of Jiangning subdistrict in Nanjing, this paper carries out some useful exploration in current problems and contradictions in rural spatial development, methods and principles for distribution planning as well as innovation of relative supporting policies and systems in China.

关键词： Urban planning technology planning Environmental economics Distributed computing Information technology conference management Financial management Innovation management Research and development management technology management

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Indicators Directed Multi-strategy Artificial Bee Colony Algorithm 1

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Proceedings of the 4th international conference on Neural computing for Advanced Applications, NCAA 2023

作者： Wu, Jiali Wang, Jingwu Wang, Wenjun Liu, Jiawen Wang, Hui Zhang, Hai Hu, Min School of Information Engineering Nanchang Institute of Technology Nanchang330099 China School of Electrical Engineering Nanchang Institute of Technology Nanchang330099 China School of Business Administration Nanchang Institute of Technology Nanchang330099 China

ISBN: (数字)9789819958443

ISBN: (纸本)9789819958436

Artificial bee colony (ABC) algorithm is commonly used to solve various optimization problems. Though ABC shows strong exploration capability, its weak exploitation may easily result in slow convergence. To tackle this issue, several multi-strategy ABC variants were proposed. Employing multiple search strategies with distinct features facilitates an appropriate balance between exploration and exploitation. However, choosing an appropriate strategy for the current search is a difficult task. This article suggests a new ABC variant named indicators directed multi-strategy ABC (IDMABC) to address this issue. Three evaluation indicators are designed to help ABC adaptively select suitable search strategies during the evolution. To validate the optimization capability of IDMABC, 22 classical problems are tested. IDMABC is evaluated against five other ABC variants. Results show the competitiveness of IDMABC. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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An Optimized Path Selection Algorithm for the Minimum Number of Turns in Path Planning Using a Modified A-Star Algorithm 1

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6th international conference on Innovative computing and Communication, ICICC 2023

作者： Kumar, Narayan Kumar, Amit Department of Mechanical Engineering Muzaffarpur Institute of Technology Muzaffarpur India Department of Mechanical Engineering National Institute of Technology Patna India

ISBN: (数字)9789819930104

ISBN: (纸本)9789819930098

Robots are becoming more prevalent in everyday life. The most difficult aspect of using a robot is defining its path. Path planning describes the movement of the robot from the starting point to the goal point. This research provides an improved A-star method for a mobile robot’s path-planning finding capabilities in challenging maps. The improved A-star algorithm makes use of the A-star algorithm’s properties. First, the grid surface model is built, and the modified A-star algorithm’s evaluation function is calculated. Second, the acquired multiple least-distance paths were further analyzed using a global minimum number of turns approach, which can speed up convergence and smoothen the global path. In the combinatorial selection of the optimum path from the set of the shortest paths, the path with the minimum diversion of the robot from the initial to the final position is used. It enables the robot to travel the greatest distance possible in a single spin. The straight path enables the robot to jump and cover the most amount of ground in a single leap while keeping a consistent pace. MATLAB was used to run the experiments on a specific grid area. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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Enhanced Monarch Butterfly Optimization Algorithm with Rank-Based Selection and Random Jump Strategy for Constrained Engineering Problems

Enhanced Monarch Butterfly Optimization Algorithm with Rank-...

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international conference on Circuit, Power and computing Technologies (ICCPCT)

作者： Panimalar Kathiroli Kanmani. S Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of science and technology Chennai Department of Information Technology Puducherry Technological University Puducherry

Nature-inspired algorithms have become increasingly prevalent in engineering and mathematical optimization due to their efficacy and adaptability. Among these algorithms, the Monarch Butterfly Optimization Algorithm (MBOA) is a population-based metaheuristic, characterized by its simple implementation and the utilization of the Migration Operator (MO) and Butterfly Adjusting Rate (BAR) to guide the search path of butterflies. However, the algorithm can suffer from susceptibility to local optima traps, early convergence, and inferior performance due to a lack of random factors. To address these challenges, we propose an Enhanced Monarch Butterfly Optimization Algorithm (E-MBOA) that incorporates Ranking-based Selection (RBS) and a Random Jumping strategy. The RBS prioritizes the search direction based on individuals' ranks in the population, enhancing the search process by encouraging the exploration of feasible areas with lower objective function values. By employing the Random Jumping strategy, the process can effectively investigate the search space and avoid local optima, thus mitigating early convergence issues. The proposed model is assessed on a variety of mechanical and engineering design problems. Results are contrasted to other optimization approaches published in the literature, demonstrating that the proposed model offers faster convergence to global optima and effectively avoids local optima traps. E-MBOA's performance validates its feasibility and effectiveness as a powerful tool for resolving complex engineering optimization problems.

