Showing papers in "International Journal of Parallel, Emergent and Distributed Systems in 2018"
TL;DR: In this paper, the authors discuss principle ideas of quantum cognition research program, which comprise elements of the formalism of quantum mechanics (mainly Hilbert space theory and quantization theory).
Abstract: The purpose of this article is to discuss principle ideas of quantum cognition research program, which comprise elements of the formalism of quantum mechanics (mainly Hilbert space theory and quant...
55 citations
TL;DR: In this article, a multi-objective ant colony system algorithm for virtual machine (VM) consolidation in cloud data centers is presented. And the proposed algorithm builds VM migration plans, which are then used to minimise over-provisioning of physical machines (PMs) by consolidating VMs on under-utilised PMs.
Abstract: In this paper, we present a novel multi-objective ant colony system algorithm for virtual machine (VM) consolidation in cloud data centres. The proposed algorithm builds VM migration plans, which are then used to minimise over-provisioning of physical machines (PMs) by consolidating VMs on under-utilised PMs. It optimises two objectives that are ordered by their importance. The first and foremost objective in the proposed algorithm is to maximise the number of released PMs. Moreover, since VM migration is a resource-intensive operation, it also tries to minimise the number of VM migrations. The proposed algorithm is empirically evaluated in a series of experiments. The experimental results show that the proposed algorithm provides an efficient solution for VM consolidation in cloud data centres. Moreover, it outperforms two existing ant colony optimization-based VM consolidation algorithms in terms of number of released PMs and number of VM migrations.
52 citations
TL;DR: In this paper, two constraint handling approaches for an emerging metaheuristic of Cohort Intelligence (CI) are proposed, i.e., CI with static penalty function approach (SCI) and CI with dynamic penalty function (DCI).
Abstract: Most of the metaheuristics can efficiently solve unconstrained problems; however, their performance may degenerate if the constraints are involved. This paper proposes two constraint handling approaches for an emerging metaheuristic of Cohort Intelligence (CI). More specifically CI with static penalty function approach (SCI) and CI with dynamic penalty function approach (DCI) are proposed. The approaches have been tested by solving several constrained test problems. The performance of the SCI and DCI have been compared with algorithms like GA, PSO, ABC, d-Ds. In addition, as well as three real world problems from mechanical engineering domain with improved solutions. The results were satisfactory and validated the applicability of CI methodology for solving real world problems.
40 citations
TL;DR: The results reveal that Couchbase had a better performance at most of the operations, except for retrieving multiple documents and inserting documents with multiple threads, operations in which MongoDB scored better.
Abstract: The amount of data being produced is increasing constantly, as the number and variety of connected devices are growing and the advances in data storage and mining are supporting this evolution. However, storing and handling high quantities of data is challenging the current Relational Database Management Systems. Big Data and its related products came to help in this matter, and the NoSQL databases arise with the purpose to offer better solutions and features to handle massive amounts of data with higher performance, sometimes near real-time. The present study presents the NoSQL databases scenario and background, and elaborates a detailed study with the characteristics, a features comparison and a performance evaluation of three different NoSQL databases extensively used in the market nowadays: Couchbase, MongoDB and RethinkDB. Tests were performed in two different scenarios: single thread and multiple threads. The results reveal that Couchbase had a better performance at most of the operations, e...
35 citations
TL;DR: In this paper the synchronization of two Chua circuits is simulated in Simulation Programs with Integrated Circuit Emphasis and it is shown that the choice of control signal is not straightforward, especially in the case of multistability and hidden attractors.
Abstract: Graphical AbstractTwo symmetric hidden chaotic attractors (blue), trajectories (red) from unstable manifolds of two saddle points are either attracted to locally stable zero equilibrium, or tend to infinity; trajectories (black) from stable manifolds tend to equilibria.
34 citations
TL;DR: The performance of an emerging socio inspired metaheuristic optimization technique referred to as Cohort Intelligence (CI) algorithm is evaluated on discrete and mixed variable nonlinear constrained optimization problems.
Abstract: In this study, the performance of an emerging socio inspired metaheuristic optimization technique referred to as Cohort Intelligence (CI) algorithm is evaluated on discrete and mixed variable nonlinear constrained optimization problems. The investigated problems are mainly adopted from discrete structural optimization and mixed variable mechanical engineering design domains. For handling the discrete solution variables, a round off integer sampling approach is proposed. Furthermore, in order to deal with the nonlinear constraints, a penalty function method is incorporated. The obtained results are promising and computationally more efficient when compared to the other existing optimization techniques including a Multi Random Start Local Search algorithm. The associated advantages and disadvantages of CI algorithm are also discussed evaluating the effect of its two parameters namely the number of candidates, and sampling space reduction factor.
