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Seagull Optimization Algorithm (SOA) is a metaheuristic algorithm that mimics the migrating and hunting behaviour of seagulls. SOA is able to solve continuous real-life problems, but not to discrete problems. The eight different binary versions of SOA are proposed in this paper. The proposed algorithm uses four transfer functions, S-shaped and V-shaped, which are used to map the continuous search space into discrete search space. Twenty-five benchmark functions are used to validate the performance of the proposed algorithm. The statistical significance of the proposed algorithm is also analysed. Experimental results divulge that the proposed algorithm outperforms the competitive algorithms. The proposed algorithm is also applied on data mining. The results demonstrate the superiority of binary seagull optimization algorithm in data mining application.

Fault diagnosis for a multiprocessor system is a process of identifying the faulty nodes in the system and is an important issue on the reliability of the system. As to the problem that there are few effective algorithms to diagnose faulty nodes in a given star network system in the literature, this paper proposes a precise fault diagnosis algorithm to identify faulty nodes in a star network system with a given syndrome under the comparison model. Such an algorithm contains three main parts. In the first part, we present an algorithm called Partition-Cycle for partitioning a cycle into sequences based on a given syndrome of the cycle. In the second part, we introduce an algorithm called Digout to diagnose these cycle sequences obtained the first part, which can diagnose each node in the cycle to be faulty or fault-free or unknown. In the third part, we design a diagnosis algorithm called Star-Digout to diagnose faulty nodes in an $n$ -dimensional ( $n\geqslant 6$ ) star networks, which is proved to contain a cycle that contains all nodes in the network and is not the same two nodes. Our theoretical analysis shows the time complexity of the diagnosis algorithm is $O(n!)$ . Our simulation results show that our algorithm is a precise diagnosis algorithm for a star network system.

It is common knowledge that edge disjoint paths have close relationship with the edge connectivity. Motivated by the well-known Menger theorem, we find that the maximum cardinality of edge disjoint paths connecting any two disjoint connected subgraphs with $g$ vertices in $G$ can also define by the minimum modified edge-cut, called the $g$ -extra edge-connectivity of $G~(\lambda _{g}(G))$ . It is the cardinality of the minimum set of edges in $G$ , if such a set exists, whose deletion disconnects $G$ and leaves every remaining component with at least $g$ vertices. The $n$ -dimensional augmented cube $AQ_{n}$ is a variant of hypercube $Q_{n}$ . In this paper, we observe that the $g$ -extra edge-connectivity of the augmented cube for some exponentially large enough $g$ exists a concentration behavior, for about 72.22 percent values of $g\leq 2^{n-1}$ , and that the $g$ -extra edge-connectivity of $AQ_{n}$ ( $n\geq 3$ ) concentrates on ${\lceil \frac {n}{2}\rceil }-1$ special values. Specifically, we prove that the exact value of $g$ -extra edge-connectivity of augmented cube is a constant $2\left({{\lceil \frac {n}{2}\rceil }-r}\right)2^{{\lfloor \frac {n}{2}\rfloor }+r}$ for each integer $2^{{\lfloor \frac {n}{2}\rfloor }+r}-l_{r}\leq g\leq 2^{{\lfloor \frac {n}{2}\rfloor }+r}$ , where $n\geq 3$ , $r=1, 2, \ldots, {\lceil \frac {n}{2}\rceil }-1$ and $l_{r}=\frac {2^{2r+1}-2}{3}$ if $n$ is odd and $l_{r}=\frac {2^{2r+2}-4}{3}$ if $n$ is even. The above upper and lower bounds of $g$ are sharp. Moreover, we also obtain the exponential edge disjoint paths in $AQ_{n}$ with edge faults.

