scholarly journals Fuzzy particle swarm optimization algorithm (NFPSO) for reachability analysis of complex software systems

2020 ◽  
Author(s):  
Nahid Salimi ◽  
Vahid Rafe ◽  
hamed tabrizchi ◽  
Amir Mosavi
Keyword(s):  
Particle Swarm Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Pso Algorithm ◽  
Software Systems ◽  
Reachable State ◽  
State Space Explosion ◽  
Adaptive Pso ◽  
Speed And Accuracy ◽  
Fuzzy Adaptive ◽  
Do So

Nowadays, model checking is applied as an accuratetechnique to verify software systems. The main problem of modelchecking techniques is the state space explosion. This problemoccurs due to the exponential memory usage by the model checker.In this situation, using meta-heuristic and evolutionary algorithmsto search for a state in which a property is satisfied/violated is apromising solution. Recently, different evolutionary algorithmslike GA, PSO, etc. are applied to find deadlock state. Even thoughuseful, most of them are concentrated on finding deadlock. Thispaper proposes a fuzzy algorithm in order to analyze reachabilityproperties in systems specified through GTS with enormous statespace. To do so, we first extend the existing PSO algorithm (forchecking deadlocks) to analyze reachability properties. Then, toincrease the accuracy, we employ a Fuzzy adaptive PSO algorithmto determine which state and path should be explored in each stepto find the corresponding reachable state. These two approachesare implemented in an open-source toolset for designing andmodel checking GTS called GROOVE. Moreover, theexperimental results indicate that the hybrid fuzzy approachimproves speed and accuracy in comparison with other techniquesbased on meta-heuristic algorithms such as GA and the hybrid ofPSO-GSA in analyzing reachability properties.

scholarly journals Fuzzy particle swarm optimization algorithm (NFPSO) for reachability analysis of complex software systems

2020 ◽  
Author(s):  
Nahid Salimi ◽  
Vahid Rafe ◽  
Hamed Tabrizchi ◽  
Amir Mosavi
Keyword(s):  
Model Checking ◽  
Evolutionary Algorithms ◽  
State Space ◽  
Heuristic Algorithms ◽  
Pso Algorithm ◽  
Software Systems ◽  
Adaptive Pso ◽  
Promising Solution ◽  
Speed And Accuracy ◽  
Fuzzy Adaptive

Nowadays, model checking is applied as an accurate technique to verify software systems. The main problem of model checking techniques is the state space explosion. This problem occurs due to the exponential memory usage by the model checker. In this situation, using meta-heuristic and evolutionary algorithms to search for a state in which a property is satisfied/violated is a promising solution. Recently, different evolutionary algorithms like GA, PSO, etc. are applied to find deadlock state. Even though useful, most of them are concentrated on finding deadlock. This paper proposes a fuzzy algorithm in order to analyze reachability properties in systems specified through GTS with enormous state space. To do so, we first extend the existing PSO algorithm (for checking deadlocks) to analyze reachability properties. Then, to increase the accuracy, we employ a Fuzzy adaptive PSO algorithm to determine which state and path should be explored in each step to find the corresponding reachable state. These two approaches are implemented in an open-source toolset for designing and model checking GTS called GROOVE. Moreover, the experimental results indicate that the hybrid fuzzy approach improves speed and accuracy in comparison with other techniques based on meta-heuristic algorithms such as GA and the hybrid of PSO-GSA in analyzing reachability properties.

Task Scheduling Using an Adaptive PSO Algorithm in Cloud Computing Environment

2018 ◽  
pp. 419-426
Author(s):  
B. Sivaramakrishna ◽  
T. V. Rao
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Electricity Generation ◽  
Heuristic Algorithms ◽  
Renewable Energy Sources ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Adaptive Pso

Now-a-days energy planners are aiming to increase the use of renewable energy sources and nuclear to meet the electricity generation. But till now coal-based power plants are the major source of electricity generation. The problem of task scheduling is one of the most important steps in taking advantage of the cloud computing environment. Various experiments show that although it is almost impossible to have an optimal solution, it seems that there is a more optimal solution using heuristic algorithms. This work compares three heuristic approaches to scheduling cloud environment tasks. These approaches are the PSO algorithm, the ACO, and the adaptive PSO algorithm for efficient task scheduling. The goal of all three of these algorithms is to generate an optimal schedule to minimize task completion time.

The Optimization of Project Time-Cost Based on Particle Swarm Optimization Algorithm

2013 ◽  
Vol 791-793 ◽  
pp. 1423-1426
Author(s):  
Hai Min Wei ◽  
Rong Guang Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Implementation Process ◽  
Pso Algorithm ◽  
Project Schedule ◽  
Swarm Optimization ◽  
Schedule Optimization ◽  
Schedule Management

Project schedule management is the management to each stage of the degree of progress and project final deadline in the project implementation process. Its purpose is to ensure that the project can meet the time constraints under the premise of achieving its overall objectives.When the progress of schedule found deviation in the process of schedule management ,the progress of the plan which have be advanced previously need to adjust.This article mainly discussed to solve the following two questions:establish the schedule optimization model by using the method of linear;discuss the particle swarm optimization (PSO) algorithm and its parameters which have effect on the algorithm:Particle swarm optimization (PSO) algorithm is presented in the time limited project and the application of a cost optimization.

