Incorporate Cost Matrix into Learning Vector Quantization Modeling: a Comparative Study of Genetic Algorithm, Simulated Annealing and Particle Swarm Optimization

2011 ◽  
pp. 122-129 ◽  
Author(s):  
Ning Chen ◽  
Bernardete Ribeiro ◽  
Armando Vieira ◽  
Joao Duarte ◽  
Joao C. Neves
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Comparative Study ◽  
Vector Quantization ◽  
Particle Swarm ◽  
Learning Vector Quantization ◽  
Swarm Optimization ◽  
Cost Matrix

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.

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 comparative study of particle swarm optimization and genetic algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 40
Author(s):  
Saman M. Almufti ◽  
Amar Yahya Zebari ◽  
Herman Khalid Omer
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Comparative Study ◽  
Evolutionary Computation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Computation Algorithms ◽  
Crossover And Mutation

This paper provides an introduction and a comparison of two widely used evolutionary computation algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) based on the previous studies and researches. It describes Genetic Algorithm basic functionalities including various steps such as selection, crossover, and mutation.  

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.

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