Fast and secure elliptic curve scalar multiplication algorithm based on special addition chains

To resist the side chaimel attacks of elliptic curve cryptography, a new fast and secure point multiplication algorithm is proposed. The algorithm is based on a particular kind of addition chains involving only additions, providing a natural protection against side channel attacks. Moreover, the new addition formulae that take into account the specific structure of those chains making point multiplication very efficient are proposed. The point multiplication algorithm only needs 1 719 multiplications for the SAC260 of 160-bit integers. For chains of length from 280 to 260, the proposed method outperforms all the previous methods with a gain of 26% to 31% over double-and add, 16% to22% over NAF, 7% to 13% over4-NAF and 1% to 8% over the present best algorithm--double-base chain.

A blockchain bee colony double inhibition labor division algorithm for spatio-temporal coupling task with application to UAV swarm task allocation

It is difficult for the double suppression division algorithm of bee colony to solve the spatio-temporal coupling or have higher dimensional attributes and undertake sudden tasks.Using the idea of clustering,after clustering tasks according to spatio-temporal attributes,the clustered groups are linked into task sub-chains according to similarity.Then,based on the correlation between clusters,the child chains are connected to form a task chain.Therefore,the limitation is solved that the task chain in the bee colony algorithm can only be connected according to one dimension.When a sudden task occurs,a method of inserting a small number of tasks into the original task chain and a task chain reconstruction method are designed according to the relative relationship between the number of sudden tasks and the number of remaining tasks.Through the above improvements,the algorithm can be used to process tasks with spatio-temporal coupling and burst tasks.In order to reflect the efficiency and applicability of the algorithm,a task allocation model for the unmanned aerial vehicle(UAV)group is constructed,and a one-to-one correspondence between the improved bee colony double suppression division algorithm and each attribute in the UAV group is proposed.Task assignment has been constructed.The study uses the self-adjusting characteristics of the bee colony to achieve task allocation.Simulation verification and algorithm comparison show that the algorithm has stronger planning advantages and algorithm performance.

A Chain Routing Algorithm Based on Traffic Prediction in Wireless Sensor Networks

As a representative of chain-based protocol in Wireless Sensor Networks (WSNs), EEPB is an elegant solution on energy efficiency. However, in the latter part of the operation of the network, there is still a big problem: reserving energy of the node frequently presents the incapacity of directly communicating with the base station, at the same time capacity of data acquisition and transmission as normal nodes. If these nodes were selected as LEADER nodes, that will accelerate the death process and unevenness of energy consumption distribution among nodes.This paper proposed a chain routing algorithm based ontraffic prediction model (CRTP).The novel algorithmdesigns a threshold judgment method through introducing the traffic prediction model in the process of election of LEADER node. The process can be dynamically adjusted according to the flow forecasting. Therefore, this algorithm lets the energy consumption tend-ing to keep at same level. Simulation results show that CRTP has superior performance over EEPB in terms of balanced network energy consumption and the prolonged network life.

Development and Comparison of Hybrid Genetic Algorithms for Network Design Problem in Closed Loop Supply Chain

This paper presents four different hybrid genetic algorithms for network design problem in closed loop supply chain. They are compared using a complete factorial experiment with two factors, viz. problem size and algorithm. Based on the significance of the factor “algorithm”, the best algorithm is identified using Duncan’s multiple range test. Then it is compared with a mathematical model in terms of total cost. It is found that the best hybrid genetic algorithm identified gives results on par with the mathematical model in statistical terms. So, the best algorithm out of four algorithm proposed in this paper is proved to be superior to all other algorithms for all sizes of problems and its performance is equal to that of the mathematical model for small size and medium size problems.

Models for Location Inventory Routing Problem of Cold Chain Logistics with NSGA-Ⅱ Algorithm

In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem.

Manufacturing Supply Chain Optimization Problem with Time Windows Based on Improved Orthogonal Genetic Algorithm

Aim to the manufacturing supply chain optimization problem with time windows,presents an improved orthogonal genetic algorithm to solve it. At first,we decompose this problem into two sub-problems (distribution and routing) plus an interface mechanism to allow the two algorithms to collaborate in a master-slave fashion,with the distribution algorithm driving the routing algorithm. At second,we describe the proposed improved orthogonal genetic algorithm for solving giving problem detailedly. Finally,the examples suggest that this proposed approach is feasible,correct and valid.