30 citations
TL;DR: Eight variants of the Whale Optimisation Algorithm, that are based on eight different transfer functions, are introduced and used as search strategies in a wrapper feature selection model, and the superiority of the V-shaped approach is proven.
Abstract: In this paper, two variants of the Whale Optimization Algorithm (WOA), called SWOA and VWOA, are introduced and used as search strategies in a wrapper feature selection model. Feature selection is a challenging task in machine learning process. It aims to minimize the size of a dataset by removing redundant and/or irrelevant features, with no information lose, to improve the efficiency of the learning algorithms. In this work, two transfer functions (i.e., sigmoid and tanh) that belong to two different families (S-shaped and V-shaped) are used to convert the continuous version of the WOA to binary. The proposed approaches have been tested on 9 different high dimensional medical datasets, with a low number of samples and multiple classes. The results revealed a superior performance for the VWOA over the SWOA and other approaches used for the comparison purposes.
30 citations
TL;DR: A memristor-based neuromorphic system that can be used for ex situ training of various multi-layer neural network algorithms, based on an analogue neuron circuit that is capable of performing an accurate dot product calculation is described.
Abstract: This paper describes a memristor-based neuromorphic system that can be used for ex situ training of various multi-layer neural network algorithms. This system is based on an analogue neuron circuit that is capable of performing an accurate dot product calculation. The presented ex situ programming technique can be used to map many key neural algorithms directly onto the grid of resistances in a memristor crossbar. Using this weight-to-crossbar mapping approach along with the memristor based circuit architecture, complex neural algorithms can be easily implemented using this system. Some existing memristor based circuits provide an approximated dot product based on conductance summation, but neuron outputs are not directly correlated to the numerical values obtained in a traditional software approach. To show the effectiveness and versatility of this circuit, two different powerful neural networks were simulated. These include a Restricted Boltzmann Machine for character recognition and a Multilaye...
25 citations
TL;DR: A suitable drive signal has been identified based on intuitive analysis of the memristor dynamics, and by solving the optimization problem to achieve the phase space separation, which was formulated as an optimization problem, and solved by a genetic optimization algorithm developed in this study.
Abstract: Recently, the SWEET sensing setup has been proposed as a way of exploiting reservoir computing for sensing. The setup features three components: an input signal (the drive), the environment and a reservoir, where the reservoir and the environment are treated as one dynamical system, a superreservoir. Due to the reservoir-environment interaction, the information about the environment is encoded in the state of the reservoir. This information can be inferred (decoded) by analysing the reservoir state. The decoding is done by using an external drive signal. This signal is optimised to achieve a separation in the space of the reservoir states: Under different environmental conditions, the reservoir should visit distinct regions of the configuration space. We examined this approach theoretically by using an environment-sensitive memristor as a reservoir, where the memristance is the state variable. The goal has been to identify a suitable drive that can achieve the phase space separation, which was formulated as an optimization problem, and solved by a genetic optimization algorithm developed in this study. For simplicity reasons, only two environmental conditions were considered (describing a static and a varying environment). A suitable drive signal has been identified based on intuitive analysis of the memristor dynamics, and by solving the optimization problem. Under both drives the memristance is driven to two different regions of the onedimensional state space under the influence of the two environmental conditions, which can be used to infer about the environment. The separation occurs if there is a synchronisation between the drive and the environmental signals. To quantify the magnitude of the separation, we introduced a quality of sensing index: The ability to sense depends critically on the synchronisation between the drive and environmental conditions. If this synchronisation is not maintained the quality of sensing deteriorates
22 citations
TL;DR: The proposed Multi-CI outperformed these algorithms in terms of the solution quality including objective function value and computational cost, i.e. computational time and functional evaluations.
Abstract: A Multi-Cohort Intelligence (Multi-CI) metaheuristic algorithm in emerging socio-inspired optimisation domain is proposed. The algorithm implements intra-group and inter-group learning mech...
21 citations
TL;DR: It is proved that the iterates generated by the developed decentralized method converge to a consensual optimal solution (model) and numerical results demonstrate that it is a promising approach for decentralized learning in sensor networks.
Abstract: In this paper, we study the problem of decentralized learning in sensor networks in which local learners estimate and reach consensus to the quantity of interest inferred globally while communicati...
TL;DR: An entirely different novel class of sensing approaches is being suggested, to be referred to as ‘reservoir computing for sensing’, where the reservoir plays a central role, which could provide guidelines for engineering novel sensing applications.