In a graph $G$ , two spanning trees $T_{1}$ and $T_{2}$ are rooted at the same vertex $r$ . If, for every $v \in V(G)$ , the paths from $v$ to the root $r$ in $T_{1}$ and $T_{2}$ are internally vertex-disjoint, they are called independent spanning trees (ISTs). ISTs can be applied in numerous fields, such as fault-tolerant broadcasting and secure message distribution. The ( $n$ , $k$ )-star graphs $S_{n,k}$ constitute a generalized version of the star network. The method of constructing ISTs in ( $n$ , $k$ )-star graphs remains unknown. In this paper, we propose one recursive algorithm and one parallel algorithm for constructing ISTs on ( $n$ , $k$ )-star graphs. The main ideas of the recursive algorithm are to use induction to change small trees into large trees, to use a modified breadth-first search (MBFS) traversal to create a frame for an IST, and to use a breadth-first search (BFS) traversal to connect the rest of nodes. The main ideas of the parallel algorithm are to create frames through MBFS traversals in parallel, and to use some specific rules to connect the rest of nodes in parallel. We also present validation proofs for both algorithms, and analyze the time complexities of both algorithms. The time complexity of the recursive algorithm in $S_{n,k}$ is $O\left({n\times \frac {n!}{(n-k)!}}\right)$ , where $\frac {n!}{(n-k)!}$ is the number of nodes of $S_{n,k}$ . The time complexity of the parallel algorithm can be reduced to $O\left({\frac {n!}{(n-k)!}}\right)$ if the system has $n-1$ processors computing in parallel. Both algorithms are correct with the stated time complexity values; the parallel algorithm is more efficient than the recursive algorithm.

Spherical evolution is a recently proposed nature-inspired meta-heuristic algorithm which is proven to have nontrivial efficiency and effectiveness in solving complex optimization problems. However, it has some limitations caused by its inherent scale factor and dimension factor. Hypercube search and chaotic local search are two kinds of effective search mechanisms. To construct an algorithm which has better exploration and exploitation abilities, we propose a novel algorithm which contains triple distinct search dynamics (TDSD), i.e., spherical search, hypercube search and chaotic local search. Effective control among them enhances search performance of TDSD. It is verified on thirty CEC2017 benchmark functions and three real-world optimization problems.

The probability that a multiprocessor computer system has faults arises as the cardinality of the system grows. The subsystem reliability in a system, defined as the probability that there exists a fault-free subsystem of a specified cardinality when the system has faults. In this paper, we derive an upper bound and a lower bound on the probability of a (n-1)-dimensional subgraph being fault-free in a n-dimensional balanced hypercube under the probabilistic fault model. Numerical simulations indicate that these two analytical results we get are in good consistency, especially when the value of node reliability goes low.

In this work we investigate advanced stochastic methods for solving a specific multidimensional problem related to neural networks. Monte Carlo and quasi-Monte Carlo techniques have been developed over many years in a range of different fields, but have only recently been applied to the problems in neural networks. As well as providing a consistent framework for statistical pattern recognition, the stochastic approach offers a number of practical advantages including a solution to the problem for higher dimensions. For the first time multidimensional integrals up to 100 dimensions related to this area will be discussed in our numerical study.

The article considers the possibility of transition to the mechanism of remote customs control over the transport of international trade goods in the context of strengthening border controls in connection with the global pandemic. The analysis of the structure of regulatory documents required for border customs control is carried out. The concept of the “Single Document” and its information model have been developed using an information matrix in the form of a hypercube. The current and proposed methodology for managing databases of customs control at the border, based on a model of a multidimensional information matrix, has been analyzed. On the basis of the information model of the “Single Document”, an optimal algorithm for managing the databases of the unified automated information system of customs authorities has been developed and its effectiveness has been substantiated by the method of assessing the capacity of information matrices. As a result, the implementation of this algorithm reduced the amount of processed information by 28.5%.

This study is devoted to the problem of the ability to represent multidimensional design of building territory based on Online Analytical Processing (OLAP) technology. Building territory is viewed as complex information system, which relate with other information systems of surrounding areas. Mathematical modeling of such information system for multidimensional design based on OLAP technology is proposed to solve this problem. It is proposed to use a Online Transaction Processing (OLTP) system for multidimensional analysis and design of the two subject areas. The first subject area contains background information about the topography, geodesy and geology of the building territory. The second subject area contains information about the surrounding areas to the building territory. OLAP cubes represent multidimensional information objects with associated properties. Each OLTP system is an implementation of a domain model and is designed by a multidimensional matrix, which is represented as an OLAP cube. The application of this concept in a Web-based environment is covered.

