Observations on Effect of IPC in GA Based Scheduling on Computational Grid

2012 ◽  
Vol 4 (1) ◽  
pp. 67-80 ◽  
Author(s):  
Shiv Prakash ◽  
Deo P. Vidyarthi
Keyword(s):  
Optimization Problems ◽  
Scheduling Algorithm ◽  
Arrival Rate ◽  
Computational Grid ◽  
Process Communication ◽  
Model Studies ◽  
Proposed Model ◽  
Distributed Platform ◽  
Complex Optimization ◽  
Inter Process Communication

Computational Grid (CG) provides a wide distributed platform for high end compute intensive applications. Inter Process Communication (IPC) affects the performance of a scheduling algorithm drastically. Genetic Algorithms (GA), a search procedure based on the evolutionary computation, is able to solve a class of complex optimization problems. This paper proposes a GA based scheduling model observing the effect of IPC on the performance of scheduling in computational grid. The proposed model studies the effects of Inter Process Communication (IPC), processing rate () and arrival rate (). Simulation experiment, to evaluate the performance of the proposed algorithm is conducted and results reveal the effectiveness of the model.

Maximizing the Flow Reliability in Cellular IP Network Using PSO

2011 ◽  
Vol 3 (1) ◽  
pp. 1-19
Author(s):  
Mohammad Anbar ◽  
Deo P. Vidyarthi
Keyword(s):  
Optimization Problems ◽  
Arrival Rate ◽  
Base Station ◽  
Base Stations ◽  
Safe Delivery ◽  
Mobile Hosts ◽  
Model Studies ◽  
Packet Arrival ◽  
Ip Network ◽  
Cellular Ip

A Cellular IP (CIP) network involves a bulk of data transmission. It is highly reliable and guarantees the safe delivery of the packets required in such systems. Reliable traffic performance leads to efficient and reliable connectivity in Cellular IP network. CIP network, which consists of mobile hosts, base stations, and links, are often vulnerable and prone to failure. During the routing operation in the network, the base station, which works as router for the transmitted packets, may fail to perform. Reliable transmission is desirable, in terms of services of the base stations in the network, reliable routing, and processing the data. In this paper, the authors design a reliability model to increase the reliability of a flow, consisting of packets, passing through routers in a Cellular IP network. Particle Swarm Optimization (PSO) is able to solve a class of complex optimization problems. PSO is used to improve the reliability of the flow in CIP network. The proposed model studies the effects of packet processing rate (), packet arrival rate (), and the number of packets per flow on the reliability of the system. A simulation experiment is conducted and results reveal the effectiveness of the model.

Maximizing the Flow Reliability in Cellular IP Network Using PSO

2013 ◽  
pp. 1-19
Author(s):  
Mohammad Anbar ◽  
Deo P. Vidyarthi
Keyword(s):  
Optimization Problems ◽  
Arrival Rate ◽  
Base Station ◽  
Base Stations ◽  
Safe Delivery ◽  
Mobile Hosts ◽  
Model Studies ◽  
System A ◽  
Ip Network ◽  
Cellular Ip

A Cellular IP (CIP) network involves a bulk of data transmission. It is highly reliable and guarantees the safe delivery of the packets required in such systems. Reliable traffic performance leads to efficient and reliable connectivity in Cellular IP network. CIP network, which consists of mobile hosts, base stations, and links, are often vulnerable and prone to failure. During the routing operation in the network, the base station, which works as router for the transmitted packets, may fail to perform. Reliable transmission is desirable, in terms of services of the base stations in the network, reliable routing, and processing the data. In this paper, the authors design a reliability model to increase the reliability of a flow, consisting of packets, passing through routers in a Cellular IP network. Particle Swarm Optimization (PSO) is able to solve a class of complex optimization problems. PSO is used to improve the reliability of the flow in CIP network. The proposed model studies the effects of packet processing rate (), packet arrival rate (), and the number of packets per flow on the reliability of the system. A simulation experiment is conducted and results reveal the effectiveness of the model.

A Hybrid GABFO Scheduling for Optimal Makespan in Computational Grid

2014 ◽  
Vol 5 (3) ◽  
pp. 57-83 ◽  
Author(s):  
Shiv Prakash ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Optimization Problem ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Characteristic Parameter ◽  
Search Space ◽  
Computational Grid ◽  
Complex Task ◽  
Proposed Model ◽  
Evolutionary Technique ◽  
Complex Optimization

