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The GEO (Golden Eagle Optimizer) is a population based meta heuristic algorithm that simulates the cooperative hunting behavior of golden eagles. In view of the problem of poor solution accuracy and local optima traps in the GEO algorithm, this study proposes an improved MERGEO (Mapped Elitist Reverse GEO) algorithm. Based on the original algorithm, symmetric mapping search strategy, adaptive elite strategy and random backward learning mechanism, are used to balance the exploration and development stages of the algorithm, and obtain the ability to avoid local optimal and better optimization accuracy. The independent strategy effectiveness analysis, scalability analysis and optimization performance comparison with other algorithms are carried out on 10 benchmark test functions. The experimental results show that the improved MERGEO algorithm has strong competitiveness and good optimization ability. The improved algorithm is applied to the coverage optimization problem of wireless sensor networks and pressure vessel design problem, which verifies the practical application value of improved algorithm. [ABSTRACT FROM AUTHOR]

Big Data analytics performance is critical in the dynamic world of Industry 5.0, where human engagement with cutting-edge technology is essential. Based on a comparison experiment, this empirical research highlights the significance of optimal data processing algorithms by providing important insights into the relationship between data amount and processing speed. The requirement of resource-intensive demands for efficient resource allocation and optimization in Industry 5.0 operations is emphasized. Operation C's exceptional performance in terms of mistake rates, data correctness, and processing quality highlights the need of careful data management procedures. As Industry 5.0 develops, scalability becomes more important. Operation C is a perfect example of how to adapt to higher data volumes. The way forward for an industrial future that is more responsive, sustainable, and efficient is shaped by this study.

In this article we construct parallel solvers analyze the efficiency and accuracy of general parallel solvers for three dimensional parabolic problems with the fractional power of elliptic operators. The proposed discrete method are targeted for general non-constant elliptic operators, the second motivation for the usage of such schemes arises when non-uniform space meshes are essential. Parallel solvers are required to solve the obtained large size systems of linear equations. The detailed scalability analysis is done in order to compare the efficiency of prposed parallel algorithms. Results of computational experiments are presented and analyzed.

WSN can be a tech in-full development with top-notch, outstanding foreseeable future. The utilization of wireless sensor network (WSN) to get a good quality observation is consists using some range of detector nodes having a network capacity which might be set up for productive effects or to get constant monitoring function. Water is critical within our own lives because of our wellbeing insurance and in different cases such as farming, marketplace, and for the animal's presence. Diseases linked to inadequate sanitation and water states have over 200 million cases reported causing 5–10 million deaths throughout the world. Water tracking procedures are indispensable to restrain exactly the challenge is gently linked to inadequate household allocation, wasteful usage and too little decent. Wireless sensor networks also have gained fame over the study area only because they supply a promising infrastructure for both multiple monitoring and control software. For fixing the dilemma, various drinking water caliber processes were grown previously in the last ten years. The parameters included with the drinking water quality tracking would be the PH degree, turbidity, dissolved oxygen and temperature has been quantified at the true period from the detectors that ship the info into the bottom channel or control/monitoring space. This document suggests the way the tracking platform might be set up highlighting the elements of low-cost, simple setup, and effortless maintenance and handling. Using this radio platform for tracking purpose is not only going to lower the total observation procedure expenditure in the duration of centers installation and labor price tag but can even offer versatility in the duration of location or distance. In this paper, the essential layout and execution of WSN comprising Lora (wide area network) established technological innovation is suggested. The developed system is cheap and makes it possible for customization. A few preliminary links between dimension to rate the efficiency and efficiency of the machine can also be shown. [ABSTRACT FROM AUTHOR]

Information and Communication Technology (ICT) infrastructures are at the heart of emerging Smart Grid scenarios with high penetration of Distributed Energy Resources (DER). The scalability of such ICT infrastructures is a key factor for the large scale deployment of the aforementioned Smart Grid solutions, which could not be ensured by small-scale pilot demonstrations. This paper presents a novel methodology that has been developed in the scope of the H2020 project InteGrid, which enables the scalability analysis of ICT infrastructures for Smart Grids. It is based on the Smart Grid Architecture Model (SGAM) framework, which enables a standardized and replicable approach. This approach consists of two consecutive steps: a qualitative analysis that aims at identifying potential bottlenecks in an ICT infrastructure; and a quantitative analysis of the identified critical links under stress conditions by means of simulations with the aim of evaluating their operational limits. In this work the proposed methodology is applied to a cluster of solutions demonstrated in the InteGrid Slovenian pilot. This pilot consists of a Large Customer Commercial Virtual Power Plant (VPP) that provides flexibility in medium voltage for tertiary reserve and a Traffic Light System (TLS) to validate such flexibility offers. This approach creates an indirect Transmission System Operator (TSO)—Distribution System Operator (DSO) coordination scheme.

