scholarly journals A Modified Binary Firefly Algorithm to Solve Hardware/Software Partitioning Problem

Informatica ◽  
2021 ◽  
Vol 45 (7) ◽  
Author(s):  
Khetatba Mourad ◽  
Rachid Boudour
Keyword(s):  
Firefly Algorithm ◽  
Partitioning Problem ◽  
Software Partitioning

Community Structure Detection Using Firefly Algorithm

2018 ◽  
Vol 9 (4) ◽  
pp. 52-70 ◽  
Author(s):  
Ameera Saleh Jaradat ◽  
Safa'a Bani Hamad
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Graph Partitioning ◽  
Firefly Algorithm ◽  
Real Life ◽  
People Detection ◽  
Continuous Growth ◽  
Structure Detection ◽  
Partitioning Problem ◽  
And Function

This article describes how parallel to the continuous growth of the Internet, which allows people to share and collaborate more, social networks have become more attractive as a research topic in many different disciplines. Community structures are established upon interactions between people. Detection of these communities has become a popular topic in computer science. How to detect the communities is of great importance for understanding the organization and function of networks. Community detection is considered a variant of the graph partitioning problem which is NP-hard. In this article, the Firefly algorithm is used as an optimization algorithm to solve the community detection problem by maximizing the modularity measure. Firefly algorithm is a new Nature-inspired heuristic algorithm that proved its good performance in a variety of applications. Experimental results obtained from tests on real-life networks demonstrate that the authors' algorithm successfully detects the community structure.

A Multi-Objective Optimization Genetic Algorithm for SOPC Hardware-Software Partitioning

2012 ◽  
Vol 457-458 ◽  
pp. 1142-1148
Author(s):  
Fu Yang ◽  
Liu Xin ◽  
Pei Yuan Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Multi Objective Optimization ◽  
Key Technology ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Partitioning Problem ◽  
System On Programmable Chip ◽  
Software Partitioning

Hardware-software partitioning is the key technology in hardware-software co-design; the results will determine the design of system directly. Genetic algorithm is a classical search algorithm for solving such combinatorial optimization problem. A Multi-objective genetic algorithm for hardware-software partitioning is presented in this paper. This method can give consideration to both system performance and indicators such as time, power, area and cost, and achieve multi-objective optimization in system on programmable chip (SOPC). Simulation results show that the method can solve the SOPC hardware-software partitioning problem effectively.

On the hardware-software partitioning problem

2003 ◽  
Vol 8 (3) ◽  
pp. 269-297 ◽  
Author(s):  
Marisa López-Vallejo ◽  
Juan Carlos López
Keyword(s):  
Partitioning Problem ◽  
Software Partitioning

scholarly journals Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning

2019 ◽  
Vol 9 (5) ◽  
pp. 866 ◽  
Author(s):  
Tao Zhang ◽  
Changfu Yang ◽  
Xin Zhao
Keyword(s):  
Embedded System ◽  
Heuristic Algorithms ◽  
Optimization Methods ◽  
Clustering Method ◽  
Partitioning Problems ◽  
Partitioning Problem ◽  
New Si ◽  
Multiple Processing ◽  
Cluster Points ◽  
Software Partitioning

Today, more and more complex tasks are emerging. To finish these tasks within a reasonable time, using the complex embedded system which has multiple processing units is necessary. Hardware/software partitioning is one of the key technologies in designing complex embedded systems, it is usually taken as an optimization problem and be solved with different optimization methods. Among the optimization methods, swarm intelligent (SI) algorithms are easily applied and have the advantages of strong robustness and excellent global search ability. Due to the high complexity of hardware/software partitioning problems, the SI algorithms are ideal methods to solve the problems. In this paper, a new SI algorithm, called brainstorm optimization (BSO), is applied to hardware/software partitioning. In order to improve the performance of the BSO, we analyzed its optimization process when solving the hardware/software partitioning problem and found the disadvantages in terms of the clustering method and the updating strategy. Then we proposed the improved brainstorm optimization (IBSO) which ameliorated the original clustering method by setting the cluster points and improved the updating strategy by decreasing the number of updated individuals in each iteration. Based on the simulation methods which are usually used to evaluate the performance of the hardware/software partitioning algorithms, we generated eight benchmarks which represent tasks with different scales to test the performance of IBSO, BSO, four original heuristic algorithms and two improved BSO. Simulation results show that the IBSO algorithm can achieve the solutions with the highest quality within the shortest running time among these algorithms.

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