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.

Hardware/software partitioning method of embedded system based on π-nets

2013 ◽  
Vol 32 (3) ◽  
pp. 855-860
Author(s):  
Rong-zuo GUO ◽  
Jun HUANG ◽  
Lin WANG
Keyword(s):  
Embedded System ◽  
Software Partitioning

A Glowworm Swarm Optimization Algorithm for Uninhabited Combat Air Vehicle Path Planning

2015 ◽  
Vol 24 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Zhonghua Tang ◽  
Yongquan Zhou
Keyword(s):  
Path Planning ◽  
Heuristic Algorithms ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Population Based ◽  
Computational Accuracy ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Air Vehicle ◽  
The Individual

AbstractUninhabited combat air vehicle (UCAV) path planning is a complicated, high-dimension optimization problem. To solve this problem, we present in this article an improved glowworm swarm optimization (GSO) algorithm based on the particle swarm optimization (PSO) algorithm, which we call the PGSO algorithm. In PGSO, the mechanism of a glowworm individual was modified via the individual generation mechanism of PSO. Meanwhile, to improve the presented algorithm’s convergence rate and computational accuracy, we reference the idea of parallel hybrid mutation and local search near the global optimal location. To prove the performance of the proposed algorithm, PGSO was compared with 10 other population-based optimization methods. The experiment results show that the proposed approach is more effective in UCAV path planning than most of the other meta-heuristic algorithms.

scholarly journals Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS

2018 ◽  
Vol 7 (8) ◽  
pp. 292 ◽  
Author(s):  
Bahram Saeidian ◽  
Mohammad Mesgari ◽  
Biswajeet Pradhan ◽  
Mostafa Ghodousi
Keyword(s):  
Heuristic Algorithms ◽  
Initial Conditions ◽  
Optimization Algorithms ◽  
Gis Analysis ◽  
Clustering Method ◽  
Geospatial Information ◽  
Location Allocation ◽  
Urban Disaster ◽  
Location Allocation Problem ◽  
Selection Of

After an earthquake, it is required to establish temporary relief centers in order to help the victims. Selection of proper sites for these centers has a significant effect on the processes of urban disaster management. In this paper, the location and allocation of relief centers in district 1 of Tehran are carried out using Geospatial Information System (GIS), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision model, a simple clustering method and the two meta-heuristic algorithms of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). First, using TOPSIS, the proposed clustering method and GIS analysis tools, sites satisfying initial conditions with adequate distribution in the area are chosen. Then, the selection of proper centers and the allocation of parcels to them are modelled as a location/allocation problem, which is solved using the meta-heuristic optimization algorithms. Also, in this research, PSO and ACO are compared using different criteria. The implementation results show the general adequacy of TOPSIS, the clustering method, and the optimization algorithms. This is an appropriate approach to solve such complex site selection and allocation problems. In view of the assessment results, the PSO finds better answers, converges faster, and shows higher consistency than the ACO.

scholarly journals PSO Optimized Nearest Neighbor Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 1508-1513
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Heuristic Algorithms ◽  
Optimization Methods ◽  
Classification Model ◽  
Natural Phenomenon ◽  
Wide Applicability ◽  
Data Mining Algorithms ◽  
Wide Range ◽  
Mining Algorithms

Data mining can be considered to be an important aspects of information industry. Data mining has found a wide applicability in almost every field which deals with data. Out of the various techniques employed for data mining, Classification is a very commonly used tool for knowledge discovery. Various alternatives methods are available which can be used to create a classification model, out of which the most common and apprehensible one is KNN. In spite of KNN having a number of shortcomings and limitations in it, these can be overcome by with the help of alterations which can be made to the basic KNN algorithm. Due to its wide applicability, kNN has been the focus of extensive research and as a result, many alternatives have been performed with wide range of success in performance improvement. A major hardship being faced by the data mining applications is the large number of dimensions which render most of the data mining algorithms inefficient. The problem can be solved to some extent by using dimensionality reduction methods like PCA. Further improvements in the efficiency of the classification based mining algorithms can be achieved by using optimization methods. Meta-heuristic algorithms inspired by natural phenomenon like particle swarm optimization can be used very effectively for the purpose.

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