Chaotic Sine-Cosine Algorithms (CSCAs) are new metaheuristic optimization algorithms. However, Chaotic Sine-Cosine Algorithm (CSCAs) are able to manipulate the problems in the standard Sine-Cosine Algorithm (SCA) like, slow convergence rate and falling into local solutions. This manipulation is done by changing the random parameters in the standard Sine-Cosine Algorithm (SCA) with the chaotic sequences. To verify the ability of the Chaotic Sine-Cosine Algorithms (CSCAs) for solving problems with large scale problems. The behaviors of the Chaotic Sine-Cosine Algorithms (CSCAs) were studied under different dimensions 10, 30, 100, and 200. The results show the high quality solutions and the superiority of all Chaotic Sine-Cosine Algorithms (CSCAs) on the standard SCA algorithm for all selecting dimensions. Additionally, different initial values of the chaotic maps are used to study the sensitivity of Chaotic Sine-Cosine Algorithms (CSCAs). The sensitivity test reveals that the initial value 0.7 is the best option for all Chaotic Sine-Cosine Algorithms (CSCAs). [ABSTRACT FROM AUTHOR]

In this study, an extensive numerical analysis is carried out to investigate the effects of different quantum-based operators on the performance of continuous quantum-inspired evolutionary algorithms (QEAs). In this context, different variants of quantum-inspired evolutionary operators are adopted for numerical simulations. Furthermore, some novel chaos-enhanced QEAs are proposed and their performances are evaluated through the numerical comparative study. Based on evaluating the accuracy, robustness, convergence, scalability and sensitivity to initialisation of the rival methods, it is indicated that the algorithmic structure of QEAs is prone to being combined with chaotic maps. The results demonstrate that chaotically implemented QEAs can effectively explore/exploit the solution spaces of different landscapes and dimensionality, and finally, converge to acceptable regions within the solution domain. [ABSTRACT FROM AUTHOR]

Highlights: [•] We have addressed the Software Scheduling Problem (SPS), which is of outmost importance for current software companies as the software projects are becoming very large. [•] A scalability analysis of eight multi-objective metaheuristics (NSGA-II, SPEA2, PAES, DE-PT, MO-GA, MOABC, MOCell, GDE3) is performed over a set of SPS instances with up to 256 employees and 512 tasks. [•] PAES has shown to be the most scalable algorithm: it is able to provide the project manager with the more diverse project schedules. [ABSTRACT FROM AUTHOR]

In this paper we analyse the scalability of seven multiobjective evolutionary algorithms when they solve large instances of a known biological problem, the motif discovery problem (MDP). The selected algorithms are a population-based and a trajectory-based algorithms (DEPT and MO-VNS, respectively), three swarm intelligence algorithms (MOABC, MO-FA, and MO-GSA), a genetic algorithm (NSGA-II), and SPEA2. The MDP is one of the most important sequence analysis problems related to discover common patterns, motifs, in DNA sequences. A motif is a nucleic acid sequence pattern that has some biological significance as being DNA binding sites for a regulatory protein, i.e., a transcription factor (TF). A biologically relevant motif must have a certain length, be found in many sequences, and present a high similarity among the subsequences which compose it. These three goals are in conflict with each other, therefore a multiobjective approach is a good way of facing the MDP. In addition, in recent years, scientists are decoding genomes of many organisms, increasing the computational workload of the algorithms. Therefore, we need algorithms that are able to deal with these new large DNA instances. The obtained experimental results suggest that MOABC and MO-FA are the algorithms with the best scalability behaviours. [ABSTRACT FROM AUTHOR]

This paper presents a new formulation of the isoefficiency function which can be applied to parallel systems executing balanced or unbalanced workloads. This new formulation allows analyzing the scalability of parallel systems under either balanced or unbalanced workloads. Finally, the validity of this new metric is evaluated using some synthetic benchmarks. The experimental results allow assessing the importance of considering the unbalanced workloads while analyzing the scalability of parallel systems. [ABSTRACT FROM AUTHOR]

Abstract: Recent developments in the analysis of large Markov models facilitate the fast approximation of transient characteristics of the underlying stochastic process. Fluid analysis makes it possible to consider previously intractable models whose underlying discrete state space grows exponentially as model components are added. In this work, we show how fluid-approximation techniques may be used to extract passage-time measures from performance models. We focus on two types of passage measure: passage times involving individual components, as well as passage times which capture the time taken for a population of components to evolve. Specifically, we show that for models of sufficient scale, global passage-time distributions can be well approximated by a deterministic fluid-derived passage-time measure. Where models are not of sufficient scale, we are able to generate upper and lower approximations for the entire cumulative distribution function of these passage-time random variables, using moment-based techniques. Additionally, we show that, for passage-time measures involving individual components, the cumulative distribution function can be directly approximated by fluid techniques. Finally, using the GPA tool, we take advantage of the rapid fluid computation of passage times to show how a multi-class client–server system can be optimised to satisfy multiple service level agreements. [Copyright &y& Elsevier]