An Algorithm for the Inverse Problem of Matrix Processing: DNA Chains, Their Distance Matrices and Reconstructing

We continue to consider one of the cybernetic methods in biology related to the study of DNA chains. Exactly, we are considering the problem of reconstructing the distance matrix for DNA chains. Such a matrix is formed on the basis of any of the possible algorithms for determining the distances between DNA chains, as well as any specific object of study. At the same time, for example, the practical programming results show that on an average modern computer, it takes about a day to build such a 30 × 30 matrix for mnDNAs using the Needleman-Wunsch algorithm;therefore, for such a 300 × 300 matrix, about 3 months of continuous computer operation is expected. Thus, even for a relatively small number of species, calculating the distance matrix on conventional computers is hardly feasible and the supercomputers are usually not available. Therefore, we started publishing our variants of the algorithms for calculating the distance between two DNA chains, then we publish algorithms for restoring partially filled matrices, i.e., the inverse problem of matrix processing. Previously, we used the method of branches and boundaries, but in this paper we propose to use another new algorithm for restoring the distance matrix for DNA chains. Our recent work has shown that even greater improvement in the quality of the algorithm can often be achieved without improving the auxiliary heuristics of the branches and boundaries method. Thus, we are improving the algorithms that formulate the greedy function of this method only. .

On Finding the Smallest Generalized Eigenpair Using Markov Chain Monte Carlo Algorithm

This paper proposes a new technique based on inverse Markov chain Monte Carlo algorithm for finding the smallest generalized eigenpair of the large scale matrices. Some numerical examples show that the proposed method is efficient.

A Novel GLS Consensus Algorithm for Alliance Chain in Edge Computing Environment

Edge computing devices are widely deployed.An important issue that arises is in that these devices suffer from security attacks.To deal with it,we turn to the blockchain technologies.The note in the alliance chain need rules to limit write permissions.Alliance chain can provide security management functions,using these functions to meet the management between the members,certification,authorization,monitoring and auditing.This article mainly analyzes some requirements realization which applies to the alliance chain,and introduces a new consensus algorithm,generalized Legendre sequence(GLS)consensus algorithm,for alliance chain.GLS algorithms inherit the recognition and verification efficiency of binary sequence ciphers in computer communication and can solve a large number of nodes verification of key distribution issues.In the alliance chain,GLS consensus algorithm can complete node address hiding,automatic task sorting,task automatic grouping,task node scope confirmation,task address binding and stamp timestamp.Moreover,the GLS consensus algorithm increases the difficulty of network malicious attack.

Algebraic or Algorithmic: Searching for Optimal Solutions in Multi-Stage Supply Chain Models

In this paper we proposed an AMH Supply Chain model to obtain optimal solutions for Two-, Three- and Four-Stage for deterministic models. Besides deriving its algebraic solutions, a simple searching method is successfully applied in obtaining optimal total costs and its integer multipliers. Our model has shown promising results in comparison to Equal Cycle Time and other existing ones. The tests focused on obtaining optimal total annual costs and other related details of Two-, Three- and Four-Stage for deterministic models. The results are run under Visual Basic Programming platform using Intel? CoreTM2 Duo T6500 Processor.

Parallel ant colony algorithm and its application in the capacitated lot sizing problem for an agile supply chain

In order to study the capacitated lot sizing problem for a supply chain of corporate multi-location factories to minimize the total costs of production, inventory and transportation under the system capacity restriction and product due date, while at the same time considering the menu distributed balance, the mathematical programming models are decomposed and reduced from the 3 levels into 2 levels according to the idea of just-in-time production. In order to overcome the premature convergence of ACA (ant colony algorithms), the idea of mute operation is adopted in genetic algorithms and a PACA (parallel ant colony algorithms) is proposed for supply chain optimization. Finally, an illustrative example is given, and a comparison is made with standard BAB (Branch and Bound) and PACA approach. The result shows that the latter is more effective and promising.

Potential-Decomposition Strategy in Markov Chain Monte Carlo Sampling Algorithms

We introduce the potential-decomposition strategy （PDS）, which can be used in Markov chain Monte Carlo sampling algorithms. PDS can be designed to make particles move in a modified potential that favors diffusion in phase space, then, by rejecting some trial samples, the target distributions can be sampled in an unbiased manner. Furthermore, if the accepted trial samples are insumcient, they can be recycled as initial states to form more unbiased samples. This strategy can greatly improve efficiency when the original potential has multiple metastable states separated by large barriers. We apply PDS to the 2d Ising model and a double-well potential model with a large barrier, demonstrating in these two representative examples that convergence is accelerated by orders of magnitude.

Assessing supply chain performance using genetic algorithm and support vector machine

The rough set-genetic support vector machine(SVM) model is applied to supply chain performance evaluation. First, the rough set theory is used to remove the redundant factors that affect the performance evaluation of supply chain to obtain the core influencing factors. Then the support vector machine is used to extract the core influencing factors to predict the level of supply chain performance. In the process of SVM classification, the genetic algorithm is used to optimize the parameters of the SVM algorithm to obtain the best parameter model, and then the supply chain performance evaluation level is predicted. Finally, an example is used to predict this model, and compared with the result of using only rough set-support vector machine to predict. The results show that the method of rough set-genetic support vector machine can predict the level of supply chain performance more accurately and the prediction result is more realistic, which is a scientific and feasible method.

