Master-slave Model Research Articles

Balancing the Degree of Exploration and Exploitation of Swarm Intelligence Using Parallel Computing

The two basic search behaviors used in metaheuristic algorithms in general and swarm intelligence in particular are exploration and exploitation. Exploration refers to searching the unexplored area of the feasible region while exploitation refers to the search of the neighborhood of a promising region. The success of these algorithms highly depends on how these two search behaviors are balanced. Increasing the number of initial solutions indeed increase the performance of the algorithm over the expense of the runtime. Different exploration and exploitation adjustments are used in different metaheuristic algorithms. To well balance the degree of exploration and exploitation, which ultimately leads to finding good solutions, needs a careful implementation and sufficient runtime. However, the runtime is another issue which has been a bottleneck for different application, especially for high dimensional, highly constrained and expensive objective function cases. Using parallel computing to overcome this limitation has been a central research issue in the study of metaheuristic algorithms. The three basic parallelization approaches used are the parallel move model which is a master slave model where solutions will be distributed to different processors for updating, parallel multi start model which uses several updating methods for the solutions in parallel and move acceleration method, which refers to a parallel implementation with parallelizing the objective function. There is no specific parallelization used towards balancing of exploration and exploitation is mentioned. Hence, in this paper a parallel implementation of swarm intelligence in general and two algorithms, namely particle swarm optimization and prey predator algorithm, in particular will be discussed. The parallelization proposed is aimed to merge the parallel move model and the parallel multi start model with the aim of adjusting the degree of exploration and exploitation. Experimental study based on ten benchmark problems from constrained as well as unconstrained problems will be used for simulation. It will be shown that the parallelization with specific distribution of exploration and exploitation runs better and efficiently compared to the serial and also the parallel version without exporation/exploitation adjustment.

A Distributed Swarm Optimizer With Adaptive Communication for Large-Scale Optimization.

Large-scale optimization with high dimensionality and high computational cost becomes ubiquitous nowadays. To tackle such challenging problems efficiently, devising distributed evolutionary computation algorithms is imperative. To this end, this paper proposes a distributed swarm optimizer based on a special master-slave model. Specifically, in this distributed optimizer, the master is mainly responsible for communication with slaves, while each slave iterates a swarm to traverse the solution space. An asynchronous and adaptive communication strategy based on the request-response mechanism is especially devised to let the slaves communicate with the master efficiently. Particularly, the communication between the master and each slave is adaptively triggered during the iteration. To aid the slaves to search the space efficiently, an elite-guided learning strategy is especially designed via utilizing elite particles in the current swarm and historically best solutions found by different slaves to guide the update of particles. Together, this distributed optimizer asynchronously iterates multiple swarms to collaboratively seek the optimum in parallel. Extensive experiments on a widely used large-scale benchmark set substantiate that the distributed optimizer could: 1) achieve competitive effectiveness in terms of solution quality as compared to the state-of-the-art large-scale methods; 2) accelerate the execution of the algorithm in comparison with the sequential one and obtain almost linear speedup as the number of cores increases; and 3) preserve a good scalability to solve higher dimensional problems.

GPU Energy Consumption Optimization With a Global-Based Neural Network Method

With the widespread use of smart technologies, graphics processing unit (GPU) power-optimization issues are becoming increasingly important. Many researchers have tried to use dynamic voltage and frequency scaling (DVFS) technology to optimize a GPU’s internal energy consumption. However, DVFS energy management often has difficulty balancing GPU performance and energy efficiency. This paper aims to implement a DVFS energy management strategy. We constructed a new type of neural network to a GPU-based energy management scheme, implemented the global-based DVFS model, and explored its implementation details. Using a master-slave model, we built a global energy control solution strategy. This strategy performs global collaborative DVFS adjustments on the GPU’s energy consumption module based on task characteristics. Through the software construction and implementation of the global-based DVFS model, we proved that the strategy improves the GPU performance while improving the GPU’s energy efficiency. We conducted performance and energy tests on three GPUs on the Tesla, Fermi, and Kepler platforms. The experiments showed that this strategy improved the performance and power consumption of GPUs based on each of the platforms.