This paper presents a new expression for an isoefficiency function which can be applied both to homogeneous and heterogeneous systems. Using this new function, called H-isoefficiency, it is now possible to analyze the scalability of heterogeneous clusters. In order to show how this new metric can be used, a theoretical a priori analysis of the scalability of a Gauss Elimination algorithm is presented, together with a model evaluation which demonstrates the correlation between the theoretical analysis and the experimental results. [ABSTRACT FROM AUTHOR]

We present MOPED, a framework for Multiple Object Pose Estimation and Detection that seamlessly integrates single-image and multi-image object recognition and pose estimation in one optimized, robust, and scalable framework. We address two main challenges in computer vision for robotics: robust performance in complex scenes, and low latency for real-time operation. We achieve robust performance with Iterative Clustering Estimation (ICE), a novel algorithm that iteratively combines feature clustering with robust pose estimation. Feature clustering quickly partitions the scene and produces object hypotheses. The hypotheses are used to further refine the feature clusters, and the two steps iterate until convergence. ICE is easy to parallelize, and easily integrates single- and multi-camera object recognition and pose estimation. We also introduce a novel object hypothesis scoring function based on M-estimator theory, and a novel pose clustering algorithm that robustly handles recognition outliers. We achieve scalability and low latency with an improved feature matching algorithm for large databases, a GPU/CPU hybrid architecture that exploits parallelism at all levels, and an optimized resource scheduler. We provide extensive experimental results demonstrating state-of-the-art performance in terms of recognition, scalability, and latency in real-world robotic applications. [ABSTRACT FROM AUTHOR]

This paper studies the scalability of an Evolutionary Algorithm (EA) whose population is structured by means of a gossiping protocol and where the evolutionary operators act exclusively within the local neighborhoods. This makes the algorithm inherently suited for parallel execution in a peer-to-peer fashion which, in turn, offers great advantages when dealing with computationally expensive problems because distributed execution implies massive scalability. In this paper we show another advantage of this algorithm: We experimentally demonstrate that it scales up better than traditional alternatives even when executed in a sequential fashion. In particular, we analyze the behavior of several EAs on well-known deceptive trap functions with varying sizes and levels of deceptiveness. The results show that the new EA requires smaller optimal population sizes and fewer fitness evaluations to reach solutions. The relative advantage of the new EA is more outstanding as problem hardness and size increase. In some cases the new algorithm reduces the computational efforts of the traditional EAs by several orders of magnitude. [ABSTRACT FROM AUTHOR]

Abstract: OpenMP is a successful approach to writing threaded parallel applications. This article describes the state of the art in performance profiling OpenMP applications, covering vendor performance tools and platform independent techniques. The features of the OpenMP profiler ompP are described in detail and an outlook of future directions in this area is given. [ABSTRACT FROM AUTHOR]

The article presents the second part of the study that examines the performance of a massively parallel version of DIRECT, a global search algorithm that has been implemented in multidisciplinary design optimization applications. It cites the comparison between the theoretical and experimental results for two parallel clusters having different system scales and network connectivities. It explores the effect of parallel system parameters, which include the objective function cost Tf, on the performance of the search algorithm on large-scale clusters. A scalability analysis which has been based on isoefficiency is also provided.

The article presents the first part of the study that examines the performance of a massively parallel version of DIRECT, which is a global search algorithm that has been employed in multidisciplinary design optimization applications. It explores the design issues for the parallel version of DIRECT. It discusses the parallel design with the overall scheme and details of the high-level implementation. It also examines the performance sensitivity to important parameters used for problem configurations, parallel schemes, as well as system settings.

Abstract: Bag-of-Tasks applications are parallel applications composed of independent tasks. Examples of Bag-of-Tasks (BoT) applications include Monte Carlo simulations, massive searches (such as key breaking), image manipulation applications and data mining algorithms. This paper analyzes the scalability of Bag-of-Tasks applications running on master–slave platforms and proposes a scalability-related measure dubbed input file affinity. In this work, we also illustrate how the input file affinity, which is a characteristic of an application, can be used to improve the scalability of Bag-of-Tasks applications running on master–slave platforms. The input file affinity was considered in a new scheduling algorithm dubbed Dynamic Clustering, which is oblivious to task execution times. We compare the scalability of the Dynamic Clustering algorithm to several other algorithms, oblivious and non-oblivious to task execution times, proposed in the literature. We show in this paper that, in several situations, the oblivious algorithm Dynamic Clustering has scalability performance comparable to non-oblivious algorithms, which is remarkable considering that our oblivious algorithm uses much less information to schedule tasks. [Copyright &y& Elsevier]