In this article, a conceptual load flow model is designed with a MATLAB 2015a software. The proposed modelling system is mainly based on load distribution and power flow. This paper also examines the impact of higher load in a load flow distributed bus system. Further, the proposed research work explores the effect of damping minimization on steady-state time with respect to constant voltage amplitude. The developed hypercube architecture employs the ant colony optimization algorithm (ACO) to overcome the redistributing network losses.

In the past few years, edge-disjoint spanning trees (EDSTs) have attracted extensive attention due to their applications in reliable communication, fault-tolerant broadcasting, secure message distribution, etc. As one architecture of many interconnection networks, hypercubes (denoted as Q n ) play an important role in parallel computing systems, as well as their line graphs (denoted as L(Q n )), but few results of EDSTs in L(Q n ) are reported. In this paper, we establish the relation between EDSTs in Q n and EDSTs in L(Q n ), then we propose an algorithm to obtain the EDSTs in L(Q n ) and present the corresponding simulation experiment to verify its validity.

Exploratory Landscape Analysis is a methodology for transforming the samples of an optimization problem into numerical descriptors called landscape features. Since Exploratory Landscape Analysis is sample based, recent studies have shown that the choice of the method used to collect the problem samples can have an effect on the calculation of landscape features.In our recent work, we have shown that certain landscape features are not invariant to even simple function transformations such as shifting or scaling. However, the analysis in our previous work was conducted using only Latin Hypercube Sampling. Since we are now aware that the choice of sampling method can affect the calculation of landscape features, this paper expands upon our earlier work by using a variety of different sampling methods. We show that different sampling methods do indeed have an effect on the invariance of landscape features, and present a list of landscape features that are invariant under all of our chosen sampling methods.

Multidimensional torus topology plays a key role as a topology of the communication system of supercomputers and clusters as well as networks on chip. An infinite Petri net model of multidimensional torus communication grid with Moore neighborhood and combined cut-through and store-and-forward switching device has been constructed, its place invariance proven based on solving infinite linear Diophantine systems of equations in parametric form. For specifying Moore neighborhood, that traverses all hypercube bounds of lesser dimension, we use designation of the switching device ports by the coordinate difference. It is mentioned that the obtained results are applicable for modeling brain and processes of spreading insects and viruses.

For an integer ℓ ≥ 2, the ℓ-component connectivity of a graph G, denoted by κℓ(G), is the minimum number of vertices whose removal from G results in a disconnected graph with at least ℓ components or a graph with fewer than ℓ vertices. This is a natural generalization of the classical connectivity of graphs defined in term of the minimum vertex-cut and a good measure of vulnerability for the graph corresponding to a network. So far, the exact values of ℓ-connectivity are known only for a few classes of networks and small ℓ's. It has been pointed out in component connectivity of the hypercubes, International Journal of Computer Mathematics 89 (2012) 137-145] that determining ℓ-connectivity is still unsolved for most interconnection networks such as alternating group graphs and star graphs. In this paper, by exploring the combinatorial properties and the fault-tolerance of the alternating group graphs AGn and a variation of the star graphs called split-stars Sn2, we study their ℓ-component connectivities. We obtain the following results: 1) κ3(AGn) = 4n - 10 and κ4(AGn) = 6n - 16 for n ≥ 4, and κ5(AGn) = 8n - 24 for n ≥ 5 and 2) κ3(Sn2) = 4n - 8, κ4(Sn2) = 6n - 14, and κ5(Sn2) = 8n - 20 for n ≥ 4.

The dynamically modifying method is proposed to correct digital twin model online in real-time. Firstly, the digital twin model is divided into gradual-changing model and fast-changing model based on data changing ratio. The proximity of the position and the similarity of the shape is introduced to described the consistency of gradual-changing model and Empirical Mode Decomposition is depicted to decompose fast-changing model into trend item and stationary item, so that the consistency between physical entity and digital twin can be measured. Solution of parameters is acquired by searching instead of solving for the purpose of quick adjustment. Furthermore, a searching method combined Latin Hypercube global searching and greedy local searching is studied. The validity of the proposed method is proved by simulation.