Scheduling in Computational Grid (CG) is an important but complex task. It is done to schedule the submitted jobs onto the nodes of the grid so that some characteristic parameter is optimized. Makespan of the job is an important parameter and most often scheduling is done to optimize makespan. Genetic Algorithm (GA) is a search procedure based on the evolutionary technique that is able to solve a class of complex optimization problem. However, GA takes longer to converge towards its near optimal solution. Bacteria Foraging Optimization (BFO), also derived from nature, is a technique to optimize a given function in a distributed manner. Due to limited availability of bacteria, BFO is not suitable to optimize the solution for the problem involving a large search space. Characteristics of both GA and BFO are combined so that their benefits can be reaped. The hybrid approach is referred to as Genetic Algorithms Bacteria Foraging Optimization (GABFO) algorithm. The proposed GABFO has been applied to optimize makespan of a given schedule in a computational grid. Results of the simulation, conducted to evaluate the performance of the proposed model, reveal the effectiveness of the proposed model.

scholarly journals Hierarchical Swarm Model: A New Approach to Optimization

2010 ◽  
Vol 2010 ◽  
pp. 1-30 ◽  
Author(s):  
Hanning Chen ◽  
Yunlong Zhu ◽  
Kunyuan Hu ◽  
Xiaoxian He
Keyword(s):  
Complex System ◽  
Evolutionary Algorithms ◽  
Performance Improvement ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Swarm Optimization ◽  
New Approach ◽  
Proposed Model ◽  
Complex Optimization ◽  
Different Levels

This paper presents a novel optimization model called hierarchical swarm optimization (HSO), which simulates the natural hierarchical complex system from where more complex intelligence can emerge for complex problems solving. This proposed model is intended to suggest ways that the performance of HSO-based algorithms on complex optimization problems can be significantly improved. This performance improvement is obtained by constructing the HSO hierarchies, which means that an agent in a higher level swarm can be composed of swarms of other agents from lower level and different swarms of different levels evolve on different spatiotemporal scale. A novel optimization algorithm (named ), based on the HSO model, is instantiated and tested to illustrate the ideas of HSO model clearly. Experiments were conducted on a set of 17 benchmark optimization problems including both continuous and discrete cases. The results demonstrate remarkable performance of the algorithm on all chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms.

scholarly journals Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems

2021 ◽  
Vol 11 (8) ◽  
pp. 3430
Author(s):  
Erik Cuevas ◽  
Héctor Becerra ◽  
Héctor Escobar ◽  
Alberto Luque-Chang ◽  
Marco Pérez ◽  
...  
Keyword(s):  
Convergence Rates ◽  
Optimization Problems ◽  
Search Space ◽  
Second Order ◽  
Order System ◽  
Search Patterns ◽  
Multiple Behaviors ◽  
Complex Optimization ◽  
Second Order Systems ◽  
Order Algorithm

Recently, several new metaheuristic schemes have been introduced in the literature. Although all these approaches consider very different phenomena as metaphors, the search patterns used to explore the search space are very similar. On the other hand, second-order systems are models that present different temporal behaviors depending on the value of their parameters. Such temporal behaviors can be conceived as search patterns with multiple behaviors and simple configurations. In this paper, a set of new search patterns are introduced to explore the search space efficiently. They emulate the response of a second-order system. The proposed set of search patterns have been integrated as a complete search strategy, called Second-Order Algorithm (SOA), to obtain the global solution of complex optimization problems. To analyze the performance of the proposed scheme, it has been compared in a set of representative optimization problems, including multimodal, unimodal, and hybrid benchmark formulations. Numerical results demonstrate that the proposed SOA method exhibits remarkable performance in terms of accuracy and high convergence rates.

scholarly journals Chunking and cooperation in particle swarm optimization for feature selection

2021 ◽  
Author(s):  
Malek Sarhani ◽  
Stefan Voß
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Cooperative Learning ◽  
Optimization Problems ◽  
Practical Importance ◽  
Particle Swarm ◽  
Large Datasets ◽  
Social Phenomena ◽  
Swarm Optimization ◽  
Complex Optimization

AbstractBio-inspired optimization aims at adapting observed natural behavioral patterns and social phenomena towards efficiently solving complex optimization problems, and is nowadays gaining much attention. However, researchers recently highlighted an inconsistency between the need in the field and the actual trend. Indeed, while nowadays it is important to design innovative contributions, an actual trend in bio-inspired optimization is to re-iterate the existing knowledge in a different form. The aim of this paper is to fill this gap. More precisely, we start first by highlighting new examples for this problem by considering and describing the concepts of chunking and cooperative learning. Second, by considering particle swarm optimization (PSO), we present a novel bridge between these two notions adapted to the problem of feature selection. In the experiments, we investigate the practical importance of our approach while exploring both its strength and limitations. The results indicate that the approach is mainly suitable for large datasets, and that further research is needed to improve the computational efficiency of the approach and to ensure the independence of the sub-problems defined using chunking.

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