Scalability has become an attribute of paramount importance for computer systems used in business, scientific and engineering applications. Although scalability has been widely discussed, especially for pure parallel computer systems, it conveniently focuses on improving performance when increasing the number of computing processors. In fact, the term “scalable” is so much abused that it has become a marketing tool for computer vendors independent of the system’s technical qualifications. Since the primary objective of scalability analysis is to determine how well a system can work on larger problems with an increase in its size, we introduce here a generic definition of scalability. For illustrative purposes only, we apply this definition to PC clusters, a rather difficult subject due to their long communication latencies. Since scalability does not solely depend on the system architecture but also on the application programs and their actual management by the run-time environment, for the sake of illustration, we evaluate scalability for programs developed under the super-programming model (SPM) (Jin and Ziavras in IEEE Trans. Parallel Distrib. Syst. 15(9):783–794, ; J. Parallel Distrib. Comput. 65(10):1281–1289, ; IEICE Trans. Inf. Syst. E87-D(7):1774–1781, ). [ABSTRACT FROM AUTHOR]

We analyze the utility and scalability of extended compact genetic algorithm (eCGA) - a genetic algorithm (GA) that automatically and adaptively mines the regularities of the fitness landscape using machine learning methods and information theoretic measures - for ground state optimization of clusters. In order to reduce the computational time requirements while retaining the high reliability of predicting near-optimal structures, we employ two efficiency-enhancement techniques: (1) hybridizing eCGA with a local search method, and (2) seeding the initial population with lowest energy structures of a smaller cluster. The proposed method is exemplified by optimizing silicon clusters with 4-20 atoms. The results indicate that the population size required to obtain near-optimal solutions with 98% probability scales sub linearly (as Θ(n0.83)) with the cluster size. The total number of function evaluations (cluster energy calculations) scales sub-cubically (as Θ(n2.45)), which is a significant improvement over exponential scaling of poorly designed evolutionary algorithms. [ABSTRACT FROM AUTHOR]

The article scrutinizes the proposed new parallel solver for the large-scale 3D neutron transport problems enforced in nuclear reactor simulations. The study evaluates the direct performance of the large-scale computational models, highlighting the limitation of varied numerical acceleration techniques when applied to heterogeneous geometry. This particularly gives emphasis on the intensive and impractical application of large heterogeneous reactor for routine engineering. Moreover, the study initiates the use of analytical model for the determination of specific performance on different kind of architectures. Furthermore, information that discloses the scalability and portability of the parallel solver is posted.

The conjugate gradient method is an iterative technique used to solve systems of linear equations. The paper analyzes the performance of parallel preconditioned conjugate gradient algorithms. First, a theoretical model is proposed for estimation of the complexity of PPCG method and a scalability analysis is done for three different data decomposition cases. Computational experiments are done on IBM SP4 computer and some results are presented. It is shown that theoretical predictions agree well with computational results. [ABSTRACT FROM AUTHOR]

Trusted Grid computing demands robust resource allocation with security assurance at all resource sites. Large-scale Grid applications are being hindered by lack of security assurance from remote resource sites. We developed a security-binding scheme through site reputation assessment and trust integration across Grid sites. We do not treat the trust factor deterministically. Instead, we apply fuzzy theory to handle the fuzziness or uncertainties behind all trust attributes. The binding is achieved by periodic exchange of site security information and matchmaking to satisfy user job demands. PKI-based trust model supports Grids in multi-site authentication and single sign-on operations. However, cross certificates are inadequate to assess local security conditions at Grid sites. We propose a new fuzzy-logic trust model for distributed trust aggregation through fuzzification and integration of security attributes. We introduce the trust index of a Grid site, which is determined by site reputation from its track record and self-defense capability attributed to the risk conditions and hardware and software defenses deployed at a Grid site. A Secure Grid Outsourcing (SeGO) system is designed for secure scheduling a large number of autonomous and indivisible jobs to Grid sites. Significant performance gains are observed after trust aggregation, which is evaluated by running scalable NAS and PSA workloads over simulated Grids. Our security-binding scheme scales well with increasing user jobs and Grid sites. The new scheme can guide the security upgrade of Grid sites and predict the Grid performance of large workloads under risky conditions. [ABSTRACT FROM AUTHOR]

This paper presents distributed algorithms for both radial and looped load flows for unbalanced, multi-phase power distribution systems. The distributed algorithms are developed from tree-based sequential algorithms. Formulas of scalability for the distributed algorithms are presented. It is shown that computation time dominates communication time in the distributed computing model. This provides benefits to real-time load flow calculations, network reconfigurations, and optimization studies that rely on load flow calculations. Also, test results match the predictions of derived formulas. This shows the formulas can be used to predict the computation time when additional processors are involved. [Copyright &y& Elsevier]