2022-05-21T19:56:08Z
http://aotp.fabad-ihe.ac.ir/?_action=export&rf=summon&issue=16396
Annals of Optimization Theory and Practice
AOTP
2588-3666
2588-3666
2020
3
2
Performance analysis of multi computer system consisting of active parallel homogeneous clients
Ibrahim
Yusuf
Abdullahi
Sanusi
Abdulkareem
Ismail
Muhammad
Isa
Suleiman
K
Shehu
Bala
U.A
Ali
Reliability is among the performance factors applied to multi computer systems consisting of active parallel hosts (clients) and a central server. For reliability evaluation and system performance, this study analyzed a multi computer system consisting of a three hosts (clients) connected to a central server. The system is configured as series-parallel system consisting of two subsystems A and B. Subsystem A consist of three clients working in parallel with each other while subsystem B consist of a central server. Both clients and server failure and repair time are to be exponentially distributed The system is analyzed using first order differential difference equations to derive the expressions for availability, mean time to failure, probability of busy period of repairman due partial or complete failure. The results are presented in tables and graphs. Reliability characteristics such as availability, MTTF, profit function as well as sensitivity analysis have been discussed. Some particular cases have also been derived and examined to see the practical effect of the model. The computed results are demonstrated by tables and graphs.
Multi computer
client
Server
reliability analysis
Industrial systems
Availability
2020
11
01
1
24
http://aotp.fabad-ihe.ac.ir/article_113056_fd23be55a3b4bd33c98e358af02e067c.pdf
Annals of Optimization Theory and Practice
AOTP
2588-3666
2588-3666
2020
3
2
Optimal redundancy allocation for the problem with chance constraints in fuzzy and intuitionistic fuzzy environments using soft computing technique
Nabaranjan
Bhattacharyee
Rajesh
Paramanik
Sanat
Mahato
In some reliability optimization problem the constraints relations have probabilistic nature. These constraints are called the chance constraints and are difficult to handle up to some extent. The aim of this paper is to solve the reliability-redundancy allocation problem involving chance constraints in precise and imprecise environments. The component reliabilities of the system are imprecise numbers and further the constraints are stochastic type i.e., chance constraints. The genetic algorithm incorporated with stochastic simulation approach is implemented to optimize the system reliability. We introduced the fuzzy and intuitionistic fuzzy numbers to consider the impreciseness. In particular, component reliabilities are assumed to be triangular fuzzy numbers and triangular intuitionistic fuzzy numbers in two different environments. The simulation technique known as Monte Carlo Simulation is used to find the deterministic constraints from the stochastic ones. To transform the constrained optimization problem into unconstrained one we make use of the effective Big-M penalty approach. The problems are coded with real coded genetic algorithm. We have taken up some numerical examples to show the performance of the proposed method and the sensitivities of the GA parameters are also presented graphically.
Reliability-redundancy Allocation Problem
Fuzzy number
intuitionistic fuzzy numbers
Real Coded Genetic Algorithm
chance constraint
Stochastic simulation technique
2020
11
01
25
47
http://aotp.fabad-ihe.ac.ir/article_118830_33482e08a05481a01f593f392c9aa80e.pdf
Annals of Optimization Theory and Practice
AOTP
2588-3666
2588-3666
2020
3
2
Decision making under intuitionistic fuzzy metric distances
Kousik
Bhattacharya
Sujit
De
This article deals with qualitative difference between two intuitionistic fuzzy sets with the help of standard pseudo metric and metric spaces. Some definitions over metric spaces, pseudo metric spaces, intuitionistic fuzzy sets, indeterminacy and the formula of measuring metrices have been incorporated. Numerical illustrations, graphical illustrations, area of applications and ranking for decision making are discussed to show the novelty of this article. Finally, conclusions and scope of future works are mentioned.
Metric distance
Pseudo metric distance
Intuitionistic fuzzy set
Ranking
2020
11
01
49
64
http://aotp.fabad-ihe.ac.ir/article_118831_40b7871b5d18aa18938177d37e8b9b53.pdf
Annals of Optimization Theory and Practice
AOTP
2588-3666
2588-3666
2020
3
2
On the shortest path calculation time in the large-scale dynamic post-disaster environment
Seyed
Shavarani
Bela
Vizvari
There are many metropolitan cities where serious disasters are expected. The disaster, especially if it is an earthquake, damages the roads structure. Thus, the shortest path between two points can be changed. Emergency vehicles, including ambulance, fire-engine, police car, and technical aid, must get the temporary shortest path in real time to work effectively. The shortest path algorithm available in MATLAB, Dijkstra, is tested on two metropolitan cities of San Francisco and Tehran. The road systems of both cities are represented by high number of nodes and connecting arcs. The conclusion is that the algorithm is suitable for finding shortest path for emergency vehicles. However, it is too slow for being a subroutine of a solver for vehicle routing problem.
shortest path
Dijkstra Algorithm
Bellman Equation
post-disaster period
emergency vehicle
2020
11
01
65
80
http://aotp.fabad-ihe.ac.ir/article_116560_385fccf2384f0f385ba62c2f9a2f780a.pdf
Annals of Optimization Theory and Practice
AOTP
2588-3666
2588-3666
2020
3
2
Automatic breast thermography images classification based on deep neural networks
Azza
mahmoud
Breast thermography is a screening tool which is capable of detecting cancer at an early stage. The main objective of this work is using the full power of deep neural network (DNN) and exploring its ability to learn the discriminative features of input data. The transfer learning and data augmentation are performed to solve the problem of lack of labled data. To improve the accuracy, the support vector machine (SVM) classifier will hybrid with the convolutional neural network (CNN) instead of using the deep model as end-to-end. The performance is verified by the k-fold cross-validation. The proposed techniques are trained and evaluated on DMR-IR dataset to classify the thermographic images to normal and abnormal groups. The proposed technique of employing AlexNet hybrid with SVM achieves the best performance, producing 92.55% accuracy, 95.56% sensitivity, 89.80% precision, 92.63% F1 score.
breast cancer
Breast thermography
Deep Neural Network
convolutional neural network
AlexNet
The support vector machine
2020
12
06
71
79
http://aotp.fabad-ihe.ac.ir/article_120067_7fe557967c1d153b0f73948db0b8ce39.pdf