A Global Reduction Based Algorithm for Computing Homology of Chain Complexes

In this paper, we propose a new algorithm to compute the homology of a finitely generated chain complex. Our method is based on grouping several reductions into structures that can be encoded as directed acyclic graphs. The organized reduction pairs lead to sequences of projection maps that reduce the number of generators while preserving the homology groups of the original chain complex. This sequencing of reduction pairs allows updating the boundary information in a single step for a whole set of reductions, which shows impressive gains in computational performance compared to existing methods. In addition, our method gives the homology generators for a small additional cost.

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

The evolution of the cold chain logistics vehicle routing problem:a bibliometric and visualization revie

This paper uses the bibliometric analysis software CiteSpace to examine the current status and evolution of cold-chain logistics vehicle routing problems(CCVRP).7381 relevant articles published in the Web of Science core collection from 2008 to 2024 were analyzed,an in-depth understanding of the publication trends and category distribution were gained.Subsequently,CiteSpace was used to create a scientific knowledge graph and perform visualization analysis.The analysis includes collaboration among authors,countries,and institutions;co-citation analysis of authors,journals,and references;citation burst detection of keywords;and co-citation cluster analysis of references.Based on a deep understanding of current research hotspots,an in-depth discussion of existing research was conducted from three perspectives:optimization objectives,distribution scenarios,and solution algorithms.The results show that CCVRP involves complex factors such as temperature requirements,time window constraints,and multi-objective optimization.These intricate constraints are causing research to become increasingly interdisciplinary and comprehensive.The evolution of hot topics shows that the research directions span multiple fields,from algorithm design to logistics management.This review helps researchers better understand the history,current status,and future development directions of CCVRP research,and provides valuable references and inspiration for academia and practice.

基于改进加权LeaderRank算法的公证人机制跨链的研究

公证人机制是基于信用背书节点的跨链机制。针对公证人背书节点信用评价单一问题,提出将加权LeaderRank算法运用到评价模型当中,通过收集节点历史交易评价信息计算出节点信用权值,参与信任度排序算法,得到安全可信的公证人节点,使得公证人机制更加稳定可信。研究结果表明,改进后的加权LeaderRank算法综合分析了节点历史交易评价信息和交易信任关系,对准确选取公证人节点、维护公证人机制安全可靠有重要意义。

决策学习型蜣螂优化算法的无人机协同路径规划

针对多无人机协同路径规划问题,提出了一种决策学习型蜣螂优化算法(DLDBO)。传统蜣螂优化算法(DBO)种群之间缺乏信息互换,容易陷入局部最优解。因此,利用Pearson相关系数计算个体之间的相似性,通过相似性指标判断并作出决策:若不相似,利用折射反向学习计算得到候选解,在一定程度上提高个体之间影响的同时增强算法跳出局部最优的能力;若相似,利用所提出的链式邻近学习引导蜣螂个体,增加影响个体更新的因素,充分促进个体之间的信息交流。在CEC2017测试套件的29个测试函数上进行了充分的对比实验,结果表明,DLDBO性能明显优于其他六种先进的变体算法。利用DLDBO规划无人机群的飞行路径,最终能够得到较为理想的协同路径并且有效避开威胁,优于其余三种优秀的协同路径规划算法,满足了无人机协同飞行的需求。

基于碳排放和软时间窗的冷链物流配送路径优化研究

针对冷链物流高时效性、高成本的特点,运输和冷藏过程中的碳排放成本,以及违反时间窗的惩罚成本等因素,构建以配送总成本最小化为目标的冷链物流配送路径模型,并采用改进的自适应遗传算法进行求解。通过实证分析,对运输路径和运输车辆进行决策,并针对优化前后的结果进行分析,验证了模型和算法的有效性。结果表明:优化后的总配送成本相较于优化前减少了16.55%,碳排放成本相较于优化前减少了2.22%,优化后的遗传算法在降低配送成本和碳排放成本上具有显著效果,可以通过合理安排配送路径及运输车辆等手段来降低配送成本和碳排放成本。

提高链式Lin-Kernighan算法性能的策略

Lin-Kernighan算法作为一种高效的组合优化问题优化算法,普遍应用于各种求解组合优化难题的算法中,尤其是旅行商问题的求解。通过对该类问题的可化简性论述,分析并建立了该类问题初始边集的概率化简模型,经实验分析方式确定了模型中的先验性概率值,并建立旅行商化简初始边集的随机算法。将该算法建立的边集作为链式Lin-Kernighan算法的参照优化边集,大幅度提高了链式Lin-Kernighan算法的求解性能,在与多种智能算法结合中取得了较好的收敛效果。