Abstract: As a paradigm of computation, reservoir computing has gained an enormous momentum. In principle, any sufficiently complex dynamical system equipped with a readout layer can be used for any computation. This can be achieved by only adjusting the readout layer. Owning to this inherent flexibility of implementation, new applications of reservoir computing are being reported at a constant rate. However, relatively few studies focus on sensing, and in the ones that do, the reservoir is often exploited in a somewhat passive manner. The reservoir is used to post-process the signal from sensing elements that are placed separately, and the reservoir could be replaced by other information processing system without loss of functionality of the sensor (‘reservoir computing and sensing’). An entirely different novel class of sensing approaches is being suggested, to be referred to as ‘reservoir computing for sensing’, where the reservoir plays a central role. In the State Weaving Environment Echo Tracker (SWEE...
TL;DR: A generalization of static and dynamic mathematical models of behavior with explicitly stated reflexive models of agents’ decision-making in the framework of game theory, collective behavior theory and learning models is devoted.
Abstract: The paper is devoted to a generalization of static and dynamic mathematical models of behavior with explicitly stated reflexive models of agents’ decision-making. Reflexion is considered as agent’s...
TL;DR: The accurate forecasting of solar irradiance with hybrid machine learning algorithm using novel Persistence-Extreme Learning Machine (P-ELM) algorithm which offers better performance over the fundamental P-ELMs.
Abstract: The accurate forecasting of solar irradiance with hybrid machine learning algorithm is presented in this paper. A novel Persistence-Extreme Learning Machine (P-ELM) algorithm is used for training o...
TL;DR: A fast decentralised gradient-based algorithm is proposed for a general class of problems with convex and differentiable objective functions and guaranteed that all the nodes in the network would reach consensus on the global variable eventually.
Abstract: We consider the decentralised consensus optimization problem in this paper. A fast decentralised gradient-based algorithm is proposed for a general class of problems with convex and differentiable objective functions. The developed method guarantees that all the nodes in the network would reach consensus on the global variable eventually. Based on the proposed decentralised algorithm, a broadcast-based protocol is designed, which is capable of providing solution for real-time in situ seismic imaging. Extensive numerical experiments on both synthetic and real sensor network seismic data sets validate the superior efficiency of the proposed algorithm over the other benchmarks.
TL;DR: A new QoS aware and Energy efficient Opportunistic Routing protocol (QEOR) to efficiently routing data under QoS and energy constraints for WSNs and results show that QEOR provides best performances as compared to other OR protocols.
Abstract: Energy efficiency and Quality of Service (QoS) providing are known to be critical design concerns in routing protocols for Wireless Sensor Networks (WSNs). Recent studies, demonstrate that Opportunistic Routing (OR) can greatly improve the performance of WSNs by exploiting the broadcast nature of the wireless medium. In this paper, we propose a new QoS aware and Energy efficient Opportunistic Routing protocol (QEOR) to efficiently routing data under QoS and energy constraints for WSNs. QEOR uses a new multi-metric QoS based candidate selection method in order to accurately select and prioritise the candidate forwarders. The selection is focused on a QoS function that takes into consideration the reliabilty of buffers and links, while the prioritisation is established according to transmission delays. To achieve an obvious improvement on the energy consumption, QEOR uses an energy efficient coordination method and an implicit ACKnowledgement scheme for collision and redundancy avoidance. Simulation...
TL;DR: The simulation results indicate that the API algorithm can be effective in identifying the unknown parameters for given chaotic systems with high accuracy and low deviations.
Abstract: In this work, the Pachycondyla Apicalis metaheuristic algorithm (API) is used to identify and optimize control parameters for piezoelectric oscillator that exhibits frequency hysteresis behavior under strong excitation when asymmetric period which the bifurcation and chaotic behavior of higher harmonics appear by minimizing errors between actual and evaluated states of the model. In order to investigate the efficiency of the API algorithm, numerical experiments are carried out on the piezoelectric chaotic resonator. The simulation results indicate that the API algorithm can be effective in identifying the unknown parameters for given chaotic systems with high accuracy and low deviations.
TL;DR: In this article, it was shown that the number of independent memory states in a purely memristive circuit is constrained by the circuit conservation laws, and that the dynamics preserves these symmetry by means of a projection on the physical subspace.