Analysis of FBG Reflection Spectrum with PSO Algorithm

2012 ◽  
Vol 182-183 ◽  
pp. 1953-1957
Author(s):  
Zhao Xia Wu ◽  
Shu Qiang Chen ◽  
Jun Wei Wang ◽  
Li Fu Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
High Precision Measurement ◽  
Swarm Optimization ◽  
Fbg Sensor ◽  
Fast Convergence Rate ◽  
Force Sense

When the parameters were measured by using fiber Bragg grating (FBG) in practice, there were some parameters hard to measure, which would influenced the reflective spectral of FBG severely, and make the characteristic information harder to be extracted. Therefore, particle swarm optimization algorithm was proposed in analyzing the uniform force reflective spectral of FBG. Based on the uniform force sense theory of FBG and particle swarm optimization algorithm, the objective function were established, meanwhile the experiment and simulation were constructed. And the characteristic information in reflective spectrum of FBG was extracted. By using particle swarm optimization algorithm, experimental data showed that particle swarm optimization algorithm used in extracting the characteristic information not only was efficaciously and easily, but also had some advantages, such as high accuracy, stability and fast convergence rate. And it was useful in high precision measurement of FBG sensor.

ANALYSIS OF MUTATION OPERATORS ON QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION ALGORITHM

2009 ◽  
Vol 05 (02) ◽  
pp. 487-496 ◽  
Author(s):  
WEI FANG ◽  
JUN SUN ◽  
WENBO XU
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Novel Mutation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Mutation Operator ◽  
Fundamental Theory ◽  
Swarm Optimization ◽  
Mutation Operators ◽  
Better Than

Mutation operator is one of the mechanisms of evolutionary algorithms (EAs) and it can provide diversity in the search and help to explore the undiscovered search place. Quantum-behaved particle swarm optimization (QPSO), which is inspired by fundamental theory of PSO algorithm and quantum mechanics, is a novel stochastic searching technique and it may encounter local minima problem when solving multi-modal problems just as that in PSO. A novel mutation mechanism is proposed in this paper to enhance the global search ability of QPSO and a set of different mutation operators is introduced and implemented on the QPSO. Experiments are conducted on several well-known benchmark functions. Experimental results show that QPSO with some of the mutation operators is proven to be statistically significant better than the original QPSO.

scholarly journals Performance Evaluation of Meta-Heuristic Algorithms for Task Scheduling in Cloud Environment

2021 ◽  
pp. 87-93
Author(s):  
Jai Bhagwan ◽  
Sanjeev Kumar
Keyword(s):  
Task Scheduling ◽  
Heuristic Algorithms ◽  
Minimum Cost ◽  
Pso Algorithm ◽  
Cloud Services ◽  
Positive Role ◽  
Current Time ◽  
Heuristic Technique ◽  
Software Services ◽  
Cost Factors

Cloud Computing is one of the important fields in the current time of technological era. Here, the resources are available virtually for users according to pay-per-usage. Many industries are providing cloud services nowadays as pay for usage which reduces the computing cost drastically. The updated software services, hardware services can be provided to the user at a minimum cost. The target of the industries and scientists is to reduce the computing cost by various technologies. Resource management or task scheduling may also play a positive role in this regard. There are various virtual machine management algorithms available that can be tested and enhanced for research and benefit of the society. In this paper, three famous Max-Min, Ant Colony Optimization, and Particle Swarm Optimization algorithms have been used for experiments. After simulation results, it is found that the PSO algorithm is performing well for makes pan and cost factors. Further, a new algorithm can be proposed or a meta-heuristic technique can be enhanced or modified for getting better results.

scholarly journals Photovoltaic Module Array Global Maximum Power Tracking Combined with Artificial Bee Colony and Particle Swarm Optimization Algorithm

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 603 ◽  
Author(s):  
Kuei-Hsiang Chao ◽  
Cheng-Chieh Hsieh
Keyword(s):  
Particle Swarm Optimization ◽  
Artificial Bee Colony ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Global Maximum ◽  
Photovoltaic Module ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Learning Factor

In this study, the output characteristics of partial modules in a photovoltaic module array when subject to shading were first explored. Then, an improved particle swarm optimization (PSO) algorithm was applied to track the global maximum power point (MPP), with a multi-peak characteristic curve. The improved particle swarm optimization algorithm proposed, combined with the artificial bee colony (ABC) algorithm, was used to adjust the weighting, cognition learning factor, and social learning factor, and change the number of iterations to enhance the tracking performance of the MPP tracker. Finally, MATLAB software was used to carry out a simulation and prove the improved that the PSO algorithm successfully tracked the MPP in the photovoltaic array output curve with multiple peaks. Its tracking performance is far superior to the existing PSO algorithm.

Sign in / Sign up

Export Citation Format

Share Document