Lag synchronisation of master–slave dynamical systems via intermittent control

ABSTRACTIn this paper, the problem on lag synchronisation is investigated for a master–slave model. The intermittent control method is used to design controllers, lag synchronisation criteria are proposed. Together with some Lyapunov–Krasovskii functionals and effective mathematical techniques, several conditions are derived. An example is given to illustrate the effectiveness of the proposed methods.

Scalable preprocessing of high volume environmental acoustic data for bioacoustic monitoring.

In this work, we examine the problem of efficiently preprocessing and denoising high volume environmental acoustic data, which is a necessary step in many bird monitoring tasks. Preprocessing is typically made up of multiple steps which are considered separately from each other. These are often resource intensive, particularly because the volume of data involved is high. We focus on addressing two challenges within this problem: how to combine existing preprocessing tasks while maximising the effectiveness of each step, and how to process this pipeline quickly and efficiently, so that it can be used to process high volumes of acoustic data. We describe a distributed system designed specifically for this problem, utilising a master-slave model with data parallelisation. By investigating the impact of individual preprocessing tasks on each other, and their execution times, we determine an efficient and accurate order for preprocessing tasks within the distributed system. We find that, using a single core, our pipeline executes 1.40 times faster compared to manually executing all preprocessing tasks. We then apply our pipeline in the distributed system and evaluate its performance. We find that our system is capable of preprocessing bird acoustic recordings at a rate of 174.8 seconds of audio per second of real time with 32 cores over 8 virtual machines, which is 21.76 times faster than a serial process.

CS-ABC: a cooperative system based on artificial bee colony to resolve the DNA fragment assembly problem

DNA Fragment Assembly (DFA) problem is one of the most active research areas in bioinformatics. It consists in assembling a set of DNA fragments to determine the complete genome sequence. Because of the large number of fragments to assemble, this problem is classified as a NP-hard optimisation problem. Thus, in order to deal with the large search space of such problem, we propose a new cooperative approach involving a set of metaheuristics. The proposed cooperative approach, named CS-ABC, is based on artificial bee colony algorithm. In this approach, metaheuristics cooperate as bees with artificial bee colony algorithm to improve the exploration and exploitation ability, forming a cooperative system. The use of a set of metaheuristics improves naturally the exploration ability since each one of them explores differently the search space. The communication between these metaheuristics is established through a shared memory. The exploitation is also enhanced by using different efficient DFA methods communicating according to the master-slave model. In the computational experiment we firstly, analyse the proposed method behaviour resolving DFA problem. Then, we compare its performance against numerous DFA methods with noiseless and noisy data based on three models of error. The proposed method has obtained promising and encouraging results.

Multi-agent oriented solution for forecasting-based control strategy with load priority of microgrids in an island mode – Case study: Tunisian petroleum platform

To improve the power supply availability in an island microgrid, this paper proposes a new approach that integrates distributed energy sources economically, reliably and efficiently. In an island mode, a microgrid must ensure its self-sufficiency of energy production since it cannot make an energetic exchange with a main grid. However, in this mode, the random behavior of the resources affected by meteorological factors presents a major constraint. The challenge related to the power availability in microgrids is to find a solution that faces the operation of intermittent power sources. The microgrid should guarantee a useful power management in order to achieve a high availability of energy. In this paper, we present a mathematical model to describe the influence of the meteorological factors on the sources production. We propose a multi-agent control strategy based on the production forecasting and load shedding for a high availability of the microgrid power supply. The proposed multi-agent system uses the master-slave model in which the communication and negotiation between the defined agents are performed by a concept of tokens. The developed control system is implemented on Spartan 6 FPGA-Board. The paper's contribution is applied to a Tunisian petroleum platform where several blackouts are recorded between 2012 and 2014. Simulation and experimental results show clearly a high availability as a performance of the proposed control strategy.

Designing bijective S-boxes using Algorithm Portfolios with limited time budgets

Substitution boxes (S-boxes) are essential parts of symmetric-key cryptosystems. Designing S-boxes with satisfactory nonlinearity and autocorrelation properties is a challenging task for both theoretical algebraic methods and computational optimization algorithms. Algorithm Portfolios (APs) are algorithmic schemes where multiple copies of the same algorithm or different algorithms share the available computational resources, running concurrently or interchangeably on a number of processors. Recently, APs have gained increasing attention due to their remarkable efficiency in multidisciplinary applications. The present work is a preliminary study of parallel APs on the bijective S-boxes design problem. The proposed APs comprise two state-of-the-art heuristic algorithms, namely Simulated Annealing and Tabu Search, and they are parallelized according to the master-slave model without exchange of information among the constituent algorithms. The proposed APs are experimentally assessed on typical problem instances under limited time budgets. Different aspects of their performance is analyzed, suggesting that the considered APs are competitive in terms of solution quality and running time against their constituent algorithms as well as different approaches.