Parallel processing architectures require suitable interconnection between its components. The type of interconnection used in the system is responsible for whole system performance. Realization of data transmission between processors or between processors and memory modules are possible through interconnection networks. Many popular interconnection networks such as Hypercube, Hyper-Mesh, 3D Hexagonal, 2D Torus, Ring, Binary Tree, Linear Array etc, have been introduced for parallel systems. Existing networks have some drawbacks, which are, increasing node degree, lower bisection width, large diameter, high network cost etc. Considering these factors, this research presents performance of Multilayer Octagon-Cell Interconnection Network. Multilayer Octagon-Cell (MLO) network is a new class of Octagon-Cell Network (OCN), which consists of identical OCNs connected together in hierarchical order. This network is expandable by increasing the depth of OCNs. The performance parameters of this network have been analyzed in contrast to other networks. These parameters have been proved through Lemmas in terms of depth of the network as well as number of nodes in the network. MLO network is scalable and recursive in nature. It has higher bisection width in comparison to OCN, Binary Tree, Linear Array, and Ring. MLO network has the diameter close to OCN and smaller in comparison to Ring and Linear Array. As this network has constant node degree, it connects more number of nodes with less number of links.

The proper performance of the permanent magnet synchronous motors leads them to be a potential candidate for various applications. In this regard, the optimum design of them will be important in such a way that the optimization algorithm can have a significant effect on the output characteristics of permanent magnet synchronous motors. Genetic Algorithm is one of well-known optimization tools where convergence and quality of optimum responses depends on its initial population. Therefore, in this paper, Latin Hypercube Sampling assisted Genetic Algorithm combined with Sensitivity analysis has been employed to extract multi-objective optimum design. Finite element modeling results has shown a better electromagnetic performance has been achieved in optimized design comparing to initial one while magnet cost has been greatly reduced.

Broadcasting is the process of dissemination of information in a network where a particular message is sent starting from an originator. The ultimate objective is to inform all entities of the network as soon as possible. Besides, the theory of broadcasting has been used in a wide range of applications. Although the problem has been proven to be NP-Complete for an arbitrary graph, it is tractable for a few families of networks, such as Hypercubes, one of the most successful large-scale parallel architectures. In this paper, we propose a new heuristic for broadcasting in a hypercube of trees in which each vertex of the hypercube could be the root of a tree. Not only does this heuristic have the same approximation ratio as the best-known algorithm and provide a good theoretical bound, but our numerical results also show its superiority in most experiments. Our heuristic is able to outperform the current upper bound in up to 90% of the situations, resulting in an average speedup of 30%. Most importantly, our results illustrate that it can maintain its performance even if the size of the network grows, which makes the proposed heuristic practically useful.

Blockchain is a distributed ledger technology operating on top of the peer-to-peer (P2P) network, and the variety of Blockchain deployments have been observed in both public and private manners. In Blockchain, the information dissemination among Blockchain nodes relies on the one-to-all-the-others broadcast mechanism over the P2P network. The underlying network topology introduces a significant impact on the performance of information dissemination in the Blockchain P2P network as well as the effectiveness and stability of Blockchain operation as a consequence. When a private blockchain is deployed in a controlled environment, it is crucial to understand the effect of underlying network topology on Blockchain performance and operational effectiveness. This paper simulates various well-known network topologies, including Hypercube, Torus, Fat-Tree, and Scale-Free as an underlying network of the Blockchain, giving more focus on controlled environments such as Data Center Network (DCN). We comparatively evaluate the performance of each network topology based on both Blockchain and network aspects. The evaluation results show that Hypercube performs the best among the topologies being assessed, especially in information propagation delay and resilience against adverse situations.

The stability of power systems is affected by the parameters of equipment. This paper analyzes the small-signal stability region (SSSR), which is comprised of all admissible control parameters that guarantee system stability. First, the robust stability with uncertain parameters varying in a hypercube is determined through the spectral radius of Kronecker matrix. Then, an adaptive space searching algorithm is proposed to cover the SSSR via the union of verified hypercubes. Case studies on the single machine infinite bus system shows the SSSR of different parameters. The proposed method provides useful information for system design.