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Apply the Feature of Entropy convergence of ACO to Short the Runtime of Gene Order

Apply the Feature of Entropy Convergence of ACO to Short the...

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international conference on Genetic and Evolutionary computing (ICGEC)

作者： Ben-Qiong Hu Gang Jiang Shi-Peng Wang Chao-Yang Pang College of Information Management Chengdu University of Technology Chengdu China Group of Gene Computation College of Mathematics and Software Science Sichuan Normal University Chengdu China College of Mathematics and Software Science Sichuan Normal University Chengdu China

Alzheimer disease (AD) is the most common form of dementia. To find a way of cure it, gene study is necessary. And gene order is a new conception of gene study currently, where gene order refers to a permutation of genes in which similar genes are ordered together one by one, and optimal gene order can be abstracted as shortest TSP route. Currently only two types of tools are reported to calculate gene order, which are Genetic Algorithm (GA) and Ant Colony Optimization (ACO). In these two types, one bottleneck of computation is that their runtime is too long while gene data is too large. To weaken the bottleneck, in this paper, the feature of entropy convergence of ACO is used as the termination criterion of ACO to speed up the computation of AD gene order. Experiment shows that the method proposed in this paper has obvious advantage on runtime and solution quality.

关键词： Entropy convergence Gallium Cities and towns Alzheimer's disease Runtime Clustering algorithms

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A linear MPC algorithm for embedded systems with computational complexity guarantees

A linear MPC algorithm for embedded systems with computation...

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international conference on System Theory, Control, and computing (ICSTCC)

作者： Ion Necoara Florin Stoican Dragos Clipici Andrei Patrascu Morten Hovd Automatic Control and Systems Engineering Department University Politehnica Bucharest Romania Department of Engineering Cybernetics Norwegian University of Science and Technology Norway

In this paper we propose a linear MPC scheme for embedded systems based on the dual fast gradient algorithm for solving the corresponding control problem. We establish computational complexity guarantees for the MPC scheme by appropriately deriving tight convergence estimates of order O(1/k 2 ) for an average primal sequence generated by our proposed numerical optimization algorithm. We also show that these estimates rely heavily upon the dual optimal variables (Lagrange multipliers). Since convergence certification in embedded MPC is essential, we also derive tight bounds on the norm of these dual optimal variables. However, computing the norm for the Lagrange multipliers associated to multi-parametric optimization problems can quickly become intractable for high dimension and/or a large set of constraints. Therefore, we recast the problem in a mixed integer formulation by using auxiliary binary variables to characterize the complementarity conditions. We also show that this problem can be solved numerically efficiently.

关键词： convergence Embedded systems Memory management Gradient methods Vectors Sparse matrices

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An Improved Artificial Bee Colony Algorithm with Multiple Search Strategy 15th

An Improved Artificial Bee Colony Algorithm with Multiple Se...

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15th international conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Ma, Jun Zhao, Yunlong Nanjing University of Aeronautics and Astronautics NanjingJiangsu China Collaborative Innovation Center of Novel Software Technology and Industrialization NanjingJiangsu China

ISBN: (纸本)9783030642426

Artificial Bee Colony (ABC) has been applied to solve constrained optimization problems such as green wireless communications, path planning and so on. To solve the problem that ABC algorithm is easy to fall into local optimum, this paper proposes an Improved Artificial Bee Colony (IABC) algorithm with multiple search strategy. An opposition-based learning technique is integrated in initialization phase. Then, in order to speed up convergence rate, each employed bee searches for neighbor with adding global information. Furthermore, multiple search strategy is used to balance the exploitation and exploration during the onlooker bee phase. Inspired by Modification Rate (MR), the solution generation method of new bees whose trail have exceed limit is modified to increase disturbance in scout bee phase. Six benchmark functions are used to test the efficiency and stability of the algorithm, and the simulation results show IABC algorithm performs better than ABC algorithm in high dimensional space. © 2020, Springer Nature Switzerland AG.