New optimal modular Sidon sets

Sidon set and its related problems including modular Sidon set have many applications in information theory and communication system. They have been widely studied in radio frequency selection, radio antennae placement and error correcting codes. In this paper, we establish a backtracking algorithm with pruning techniques to construct modular Sidon sets. By using master-slave parallel model, 50 new optimal modular Sidon sets are constructed.

Sistem Komunikasi Modul Sensor Jamak Berbasiskan Mikrokontroler Menggunakan Serial Rs-485 Mode Multi Processor Communication (Mpc)

Multi-sensor communication system uses RS-485 standard communication connecting each microcontroller-based data processing unit to form BUS topology network. The advantages of this communication system are: connectivity (easy to connecting devices on a network), scalability (flexibility to expand the network), more resistant to noise, and easier maintenance. The System is built using Master-Slave communication approach model. This system need to filter every data packet on communication channel because every device that connect in this network can hear every data packet across this network. Multi Processor Communication (MPC) model is applied to reduce processor’s burden in inspecting every data packet, so the processor that work in slave side only need to inspect the message for itself without inspecting every data packet across the communication chanel.

Sistem Komunikasi Modul Sensor Jamak Berbasiskan Mikrokontroler Menggunakan Serial Rs-485 Mode Multi Processor Communication (Mpc)

Multi-sensor communication system uses RS-485 standard communication connecting each microcontroller-based data processing unit to form BUS topology network. The advantages of this communication system are: connectivity (easy to connecting devices on a network), scalability (flexibility to expand the network), more resistant to noise, andeasier maintenance. The System is built using Master-Slave communication approach model. This system need to filter every data packet on communication channel because every device that connect in this network can hear every data packet across this network. Multi Processor Communication (MPC) modelis applied to reduce processor’s burden in inspecting every data packet, so the processor that work in slave side only need to inspect the message for itself without inspecting every data packet across the communication chanel.

Research on a Dynamic Master-Slave Cournot Triopoly Game Model with Bounded Rational Rule and Its Control

The oligopoly market is modelled by a new dynamic master-slave Cournot triopoly game model with bounded rational rule. The local stabile conditions and the stable region are got by the dynamical systems bifurcation theory. The dynamics characteristics of the system with the changes of the adjustment speed parameters are analyzed by means of bifurcation diagram, largest Lyapunov exponents, phase portrait, and sensitive dependence on initial conditions. Furthermore, the parameters adjustment method is used to control the complex dynamical behaviors of the systems. The derived results have some important theoretical and practical meanings for the oligopoly market.

Master–slave model-based parallel chaos optimization algorithm for parameter identification problems

The parameter identification problem can be formalized as a multi-dimensional optimization problem, where an objective function is established minimizing the error between the estimated and measured data. In this article, a master–slave model (MSM)-based parallel chaos optimization algorithm (PCOA) (denoted as MSM-PCOA) is proposed for parameter identification problems. The MSM-PCOA is a novel global optimization algorithm, where twice carrier wave chaos search is employed as the master model, while the migration and crossover operation are used as the slave model. The MSM-PCOA is applied to the parameter identification of two different complex systems: bidirectional inductive power transfer system and chaotic systems. Simulation results, compared with other optimization algorithms, show that MSM-PCOA has better parameter identification performance.

Current sharing in parallel fuel cell generation system based on model predictive control

Parallel connection is usually adapted in fuel cell system for the high power applications. Fuel cell subsystem has different dynamic characteristics. The connection will show imbalance in load sharing and current distribution. Thus a robust parallel operation and current sharing algorithm is required. In this paper the block diagram of parallel fuel cell generation system is presented, and a master-slave model predictive control (MPC) method is proposed for the current-sharing control of the systems. Neural network optimization is used to reduce the computational time of single step optimization in model predictive control. The proposed MPC-based current sharing strategy is verified to be feasible and effective.

Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution

This paper present short-term combined economic and emission hydrothermal optimization, addressing total fuel costs and emissions minimization. This paper uses the fuel cost function with valve-point effect, which increases the degree of optimization problem difficulty. The optimal balance between the addressed objectives, that conflict with each other, can be obtained with appropriate hydro and thermal generation schedules. A surrogate differential evolution is applied in order to satisfy 24-h system demand and final states of hydro power plant reservoirs by minimized total fuel costs and emissions. This paper proposes a novel master–slave model optimization algorithm, where the optimal thermal schedules are obtained within the slave model. The data obtained from the slave model are saved into a matrix, which serves as a surrogate model for a master model, where the hydrothermal optimization with all objectives and constraints is conducted by using a parallel self-adaptive differential evolution algorithm. In order to show the effectiveness of the proposed method, different case studies are used: economic load scheduling, economic emission scheduling, and combined economic emission scheduling. The proposed method is verified on a model consisting of four hydro power plants and three thermal power plants.

Maintenance of cooperative overlays in multi‐overlay networks

In overlay-based applications, multiple overlay networks are deployed to fulfill different service requirements. A multi-overlay environment may exist in which a number of nodes simultaneously participate in the networks. When there are multiple overlay-based applications running over a set of nodes, some of the nodes take extra effort to maintain multi-overlay networks. Therefore, maintaining these co-existing overlays incurs redundant maintenance overhead. This research presents a cooperative strategy for exploiting a master–slave model to handle the common overlay-maintenance. The purpose is to eliminate the redundant maintenance overhead. To evaluate system performance, this study not only analyses various combinations of multiple overlays but also considers the effectiveness of the master selection approach. Experimental results demonstrated that the proposed cooperative strategy significantly decreases the redundant overlay-maintenance overhead. In some cases, the overall reduction ratio of maintaining multiple overlays is as high as 60%.

Performance Comparison of Centralized and Distributed Network Models to Support QoS in Fixed WiMAX

Worldwide Interoperability for Microwave Access (WiMAX) defines different data delivery services that can be used in order to satisfy Quality of Service (QoS) requirements such as flexibility and efficiency. In this paper a new distributed Master-Slave model was introduced to compare the Performance between the centralized and distributed Network models in order to support QoS in fixed WiMAX with respect to throughput, delay and application response time parameters, to enhance the services that are provided to the end users. The design has been evaluated using the simulation tool OPNET modeler 16.0. The results obtained from the two models shows significant increase in the network throughput and drastic decrease in network delay and application response time. The results in scenario1 of two models shows the throughput increases from 50,000bit/sec to 100,000bit/sec, delay decrease from 0.0060/sec to 0.0040/sec and application response time decrease from 0.80/sec to 0.58 which greatly enhances the QoS performance.

Complexity Analysis of a Master-Slave Oligopoly Model and Chaos Control

We establish a master-slave oligopoly game model with an upstream monopoly whose output is considered and two downstream oligopolies whose prices are considered. The existence and the local stable region of the Nash equilibrium point are investigated. The complex dynamic properties, such as bifurcation and chaos, are analyzed using bifurcation diagrams, the largest Lyapunov exponent diagrams, and the strange attractor graph. We further analyze the long-run average profit of the three firms and find that they are all optimal in the stable region. In addition, delay feedback control method and limiter control method are used in nondelayed model to control chaos. Furthermore, a delayed master-slave oligopoly game model is considered, and the three firms’ profit in various conditions is analyzed. We find that suitable delayed parameters are important for eliminating chaos and maximizing the profit of the players.

Analysis and simulation of the dynamic spectrum allocation based on parallel immune optimization in cognitive wireless networks.

Spectrum allocation is one of the key issues to improve spectrum efficiency and has become the hot topic in the research of cognitive wireless network. This paper discusses the real-time feature and efficiency of dynamic spectrum allocation and presents a new spectrum allocation algorithm based on the master-slave parallel immune optimization model. The algorithm designs a new encoding scheme for the antibody based on the demand for convergence rate and population diversity. For improving the calculating efficiency, the antibody affinity in the population is calculated in multiple computing nodes at the same time. Simulation results show that the algorithm reduces the total spectrum allocation time and can achieve higher network profits. Compared with traditional serial algorithms, the algorithm proposed in this paper has better speedup ratio and parallel efficiency.