The locally twisted cube LTQ n is a variation of the hypercube Q n , and the diameter of LTQ n is only about half of the diameter of Q n . For interconnection networks, some efficient communication algorithms can be designed based on the ring structure. In addition, two edge-disjoint Hamiltonian cycles also provide the edge-fault tolerant Hamiltonicity for the interconnection network. Hung [Theoretical Computer Science 412, 4747–4753, 2011] designed an O(n2n) time algorithm to construct two edge-disjoint Hamiltonian cycles in LTQ n . In this paper, we provide parallel algorithms for each vertex in LTQ n to determine which two edges were adopted in the first or second Hamiltonian cycles, respectively. By connecting these edges, we can construct two edge-disjoint Hamiltonian cycles in LTQ n where n ≥ 4.

Snapshot hyperspectral imaging (HSI) systems are rapid and ultra-compact making them potential candidate for real-time food analysis. However, the existing technology limits the working wavelength range of these cameras requiring multiple cameras to cover a wider spectral range. We present a rapid hypercube stitching method which generates an efficiently stitched hypercube from two different HSI cameras providing a wider spectral range as well as spatial information. It shows reliability and robustness over the manual stitching. The method was able to successfully stitch respective hypercubes from near-infrared (NIR) and visible (Vis) cameras producing much lower number of non-overlapping pixels between the hypercubes then would be possible with manual stitching. We demonstrate the application of our method for stitching the hypercubes (NIR and Vis) for 32 beef samples analyzing the stitching efficiency and reliability of spectral information.

This paper deals with the strategic level of emergency health care management. Its main research topic consists in searching suitable deployment of service centers, from which the ambulance vehicles are sent to the demand points in case of emergency. Such problem can be solved by finding the optimal or at least a good enough solution of the weighted p-median problem. Due to growing number of applications of the location problems, several exact and heuristic methods have been recently developed. In this paper, we introduce a new strategy of heuristic solving method for the generalized min-sum p-location problem using a uniformly deployed set of p-median problem solutions. The set of solutions represents fixed points of a spider's network. The center of the network is determined by subsequent inspection of the shortest path connecting the current best-found solution and one of the fixed points. The inner nodes of the spider's network are obtained by inspections of paths, which connect neighboring fixed points. When series of the inner network nodes is finished, the connections of the general best-found solution and the inner nodes are inspected to improve the general best-found solution. Then a new series of inner nodes is created by inspecting the links between the neighboring current inner nodes. The process continues until some termination rule is met. The theoretical explanation is here accompanied by a brief computational study, the aim of which is to study its efficiency measured by computational time and the resulting solution accuracy.

With the increasing penetration of wind power generation, the operation reliability of the power system would be significantly impacted due to the uncertainty and volatility of wind generation. Therefore, this paper proposes a method of rapid power system operation risk assessment considering the correlation of wind generation. Firstly, a wind power scenario-constructing approach is presented. The proposed approach employs the Latin hypercube sampling to construct wind power generation scenarios while considering the correlation of wind generation. Then, the risk-oriented benefit function is adopted to evaluate the risk indices of the constructed scenarios. In order to improve the computation performance, a searching method based on the steady-state security region is applied to avoid redundant calculation of a large number of secure scenarios. The method is validated on a modified IEEE 30-bus system, and the simulation results verify that the proposed method is feasible and applicable. The computational performance of the method is also examined.

With the continued growth of electricity demand, the power system is trending towards extreme operation increasingly. In order to analyze the impact of load changes on power system operating risks, a novel power system security risk assessment algorithm is proposed based on Latin hypercube sampling and daily peak load forecasting. Firstly, the gated recurrent neural network combining dynamic time warping is applied to forecast the daily peak load. Then a Markov-based component failure model is constructed and the component state is determined by Latin hypercube sampling. Finally, the optimal load reduction and security risk of the power system is calculated. The simulation results show that, compared to Monte Carlo sampling, Latin Hypercube sampling requires fewer samples to achieve the same accuracy. Furthermore, it can be found by observing the fluctuation of power system security risk and daily peak load that their trend of changing is basically the similar but the amplitude is different. When the load level is low, there is a “compression” effect on the power system risk changes, and when the load level is high, there is a “stretch” effect. Therefore, accurate load forecasting can determine the risk trend of the system in advance, which is beneficial to the safe and stable operation of the power system.