关键词： Motion planning

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Adaptive Methods of Differential Evolution Multi-objective Optimization Algorithm Based on Decomposition 1

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2nd international conference on Neural computing for Advanced Applications, NCAA 2021

作者： Bian, Kun Sun, Yifei Cheng, Shi Liu, Zhuo Sun, Xin School of Physics and Information Technology Shaanxi Normal University Xi’an710119 China School of Computer Science Shaanxi Normal University Xi’an710119 China

ISBN: (数字)9789811651885

ISBN: (纸本)9789811651878

Decomposition-based algorithms e.g., multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been proved as an effective and useful solution in a variety of multi-objective optimization problems (MOPs). On the basis of MOEA/D, the MOEA/D-DE replaces the simulated binary crossover (SBX) operator, which is used to enhance the diversity of the solutions, into differential evolution (DE) operator. However, the amplification factor and the crossover probability are fixed in MOEA/D-DE, which would lead to a low convergence rate and be more likely to fall into local optimum. To overcome such prematurity problem, this paper proposes three different adaptive operators in DE to adjust the parameter settings adaptively, including crossover probability and amplification factor. This paper also designs a changeable parameter η in the proposed algorithms. Several experiments are set to explore how the η would affect the convergence of the proposed algorithms. These adaptive algorithms are tested on many benchmark problems in comparison to MOEA/D-DE. The experimental results illustrate that the three proposed adaptive algorithms have better performance on the most benchmark problems. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Alternate Iteration of Shuffled Belief Propagation Decoding

Alternate Iteration of Shuffled Belief Propagation Decoding

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WRI international conference on Communications and Mobile computing, CMC

作者： Sheng Wu Xiaobo Jiang Zhenghua Nie School of Electronic and Information Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781424463275;9780769539898

Low-Density Parity-Check (LDPC) codes are usually decoded by running an iterative decoding algorithm, such as belief-propagation (BP), over the factor graph of the codes. The standard BP schedule is a parallelizable decoding, using the same pre-update information during updating all the check nodes and bit nodes. In order to improve the convergence speed in terms of number of iterations, several sequential schedules were studied, such as the Layered belief-propagation (LBP) and the Shuffled belief-propagation (SBP). Correspondingly, for these serial decoding algorithms, the messages were generated by using the latest available information. In this paper, a modified shuffled version of iterative decoding of low-density parity-check codes is present by changing the updating order at bit nodes. Compared with the plain shuffled version, the proposed scheme has the same convergence rate which is twice as the conventional belief propagation, and can reduce complexity without any performance degradation. At last, the simulation result was shown with respect to the LDPC codes in CMMB standard.

关键词： Belief propagation Iterative decoding Parity check codes Iterative algorithms Code standards Sparse matrices AWGN convergence Scheduling Binary phase shift keying

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Stable path selection and safe backup routing for Optical Border Gateway Protocol (OBGP) and Extended Optical Border Gateway Protocol (OBGP+)

Stable path selection and safe backup routing for Optical Bo...

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international conference on Communication, Information & computing technology

作者： Shailesh P. Bendale Girish V. Chowdhary Department of Computer Engineering Dr. Babasaheb Ambedkar Technological University Raigad Maharashtra India

Border Gateway Protocol (BGP) is an inter-domain routing protocol that allows an autonomous system (AS) to apply local policies for selecting the best route and decide whether the route information is propagated to other ASes or not. But since the last few years, the research community has shown an increasing interest in the subject of inter-domain routing in optical networks, so there will be need to modify existing BGP with optical counterparts. However, in past few years it is widely accepted now that a multi-domain routing model mostly centered on the exchange of reachability information. So the protocol like the one we have today or the one provided by Optical Border Gateway Protocol (OBGP) is not going to be enough in the future. So routing domains must be able to exchange both, reachability, as well as aggregated Path State Information (PSI). Understanding that there is a missing piece in the routing models provided by BGP and OBGP, but contributing with solutions capable of highly improving the performance of these routing protocols without increasing the number and frequency of the routing messages exchanged between domains is a challenging task. So introduction to the Extended Optical Border Gateway Protocol(OBGP+) was proposed to overcome shortcomings. But when it comes to the case of failure scenario, it is known to us that during convergence BGP withdraws the failed path and selects immediately an alternate path for backup routing. The time required for this convergence should be minimum as possible otherwise the data loss will be enormous. In this paper, convergence time had been studied for Optical Border Gateway Protocol (OBGP) and Extended Optical Border Gateway Protocol (OBGP+). Extensive simulation of the experiments reveals that OBGP+ is performing better than OBGP in the failure scenarios.

关键词： Routing Optical fiber networks convergence Peer to peer computing Routing protocols Integrated optics

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