Abstract: We discuss the properties of the dynamics of purely memristive circuits using a recently derived consistent equation for the internal memory variables of the involved memristors. In particular, we show that the number of independent memory states in a memristive circuit is constrained by the circuit conservation laws, and that the dynamics preserves these symmetry by means of a projection on the physical subspace. Moreover, we discuss other symmetries of the dynamics under various transformations of the involved variables, and study the weak and strong non-linear regimes of the dynamics. In the strong regime, we derive a conservation law for the internal memory variable. We also provide a condition on the reality of the eigenvalues of Lyapunov matrices. The Lyapunov matrix describes the dynamics close to a fixed point, for which show that the eigenvalues can be imaginary only for mixtures of passive and active components. Our last result concerns the weak non-linear regime, showing that the intern...
TL;DR: A complexity of signals is researched using approximate entropy, sample entropy, and correlation dimension, and 0-1 test for chaos is used to show chaos of almost all signals and for one signal randomness is detected using newly applied stress test.
Abstract: In this paper, Partial Discharge pattern as an indicator of the fault state of insulation systems of medium voltage overhead lines with covered conductors are described, analyzed, and their dynamical properties are researched. Application of data obtained in natural environment with huge variety of noise interferences, affected by various weather conditions, location and time of the day lead to questioning whether the PD-activity can be considered as a system with emergent-like behaviour. The complexity of obtained data and several signal types are examined and described in this contribution. As a main result, a complexity of signals is researched using approximate entropy, sample entropy, and correlation dimension. Finally, 0-1 test for chaos is used to show chaos of almost all signals and for one signal randomness is detected using newly applied stress test. In this paper, Partial Discharge pattern as an indicator of the fault state of insulation systems of medium voltage overhead lines with cov...
TL;DR: The study deals with the generalized breakout condition that generates the so called biomorphs, i.e. a kind of Julia sets whose form reminds to that of certain types of living microorganisms.
Abstract: An exploratory study is made on the dynamics of discrete maps in the complex plane endowed with memory of past iterations. The study deals with the generalized breakout condition that generates the so called biomorphs, i.e. a kind of Julia sets whose form reminds to that of certain types of living microorganisms. Metamorphosis of a biomorph with memory of degree alpha
TL;DR: A parallel algorithm is proposed that is implemented and evaluated on multicore CPUs and on many-core GPUs and that evaluates two alternative GPU implementations that use the CUDA and Thrust software platforms and a network of workstations based solution.
Abstract: Many approaches have been proposed for deriving tests from finite state machine (FSM) specifications with respect to some established coverage criteria. A fundamental core problem in FSM-based testing relates to the derivation of input sequences that can distinguish states of an FSM specification, aka distinguishing sequences. A major effort in the construction of these sequences is based on the derivation of a successors search-tree labeled by sets of pairs of states of the given machine. We aim at reducing the time associated with such constructions through the use of state-of-the-art parallel technologies. Namely, we propose a parallel algorithm that we implement and evaluate on multicore CPUs and on many-core GPUs. We evaluate two alternative GPU implementations that use the CUDA and Thrust software platforms and a network of workstations based solution. The latter sports a workload partitioning based on Divisible Load Theory. A rigorous set of experiments highlights the differences of the pro...
TL;DR: An implementation of a general purpose CPU using signed-digit arithmetic by exploiting memristors in order to implement multi-value registers is proposed and it is shown that a break-even point exists at which signed- digit algorithms outperform conventional binary arithmetic operations.
Abstract: The carry propagation of arithmetic operations is one of the major shortcomings of common binary number encodings as the two’s complement. Signed-digit arithmetic allows the addition of two numbers...
TL;DR: A probabilistic solution discovery algorithm is developed to solve the NP-hard 0-1 knapsack problem and it is shown that a subset of ordered strategies is used to update the vector that defines the probability of choosing each item.
Abstract: In this paper, a probabilistic solution discovery algorithm is developed to solve the NP-hard 0-1 knapsack problem. The proposed method consists of three steps: strategy development, strategy analy...
TL;DR: An FPGA-based pipelined hardware accelerator to reduce computation time for solving large dimension 0–1 KPs using Binary Harmony Search algorithm and the flexibility and parallel processing capabilities of FPGAs to perform the computation concurrently thus enhancing performance.
Abstract: The 0–1 knapsack problem (KP) is a well-known intractable optimization problem with wide range of applications. Harmony Search (HS) is one of the most popular metaheuristic algorithms to successfully solve 0–1 KPs. Nevertheless, metaheuristic algorithms are generally compute intensive and slow when implemented in software. In this paper, we present an FPGA-based pipelined hardware accelerator to reduce computation time for solving large dimension 0–1 KPs using Binary Harmony Search algorithm. The proposed architecture exploits the intrinsic parallelism of population based metaheuristic algorithm and the flexibility and parallel processing capabilities of FPGAs to perform the computation concurrently thus enhancing performance. To validate the efficiency of the proposed hardware accelerator, experiments were conducted using a large number of 0–1 KPs. Comparative analysis on experimental results reveals that the proposed approach offers promising speedups of 51× – 111× as compared with a software im...