An optimization search algorithm for multivariate systems is proposed. The proposed optimization search algorithm includes initialization-, displacement-shrink- and searching areas stages. The initialization stage generates initial solutions in the search area that represented by hypercube and then evaluates the function inside this hypercube; the displacement-shrink stage calculates the displacement and updates the parameters of the hypercube; the searching areas stage using certain rules find the next hypercube. The design stages of the proposed hypercube optimization search (HOS) algorithm are presented. The proposed HOS algorithm is tested on specific benchmark functions. The experimental results on different test functions demonstrate that the HOS algorithm has shown to be a promising approach for finding the solutions of a set of optimization problems.

A decision-making problem of reasonability of upgrading a computer network by adding new connections between nodes is investigated. As a basic topology, a well-known hypercube topology is chosen. The expediency of its upgrading by links corresponding to edge sets of hypercube cuts by parallel hyperplanes is investigated. It is done by graph-theoretic approaches and tolls of polyhedral combinatorics. In the course of solving the problem, key structural characteristics of a new graph Q'n, which is a complemented by edges the hypercube graph, are studied. The offered methodology can be applied to other problems of optimization of computer networks, aiming to improve their reliability and efficiency.

In order to improve the computational efficiency of structural reliability analysis under random and interval uncertainties, a hybrid sampling method is proposed in this paper. The basic idea of the proposed method is to integrate the Bucher experimental design and Latin hypercube sampling (LHS). Firstly, the sampling of random variables and the sampling of interval variables are respectively conducted according to Bucher experimental design and LHS. Then each random sample will be combined with each interval sample to form the new sample point. One e example is used to demonstrate the effectiveness of the proposed sampling method.

The paper deals with the issues concerning the expressive power of bithreshold neurons. We study how many Boolean functions of n variables can be computed by using bithreshold neural units. Our approach is based on the application of combinatorial methods in the theory of neural computation. The lower and upper bounds on the number of bithreshold functions are established in the paper whose growth orders have the equal leading terms. We also demonstrate the relation between the representational capability of threshold and bithreshold neurons.

This study of article presents a novel optimization search algorithm to solve the examination timetabling problems. These are discrete, multi-objective and combinatorial optimization problem which is generally solved using stochastic search techniques as evolutionary algorithms (EAs) and heuristic technique. In this study, the purposed optimization search algorithm is to give a brief introduction to examination timetabling problems that have been solved using Hypercube Optimization Search (HOS) algorithm which is a new intense stochastic search technique that is based on a hypercube evolution. The proposed method is applied to solve a timetabling problem which is hard to solve for many institutions of higher education and as a result this approach has shown good performance when the proposed method is compared with other performances of existing approaches.

Fault diagnosis of systems is an important aspect of study in the design and maintenance of multiprocessor systems. In order to improve diagnostic ability and strengthen reliability, Fabrega et al. introduced extra connectivity and extra edge-connectivity, which are two important parameters in studying the reliability of multiprocessor systems. The balanced hypercube, denoted by where, is a variant of hypercube network, which has some specific topological properties. This paper studied the reliability of balanced hypercube, and determines its h-extra connectivity and the h-extra edge-connectivity for . Through the above research results, it has very important theoretical value and practical significance for the application and promotion of balanced hypercube in multiprocessor system.

In recent years, the Big Data challenge has attracted increasing attention. To help shield application programmers from the complexity of distribution, many distributed computation frameworks have been proposed in a cloud environment. The parallel interconnection networks play a vital role in big data systems. The Double Star (DS) network is newly suggested reliable interconnection topology for future systems to be employed in high speed parallel computing domain. The proposed system is derived from n-Star, one of the robust systems for advanced networks. In this paper we try to develop an algorithm for load balancing in DS. The Clustered Dimension Exchange Method (CDEM) is at the bottom of the proposed algorithm which balances each processing element of the DS network.

