A Comparison of Genetic Algorithm, Particle Swarm Optimization and Simulated Annealing in Real-Time Task Scheduling on CMP

2013 ◽  
Vol 679 ◽  
pp. 77-81 ◽  
Author(s):  
Song Chai ◽  
Yu Bai Li ◽  
Chang Wu ◽  
Jian Wang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Real Time ◽  
Schedule Problem ◽  
Particle Swarm ◽  
Full Potential ◽  
Swarm Optimization ◽  
Task Schedule ◽  
Time Task

Real-time task schedule problem in Chip-Multiprocessor (CMP) receives wide attention in recent years. It is partly because the increasing demand for CMP solutions call for better schedule algorithm to exploit the full potential of hardware, and partly because of the complexity of schedule problem, which itself is an NP-hard problem. To address this task schedule problem, various of heuristics have been studied, among which, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) are the most popular ones. In this paper, we implement these 3 schedule heuristics, and compare their performance under the context of real-time tasks scheduling on CMP. According to the results of our intensive simulations, PSO has the best fitness optimization of these 3 algorithms, and SA is the most efficient algorithm.

scholarly journals A COMPARISON OF SIMULATED ANNEALING, GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION IN OPTIMAL FIRST-ORDER DESIGN OF INDOOR TLS NETWORKS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 75-82 ◽  
Author(s):  
F. Jia ◽  
D. Lichti
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Network Design Problem ◽  
Design System ◽  
Swarm Optimization ◽  
First Order

The optimal network design problem has been well addressed in geodesy and photogrammetry but has not received the same attention for terrestrial laser scanner (TLS) networks. The goal of this research is to develop a complete design system that can automatically provide an optimal plan for high-accuracy, large-volume scanning networks. The aim in this paper is to use three heuristic optimization methods, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO), to solve the first-order design (FOD) problem for a small-volume indoor network and make a comparison of their performances. The room is simplified as discretized wall segments and possible viewpoints. Each possible viewpoint is evaluated with a score table representing the wall segments visible from each viewpoint based on scanning geometry constraints. The goal is to find a minimum number of viewpoints that can obtain complete coverage of all wall segments with a minimal sum of incidence angles. The different methods have been implemented and compared in terms of the quality of the solutions, runtime and repeatability. The experiment environment was simulated from a room located on University of Calgary campus where multiple scans are required due to occlusions from interior walls. The results obtained in this research show that PSO and GA provide similar solutions while SA doesn’t guarantee an optimal solution within limited iterations. Overall, GA is considered as the best choice for this problem based on its capability of providing an optimal solution and fewer parameters to tune.

scholarly journals Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Weizhe Zhang ◽  
Hucheng Xie ◽  
Boran Cao ◽  
Albert M. K. Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Real Time ◽  
Task Scheduling ◽  
Particle Swarm ◽  
Energy Aware ◽  
Swarm Optimization ◽  
Heterogeneous Multiprocessors ◽  
Time Task ◽  
Feasible Solutions

Energy consumption in computer systems has become a more and more important issue. High energy consumption has already damaged the environment to some extent, especially in heterogeneous multiprocessors. In this paper, we first formulate and describe the energy-aware real-time task scheduling problem in heterogeneous multiprocessors. Then we propose a particle swarm optimization (PSO) based algorithm, which can successfully reduce the energy cost and the time for searching feasible solutions. Experimental results show that the PSO-based energy-aware metaheuristic uses 40%–50% less energy than the GA-based and SFLA-based algorithms and spends 10% less time than the SFLA-based algorithm in finding the solutions. Besides, it can also find 19% more feasible solutions than the SFLA-based algorithm.

scholarly journals Dynamic economic dispatch: a comparative study for differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Jagat Kishore Pattanaik ◽  
Mousumi Basu ◽  
Deba Prasad Dash
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Comparative Study ◽  
Differential Evolution ◽  
Economic Dispatch ◽  
Particle Swarm ◽  
Evolutionary Programming ◽  
Swarm Optimization ◽  
Dynamic Economic Dispatch

AbstractThis paper presents a comparative study for five artificial intelligent (AI) techniques to the dynamic economic dispatch problem: differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing. Here, the optimal hourly generation schedule is determined. Dynamic economic dispatch determines the optimal scheduling of online generator outputs with predicted load demands over a certain period of time taking into consideration the ramp rate limits of the generators. The AI techniques for dynamic economic dispatch are evaluated against a ten-unit system with nonsmooth fuel cost function as a common testbed and the results are compared against each other.

scholarly journals Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Z. H. Che ◽  
Tzu-An Chiang ◽  
Y. C. Kuo ◽  
Zhihua Cui
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Simulated Annealing ◽  
Decision Model ◽  
Particle Swarm ◽  
Hybrid Algorithms ◽  
Supply Chain Network ◽  
Reverse Supply Chain ◽  
Swarm Optimization

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.

scholarly journals Research on performance seeking control based on Beetle Antennae Search algorithm

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1440-1445
Author(s):  
Qiangang Zheng ◽  
Dewei Xiang ◽  
Juan Fang ◽  
Yong Wang ◽  
Haibo Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Real Time ◽  
Control Method ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Engine Performance ◽  
Inlet Temperature ◽  
Swarm Optimization ◽  
Time Performance

A novel performance seeking control) method based on Beetle Antennae Search algorithm is proposed to improve the real-time performance of performance seeking control. The Beetle Antennae Search imitates the function of antennae of beetle. The Beetle Antennae Search has better real-time performance because of the objective function only calculated twice in Beetle Antennae Search at each iteration. Moreover, the Beetle Antennae Search has global search ability. The performance seeking control simulations based on Beetle Antennae Search, Genetic Algorithm and particle swarm optimization are carried out. The simulations show that the Beetle Antennae Search has much better real-time performance than the conventional probability-based algorithms Genetic Algorithm and particle swarm optimization. The simulations also show that these three probability-based algorithms can get better engine performance, such as more thrust, less specific fuel consumption and less turbine inlet temperature.

Optimization Methods for Estimation of Missing Data

2010 ◽  
pp. 210-232
Author(s):  
Tshilidzi Marwala
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Missing Data ◽  
Missing Values ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Hill Climbing ◽  
Swarm Optimization ◽  
Design Variables

This chapter presents various optimization methods to optimize the missing data error equation, which is made out of the autoassociative neural networks with missing values as design variables. The four optimization techniques that are used are: genetic algorithm, particle swarm optimization, hill climbing and simulated annealing. These optimization methods are tested on two datasets, namely, the beer taster dataset and the fault identification dataset. The results that are obtained are then compared. For these datasets, the results indicate that genetic algorithm approach produced the highest accuracy when compared to simulated annealing and particle swarm optimization. However, the results of these four optimization methods are the same order of magnitude while hill climbing produces the lowest accuracy.

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