TL;DR: A new clustering algorithm named Vertex Coloring Clustering (VCC) is presented, which uses the thought of vertex coloring to classify all cities into some classes and lets ACO act on each class to get local TSP routes, and joins these local routes as a whole route.
Abstract: Ant Colony Optimization (ACO) is a popular optimal algorithm, whose typical application is to solve Travel Salesman Problem (TSP). The time complexity of ACO is O(mN t), where the parameters m, N a...
TL;DR: Two modifications are proposed to the Progressive Random Walk algorithm in order to address its potentially insufficient search space coverage and the resulting algorithm is called modified Progressive Random walk.
Abstract: In this paper, two modifications are proposed to the Progressive Random Walk (PRW) algorithm in order to address its potentially insufficient search space coverage. The first modification replaces the Pseudo-Random Number Generator (PRNG) with the uniform distribution by the chaotic map based PRNG for generating of the offset values and the second modification is called direction switching and is based on experiment observation. The modifications are implemented into the PRW and the resulting algorithm is called modified Progressive Random Walk. The search space coverage of the two algorithms is compared. Both algorithms are used in macro ruggedness estimation of the CEC2015 benchmark set and the results are discussed.
TL;DR: Logical gates are constructed based on a method of numerical modeling of natural erosion of sandstone and AND and XOR gates and a one-bit binary half-adder are implemented.
Abstract: Logical gates are constructed based on a method of numerical modeling of natural erosion of sandstone. Values of logical variables are determined by the presence of sandstone. The presence of sandstone corresponds to the logical value of True, whereas the absence of material corresponds to the False value. Logical functions are computed through the formation of sandstone structures at action of natural erosion where loading conditions are specified. AND and XOR gates and a one-bit binary half-adder are implemented.
TL;DR: A leader election algorithm for large MANETs is presented, inspired by the concept of prevailing parliamentary democracy and elects three best-nodes – in terms of performance parameters like battery life, computing power, memory, hop distance, and mobility – as the president, leader, and vice leader.
Abstract: A fundamental problem of distributed systems, leader election, is presented in the context of mobile ad hoc networks (MANETs). In many distributed systems, the presence of a leader is necessary in order to monitor underlying computations, guarantee quality functioning, take checkpoints, generate the lost token, detect quiescence conditions, etc. Hence, several leader election algorithms have been proposed in the literature. Although, most of the algorithms focus on reducing the control message (messages that have the highest priority to deliver) count, there have been almost no attention on ensuring high availability of a leader despite various types of failures, especially, in the scenarios like rescue and warfare, where the absence of the leader, even for a short duration, may lead to havoc. We focus on this issue, particularly, for large MANETs, where a large number of applications fails to perform in the absence of a leader. We present a leader election algorithm for large MANETs. The algorith...
TL;DR: It is insisted that there can be two different foundations of mathematics: the discrete foundations, dealing with a logical way of automatic proving from some axioms and the analogue foundations, combining proof trees on tree forests by using the analogies as inference metarules.
Abstract: In neuroscience we know that there exist the following two basic effects in perceiving information: (i) the lateral inhibition, responsible for a cognitive blindness in seeing the whole picture; (ii) the lateral activation, responsible for a cognitive blindness in seeing the details. In this paper, we show that the same effects can be considered in the proof cognitions performed by mathematicians in proving sophisticated theorems, such as Fermat’s Last Theorem. Hence, we insist that there can be two different foundations of mathematics: (i) the discrete foundations, dealing with a logical way of automatic proving from some axioms (the lateral inhibition in math); (ii) the analogue foundations, combining proof trees on tree forests by using the analogies as inference metarules (the lateral activation in math). We propose a kind of analogue logic for analogue reasoning in mathematics.
TL;DR: The dependence between configuration entropy of the landscape, efficiency of the path-finding algorithm based on the cellular automaton was found and the dependence of the average speed of the agents’ motion on the landscape configuration entropy was shown.
Abstract: The article is devoted to the construction of the motion model for agents with memory. Agents can be interpreted, for example, as mobile robots or soldiers. Agents move on the landscape consisting of squares with different passability. The model is based on the cellular automaton with one common to all agents layer corresponding to the landscape and many agent-specific layers corresponding to an agent’s memory. Methods for the random landscape generation are developed. The dependence between configuration entropy of the landscape, efficiency of the path-finding algorithm based on the cellular automaton was found. Also, the dependence of the average speed of the agents’ motion on the landscape configuration entropy was shown.