Recently, meta-heuristic algorithms have been researched in quantity. However, there are no algorithms applying to different problems effectively and having notable difference compared with other meta-heuristic algorithm. Evolutionary algorithms are classical algorithms of nature-inspired metaheuristic algorithms. Spherical evolution (SE) is one of metaheuristic algorithms proposed recently. SE uses a spherical search style rather than the traditional hypercube search style. Differential evolution (DE) is a classical algorithm of hypercube search style. In this paper, considering the strong exploration capability of SE and the powerful exploitation ability of DE, we synthesize two search style and propose a hybrid algorithm, and use archive to reuse the good individuals. The proposed algorithm exhibits better performance in comparison with other state-of-the-art algorithms in terms of robustness and effectiveness based on 30 benchmark functions of IEEE CEC2017.

A metaheuristic algorithm called spherical evolution (SE) is proposed recently, and the SE innovatively adopts a novel spherical search mechanism instead of the conventional hypercube search mechanism. SE has shown superior performance over other metaheuristic algorithms. However, it still suffers from low search performance and low convergence speed. In this paper, we for the first time propose a hybrid spherical evolution and particle swarm optimization algorithm aimed to leverage strengths of two different search mechanism in a hybrid algorithm, and design a search mechanism control rule based on the fitness of individuals. Experimental results based on 30 benchmark functions of IEEE CEC2017 and results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.

Topology characteristics of multicomputer systems and clusters determine the complexity of the routing algorithm and the time of data exchange. Providing a minimum degree and diameter (minimum cost) and maximum fault tolerance is also desirable. Cartesian production of the hypercube and de Bruijn topology were performed. This topology provides simple routing, high fault-tolerance and scalability. Also, diameter of this topology increases logarithmically. This article discusses how to improve this topology by using excess de Bruijn. The method of topology synthesis based on hyper de Bruijn topology with excess code has been proposed. Routing methods for this system are considered. Comparison with the classic solution, hypercube and de Bruijn topology and excess de Bruijn is presented.

In recent two decades, nature-inspired metaheuristic algorithms have been paid more and more attention. Although many new algorithms have been proposed, there is no quintessential difference among classic metaheuristic algorithms. Therefore, some researchers have focused on the essence of search operators in these optimizers. Spherical Evolution (SE) is one of the recent studies on the search style in metaheuristic algorithms. Contrary to other algorithms, SE adopts a spherical search instead of a hypercube search. In this paper, we focus on the advantages of the two search styles, We propose a hybrid optimizer based on the two complementary search styles, and design a rule to control their utilization. Experimental results based on 30 benchmark functions of CEC2017 show that the proposed optimizer outperforms other state-of-the-art algorithms in terms of effectiveness and robustness.

We propose MultiStream, a novel multi-way join operator that optimizes tuple-routing schemes across materialized relations and intermediate results to compute the join results. It allows trading off materialization effort vs. network utilization, which is essential to tailoring the query evaluation to the quantity and power of available compute resources. This goes far beyond the capabilities of more rigid schemes like the HyperCube technique or the BiStream approach that is limited to simple binary plans only. Around MultiStream, we have implemented CLASH, a high-level abstraction on top of Apache Storm, allowing users to phrase declarative queries, which are then optimized and translated into executable Storm topologies. Experiments on Amazon’s EC2, using TPC-H and an additional synthetic benchmark, underpin the versatility of the operator model and CLASH’S ability to outperform state-of-the-art competitors.

Conditional diagnosability is widely accepted as an important measure in determining the reliability of an interconnection network. The conditional diagnosability of many well-known interconnection networks has been investigated. Exchanged crossed cube(ECQ(s,t)) is a novel variant of hypercube, which retains the advantages of exchanged hypercube and crossed cube in terms of the smaller diameter, fewer links, and lower cost factor, and indicates more balanced consideration among performance and cost. In this paper, several topological properties of ECQ(s,t) are derived. On this basis, the conditional diagnosability of ECQ(s,t) under the PMC model is shown to be $4(s-1)+1$ for $t\ge s>2$ , which is almost two times larger than its classical diagnosability and also is larger than its conditional diagnosability under the MM model.