An application and characteristic analysis of MOGA for bi-objective optimal component allocation problem in series-parallel redundant system

2009 ◽  
Vol 92 (9) ◽  
pp. 7-16 ◽  
Author(s):  
Hidemi Yamachi ◽  
Yasuhiro Tsujimura ◽  
Hisashi Yamamoto ◽  
Yasushi Kambayashi
Keyword(s):  
Allocation Problem ◽  
Redundant System ◽  
Characteristic Analysis ◽  
Component Allocation ◽  
In Series

scholarly journals Meta-Heuristics for Solving the Software Component Allocation Problem

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 153067-153076
Author(s):  
Issam Al-Azzoni ◽  
Saqib Iqbal
Keyword(s):  
Software Component ◽  
Allocation Problem ◽  
Component Allocation

scholarly journals Load characteristic analysis of a double-side internal coreless stator axial flux PMG

2019 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Ketut Wirtayasa ◽  
Pudji Irasari ◽  
Muhammad Kasim ◽  
Puji Widiyanto ◽  
Muhammad Fathul Hikmawan
Keyword(s):  
Permanent Magnet ◽  
Output Power ◽  
Power Efficiency ◽  
Voltage Regulation ◽  
Resistive Load ◽  
Characteristic Analysis ◽  
Axial Flux ◽  
Load Characteristic ◽  
In Series ◽  
The Difference

The main issue of using a permanent magnet in electric machines is the presence of cogging torque. Several methods have been introduced to eliminate it, one of which is by employing a coreless stator. In this paper, the load characteristic analysis of the double-side internal coreless stator axial flux permanent magnet generator with the specification of 1 kW, 220 V, 50 Hz, 300 rpm and 1 phase is discussed. The purpose is to learn the effect of the load to the generator performance, particularly the output power, efficiency and voltage regulation. The design and analysis are conducted analytically and numerically with two types of simulated loads, pure resistive and resistive-inductive in series. Each type of load provides power factor 1 and 0.85 respectively. The simulation results show that when loaded with resistive load, the generator gives a better performance at the output power (1,241 W) and efficiency (91 %), whereas a better voltage regulator (5.86 %) is achieved when it is loaded with impedance. Since the difference in the value of each parameter being compared is relatively small, it can be concluded that the generator represents good performance in both loads.

scholarly journals A Model-Driven Approach for Solving the Software Component Allocation Problem

Algorithms ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 354
Author(s):  
Issam Al-Azzoni ◽  
Julian Blank ◽  
Nenad Petrović
Keyword(s):  
Optimal Allocation ◽  
Smart Cities ◽  
Fog Computing ◽  
Linear Optimization ◽  
Software Component ◽  
Problem Definition ◽  
Allocation Problem ◽  
Multi Objective ◽  
Model Driven ◽  
Component Allocation

The underlying infrastructure paradigms behind the novel usage scenarios and services are becoming increasingly complex—from everyday life in smart cities to industrial environments. Both the number of devices involved and their heterogeneity make the allocation of software components quite challenging. Despite the enormous flexibility enabled by component-based software engineering, finding the optimal allocation of software artifacts to the pool of available devices and computation units could bring many benefits, such as improved quality of service (QoS), reduced energy consumption, reduction of costs, and many others. Therefore, in this paper, we introduce a model-based framework that aims to solve the software component allocation problem (CAP). We formulate it as an optimization problem with either single or multiple objective functions and cover both cases in the proposed framework. Additionally, our framework also provides visualization and comparison of the optimal solutions in the case of multi-objective component allocation. The main contributions introduced in this paper are: (1) a novel methodology for tackling CAP-alike problems based on the usage of model-driven engineering (MDE) for both problem definition and solution representation; (2) a set of Python tools that enable the workflow starting from the CAP model interpretation, after that the generation of optimal allocations and, finally, result visualization. The proposed framework is compared to other similar works using either linear optimization, genetic algorithm (GA), and ant colony optimization (ACO) algorithm within the experiments based on notable papers on this topic, covering various usage scenarios—from Cloud and Fog computing infrastructure management to embedded systems, robotics, and telecommunications. According to the achieved results, our framework performs much faster than GA and ACO-based solutions. Apart from various benefits of adopting a multi-objective approach in many cases, it also shows significant speedup compared to frameworks leveraging single-objective linear optimization, especially in the case of larger problem models.

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